diff --git a/.github/ISSUE_TEMPLATE/bug_report.md b/.github/ISSUE_TEMPLATE/bug_report.md index 24241e0295..255fa9e69a 100644 --- a/.github/ISSUE_TEMPLATE/bug_report.md +++ b/.github/ISSUE_TEMPLATE/bug_report.md @@ -8,12 +8,13 @@ assignees: '' --- - [ ] I have checked that this issue has not already been reported. -- [ ] I have confirmed this bug exists on the master branch of openproblems. +- [ ] I have confirmed this bug exists on the `main` branch of openproblems. --- -**Note**: Please read [this guide](https://matthewrocklin.com/blog/work/2018/02/28/minimal-bug-reports) detailing how to provide the necessary information for us to reproduce your bug. - +**Note**: Please read [this +guide](https://matthewrocklin.com/blog/work/2018/02/28/minimal-bug-reports) detailing +how to provide the necessary information for us to reproduce your bug. ### Minimal code sample (that we can copy&paste without having any data) @@ -29,6 +30,7 @@ assignees: ''
-[Paste the output of scanpy.logging.print_versions() leaving a blank line after the details tag] +
diff --git a/.github/ISSUE_TEMPLATE/propose-a-new-dataset.md b/.github/ISSUE_TEMPLATE/propose-a-new-dataset.md index 179ab0ef2c..7c55b46f15 100644 --- a/.github/ISSUE_TEMPLATE/propose-a-new-dataset.md +++ b/.github/ISSUE_TEMPLATE/propose-a-new-dataset.md @@ -4,7 +4,6 @@ about: Suggest new data for one or more tasks title: '' labels: enhancement, dataset assignees: '' - --- **What is the dataset?** @@ -14,4 +13,5 @@ Describe it briefly and include a citation. Include a link to the publicly available data. **Which tasks could it be used for?** -If the dataset is to be used for a task that is not yet included in the code base, use the issue template *propose a new task* instead. +If the dataset is to be used for a task that is not yet included in the code base, use +the issue template *propose a new task* instead. diff --git a/.github/ISSUE_TEMPLATE/propose-a-new-method.md b/.github/ISSUE_TEMPLATE/propose-a-new-method.md index 0bb86ac19f..61fa6d7e6a 100644 --- a/.github/ISSUE_TEMPLATE/propose-a-new-method.md +++ b/.github/ISSUE_TEMPLATE/propose-a-new-method.md @@ -14,4 +14,5 @@ Describe it briefly and include a citation. Include a link to the publicly available codebase. **Which task(s) could it be used for?** -If the method is to be used for a task that is not yet included in the code base, use the issue template *propose a new task* instead. +If the method is to be used for a task that is not yet included in the code base, use +the issue template *propose a new task* instead. diff --git a/.github/ISSUE_TEMPLATE/propose-a-new-metric.md b/.github/ISSUE_TEMPLATE/propose-a-new-metric.md index 3b5a175fd8..cf4910617d 100644 --- a/.github/ISSUE_TEMPLATE/propose-a-new-metric.md +++ b/.github/ISSUE_TEMPLATE/propose-a-new-metric.md @@ -14,4 +14,5 @@ Describe it briefly and include a citation if applicable. Include a link to the publicly available codebase if available. **Which task(s) could it be used for?** -If the method is to be used for a task that is not yet included in the code base, use the issue template *propose a new task* instead. +If the method is to be used for a task that is not yet included in the code base, use +the issue template *propose a new task* instead. diff --git a/.github/ISSUE_TEMPLATE/propose-a-new-task.md b/.github/ISSUE_TEMPLATE/propose-a-new-task.md index 53275dba9f..a421fa3a65 100644 --- a/.github/ISSUE_TEMPLATE/propose-a-new-task.md +++ b/.github/ISSUE_TEMPLATE/propose-a-new-task.md @@ -17,4 +17,5 @@ Include links to at least one publicly available dataset that could be used. Include links to codebases of at least two methods that perform the task. **Propose metrics** -Describe at least one metric by which to measure success on the task. It must be able to be applied to the proposed datasets. +Describe at least one metric by which to measure success on the task. It must be able to +be applied to the proposed datasets. diff --git a/.github/dependabot.yml b/.github/dependabot.yml index cbc7537953..c69bbe6c5a 100644 --- a/.github/dependabot.yml +++ b/.github/dependabot.yml @@ -6,6 +6,60 @@ updates: interval: "daily" open-pull-requests-limit: 1 + - package-ecosystem: "pip" + directory: "/docker/openproblems" + schedule: + interval: "daily" + open-pull-requests-limit: 1 + + - package-ecosystem: "pip" + directory: "/docker/openproblems-github-actions" + schedule: + interval: "daily" + open-pull-requests-limit: 1 + + - package-ecosystem: "pip" + directory: "/docker/openproblems-python-batch-integration" + schedule: + interval: "daily" + open-pull-requests-limit: 1 + + - package-ecosystem: "pip" + directory: "/docker/openproblems-python-extras" + schedule: + interval: "daily" + open-pull-requests-limit: 1 + + - package-ecosystem: "pip" + directory: "/docker/openproblems-python-scvi" + schedule: + interval: "daily" + open-pull-requests-limit: 1 + + - package-ecosystem: "pip" + directory: "/docker/openproblems-python-tf2.4" + schedule: + interval: "daily" + open-pull-requests-limit: 1 + + - package-ecosystem: "pip" + directory: "/docker/openproblems-r-base" + schedule: + interval: "daily" + open-pull-requests-limit: 1 + + - package-ecosystem: "pip" + directory: "/docker/openproblems-r-extras" + schedule: + interval: "daily" + open-pull-requests-limit: 1 + + - package-ecosystem: "pip" + directory: "/docker/openproblems-r-pytorch" + schedule: + interval: "daily" + open-pull-requests-limit: 1 + - package-ecosystem: "github-actions" directory: "/" schedule: diff --git a/.github/pull_request_template.md b/.github/pull_request_template.md index ca2bfe03b7..203bf09d76 100644 --- a/.github/pull_request_template.md +++ b/.github/pull_request_template.md @@ -13,13 +13,18 @@ ### Testing * [ ] This submission was written on a forked copy of SingleCellOpenProblems -* [ ] GitHub Actions "Run Benchmark" tests are passing on this base branch of this pull request (include link to passed test: ) -* [ ] If this pull request is not ready for review (including passing the "Run Benchmark" tests), I will open this PR as a draft (click on the down arrow next to the "Create Pull Request" button) +* [ ] GitHub Actions "Run Benchmark" tests are passing on this base branch of this pull + request (include link to passed test: ) +* [ ] If this pull request is not ready for review (including passing the "Run + Benchmark" tests), I will open this PR as a draft (click on the down arrow next to the + "Create Pull Request" button) ### Submission guidelines -* [ ] This submission follows the guidelines in our [Contributing](../blob/master/CONTRIBUTING.md) document -* [ ] I have checked to ensure there aren't other open [Pull Requests](../pulls) for the same update/change +* [ ] This submission follows the guidelines in our + [Contributing](../blob/master/CONTRIBUTING.md) document +* [ ] I have checked to ensure there aren't other open [Pull Requests](../pulls) for the + same update/change ### PR review checklist @@ -28,7 +33,9 @@ This PR will be evaluated on the basis of the following checks: * [ ] The task addresses a valid open problem in single-cell analysis * [ ] The latest version of master is merged and tested * [ ] The methods/metrics are imported to `__init__.py` and were tested in the pipeline -* [ ] Method and metric decorators are annotated with paper title, year, author, code version, and date +* [ ] Method and metric decorators are annotated with paper title, year, author, code + version, and date * [ ] The README gives an outline of the methods, metrics and datasets in the folder * [ ] The README provides a satisfactory task explanation (for new tasks) -* [ ] The sample test data is appropriate to test implementation of all methods and metrics (for new tasks) +* [ ] The sample test data is appropriate to test implementation of all methods and + metrics (for new tasks) diff --git a/.github/workflows/check_r_dependencies.yml b/.github/workflows/check_r_dependencies.yml index ba2a9493a0..91d87dd44a 100644 --- a/.github/workflows/check_r_dependencies.yml +++ b/.github/workflows/check_r_dependencies.yml @@ -26,7 +26,33 @@ jobs: for REQUIREMENTS in $(ls -1 docker/*/r_requirements.txt); do IMAGE=$(echo ${REQUIREMENTS} | sed 's:^docker/::' | sed 's:/.*::') docker pull -q singlecellopenproblems/${IMAGE} - docker run -t --rm --user=root singlecellopenproblems/${IMAGE} \ - Rscript /usr/src/singlecellopenproblems/scripts/upgrade_renv.R \ - /usr/src/singlecellopenproblems/${REQUIREMENTS} + PKG_CHANGED=$( + docker run -t --rm --user=root \ + -v $(pwd):/usr/src/singlecellopenproblems \ + singlecellopenproblems/${IMAGE} \ + Rscript /usr/src/singlecellopenproblems/scripts/upgrade_renv.R \ + /usr/src/singlecellopenproblems/${REQUIREMENTS} | \ + tee /dev/stderr | \ + tail -n 1 | \ + sed 's-::-/-g' + ) + git diff --exit-code || break + PKG_CHANGED="" done + echo "PKG_CHANGED=$(echo ${PKG_CHANGED} | sed 's/@/ to /')" >> $GITHUB_ENV + echo "UPDATE_BRANCH_NAME=dependencies/r/${PKG_CHANGED}" >> $GITHUB_ENV + + - name: Create Pull Request + if: >- + env.PKG_CHANGED != '' + uses: peter-evans/create-pull-request@v4 + with: + branch: ${{ env.UPDATE_BRANCH_NAME }} + delete-branch: true + base: main + labels: dependencies,r + title: "Update ${{ env.PKG_CHANGED }}" + committer: "openproblems-bio " + author: "openproblems-bio " + commit-message: "Update ${{ env.PKG_CHANGED }}" + draft: true diff --git a/.github/workflows/pre-commit.yml b/.github/workflows/pre-commit.yml index 36e77a5b0a..b38848967e 100644 --- a/.github/workflows/pre-commit.yml +++ b/.github/workflows/pre-commit.yml @@ -23,11 +23,11 @@ jobs: steps: - name: Cancel Previous Runs - uses: styfle/cancel-workflow-action@0.10.0 + uses: styfle/cancel-workflow-action@0.10.1 with: access_token: ${{ github.token }} - - uses: actions/checkout@v2 + - uses: actions/checkout@v3 with: fetch-depth: 0 @@ -47,7 +47,7 @@ jobs: if: startsWith(runner.os, 'Linux') with: path: /usr/local/lib/R/site-library - key: precommit-${{env.LINUX_VERSION}}-${{env.R_VERSION}}-renv-${{ hashFiles('.pre-commit.r_requirements.txt') }}- + key: precommit-${{env.LINUX_VERSION}}-${{env.R_VERSION}}-renv-${{ hashFiles('./docker/openproblems-github-actions/r_requirements.txt') }}- restore-keys: | precommit-${{env.LINUX_VERSION}}-${{env.R_VERSION}}-renv- @@ -55,7 +55,7 @@ jobs: run: | renv::restore() source("./scripts/install_renv.R") - install_renv(".pre-commit.r_requirements.txt") + install_renv("./docker/openproblems-github-actions/r_requirements.txt") shell: Rscript {0} - name: Cache pre-commit diff --git a/.github/workflows/process_results.yml b/.github/workflows/process_results.yml index 32217c2535..2a8bf1868a 100644 --- a/.github/workflows/process_results.yml +++ b/.github/workflows/process_results.yml @@ -66,8 +66,10 @@ jobs: S3_URI="s3://openproblems-nextflow/cwd_example" fi aws s3 cp --quiet --recursive "${S3_URI}" /tmp/results/ + rm -r website/data/results/*/ + rm -r website/content/benchmarks/*/ python openproblems/workflow/parse_nextflow.py /tmp website/data/results - python openproblems/workflow/generate_website_markdown.py website/content/tasks + python openproblems/workflow/generate_website_markdown.py website/content/benchmarks - name: AWS S3 cleanup if: "github.event_name == 'repository_dispatch'" @@ -81,21 +83,18 @@ jobs: aws s3 cp --recursive "s3://openproblems-nextflow/cwd_main" "s3://openproblems-nextflow/cwd_example" aws s3 rm --recursive "s3://openproblems-nextflow/cwd_main" - - name: Commit result - uses: EndBug/add-and-commit@v9 + - name: Push to openproblems-bio/website if: "github.event_name == 'repository_dispatch'" - with: - author_name: openproblems-bio - author_email: singlecellopenproblems@protonmail.com - message: 'Update benchmark results # ci skip' - add: "['data/results', 'content/tasks']" - new_branch: ${{ env.UPDATE_BRANCH_NAME }} - push: true - cwd: './website' + shell: bash + working-directory: './website' + env: + GITHUB_TOKEN: ${{ secrets.GH_ACTIONS_WEBSITE_PAT }} + run: | + git push origin "${UPDATE_BRANCH_NAME}" - name: Create Pull Request if: "github.event_name == 'repository_dispatch'" - uses: peter-evans/create-pull-request@v3 + uses: peter-evans/create-pull-request@v4 with: branch: ${{ env.UPDATE_BRANCH_NAME }} delete-branch: true @@ -104,3 +103,19 @@ jobs: reviewers: scottgigante, dburkhardt path: './website' token: ${{ secrets.GH_ACTIONS_WEBSITE_PAT }} + author: "openproblems-bio " + commit-message: "Update benchmark results # ci skip" + + - name: Upload results on test + if: "github.event_name != 'repository_dispatch'" + uses: actions/upload-artifact@main + with: + name: results + path: website/data/results + + - name: Upload markdown on test + if: "github.event_name != 'repository_dispatch'" + uses: actions/upload-artifact@main + with: + name: markdown + path: website/content/benchmarks diff --git a/.github/workflows/run_tests.yml b/.github/workflows/run_tests.yml index b654a6e273..217af3fb4e 100644 --- a/.github/workflows/run_tests.yml +++ b/.github/workflows/run_tests.yml @@ -21,7 +21,7 @@ jobs: runs-on: ubuntu-latest steps: - name: Cancel Previous Runs - uses: styfle/cancel-workflow-action@0.10.0 + uses: styfle/cancel-workflow-action@0.10.1 with: access_token: ${{ github.token }} @@ -43,7 +43,7 @@ jobs: BRANCH_NAME: "auto_update_docker_${{ github.run_number }}" steps: - - uses: actions/checkout@v2 + - uses: actions/checkout@v3 with: fetch-depth: 1000 @@ -79,32 +79,18 @@ jobs: cd .. echo "CHANGED=`git diff --exit-code > /dev/null && echo false || echo true`" >> $GITHUB_ENV - - name: Set up Git branch - if: env.CHANGED == 'true' && startsWith(github.ref, 'refs/heads/main') - run: | - git checkout -b $BRANCH_NAME - git push -u origin $BRANCH_NAME - - - name: Commit result - if: env.CHANGED == 'true' && startsWith(github.ref, 'refs/heads/main') - uses: EndBug/add-and-commit@v6 - with: - author_name: SingleCellOpenProblems - author_email: singlecellopenproblems@protonmail.com - message: 'Update docker version # ci skip' - add: "docker/.version" - branch: ${{ env.BRANCH_NAME }} - push: false - - name: Create Pull Request if: env.CHANGED == 'true' && startsWith(github.ref, 'refs/heads/main') - uses: peter-evans/create-pull-request@v3 + uses: peter-evans/create-pull-request@v4 with: branch: ${{ env.BRANCH_NAME }} delete-branch: true base: main title: '[auto] Update docker version' reviewers: scottgigante, dburkhardt + author: "openproblems-bio " + commit-message: Update docker version # ci skip + add-paths: docker/.version - name: Upload check results on fail if: failure() @@ -152,7 +138,7 @@ jobs: run: | sudo rm -rf /opt/remove/*/* - - uses: actions/checkout@v2 + - uses: actions/checkout@v3 with: fetch-depth: 0 @@ -191,6 +177,7 @@ jobs: renv::restore() source("./scripts/install_renv.R") install_renv("docker/openproblems-r-base/r_requirements.txt") + install_renv("docker/openproblems-github-actions/r_requirements.txt") shell: Rscript {0} - name: Pull Docker images @@ -242,7 +229,7 @@ jobs: exit 1 fi - - uses: actions/checkout@v2 + - uses: actions/checkout@v3 with: fetch-depth: 1000 @@ -254,6 +241,32 @@ jobs: sudo apt-get update -qq || (sudo rm /etc/apt/sources.list.d/* && sudo apt-get update -yqq) sudo apt-get install -qy --no-install-recommends libhdf5-dev pandoc gfortran libblas-dev liblapack-dev libedit-dev llvm-dev + - name: Check Tower authentication + env: + TOWER_ACCESS_TOKEN: ${{ secrets.TOWER_ACCESS_KEY }} + TOWER_ORG_ID: 9180144064527 + TOWER_WORKSPACE_ID: 53907369739130 + run: | + token_confirmation=$( + curl -X GET "https://api.tower.nf/user-info" \ + -H "Accept: application/json" \ + -H "Authorization: Bearer ${TOWER_ACCESS_TOKEN}" + ) + if [ -z "${token_confirmation}" ]; then + echo "Tower authentication failed; set TOWER_ACCESS_KEY in repo secrets." + echo "See CONTRIBUTING.md for details." + exit 1 + fi + workspace_confirmation=$( + curl -X GET "https://api.tower.nf/orgs/${TOWER_ORG_ID}/workspaces/${TOWER_WORKSPACE_ID}" \ + -H "Accept: application/json" \ + -H "Authorization: Bearer ${TOWER_ACCESS_TOKEN}" + ) + if [ -z "${workspace_confirmation}" ]; then + echo "Tower authentication failed; email singlecellopenproblems@protonmail.com with a request to be added to the openproblems-bio workspace" + exit 1 + fi + - name: Set up Python uses: actions/setup-python@v4 with: @@ -276,7 +289,7 @@ jobs: # If not on the base repository, append first 6 characters of username to the image name # to avoid clashes on ECR REPO_PARSED=$(echo ${{ github.repository }} | awk '{print $1}' FS=/ | head -c 6) - BRANCH_PARSED=$(echo ${{ github.ref }} | sed 's:refs/[a-z]*/::' | sed 's:[/_]:-:g') + BRANCH_PARSED=$(echo ${{ github.ref }} | sed 's:refs/[a-z]*/::' | sed 's:[^a-zA-Z0-9]:-:g') if [[ "${{ github.repository }}" == "openproblems-bio/openproblems" ]]; then BRANCH=`echo $BRANCH_PARSED | head -c 40` else @@ -385,7 +398,7 @@ jobs: RUN_NAME="$(echo "$BRANCH" | sed "s/[^a-zA-Z0-9]/_/g")_$(git rev-parse --short HEAD)_${GITHUB_RUN_ATTEMPT}" cd /mnt/openproblems-nextflow/cwd/${BRANCH} nextflow run \ - -revision v1.6 \ + -revision v1.8 \ -with-tower \ -ansi-log false -resume \ -profile aws,test \ @@ -403,12 +416,17 @@ jobs: python workflow/parse_nextflow.py /mnt/openproblems-nextflow/cwd/${BRANCH} /tmp/website python workflow/generate_website_markdown.py /tmp/website + - name: Rename nextflow log + if: always() + run: | + mv /mnt/openproblems-nextflow/cwd/${{ env.BRANCH }}/.nextflow.log /tmp/nextflow.log + - name: Upload nextflow log if: always() uses: actions/upload-artifact@main with: name: nextflow.log - path: /mnt/openproblems-nextflow/cwd/${{ env.BRANCH }}/.nextflow.log + path: /tmp/nextflow.log run_full_benchmark: needs: run_test_benchmark @@ -429,7 +447,7 @@ jobs: env: TOWER_WATCH_URL: https://tower.nf/orgs/openproblems-bio/workspaces/openproblems-bio/watch TOWER_WORKSPACE_ID: 53907369739130 - TOWER_ACTION_ID: 47GPYNQpUGw18YzLVeI6ke + TOWER_ACTION_ID: 7jylKuFGbSN65qSA4NfdFY run: | generate_parameters() { diff --git a/.gitignore b/.gitignore index bb7bba67d2..f9bee9a858 100644 --- a/.gitignore +++ b/.gitignore @@ -150,3 +150,10 @@ nf-openproblems scratch/ openproblems/results/ openproblems/work/ +batch_embed.txt +immune.h5ad + +immune.h5ad +batch_embed.txt +.vscode/launch.json +run_bbknn.py diff --git a/.lintr b/.lintr deleted file mode 100644 index eeea59c31a..0000000000 --- a/.lintr +++ /dev/null @@ -1 +0,0 @@ -linters: with_defaults(open_curly_linter = NULL) diff --git a/.markdownlint.yaml b/.markdownlint.yaml new file mode 100644 index 0000000000..91d6297e4a --- /dev/null +++ b/.markdownlint.yaml @@ -0,0 +1,3 @@ +MD013: + line_length: 88 + heading_line_length: 120 diff --git a/.pre-commit-config.yaml b/.pre-commit-config.yaml index 5aad87b889..eca5a31bdb 100644 --- a/.pre-commit-config.yaml +++ b/.pre-commit-config.yaml @@ -34,3 +34,8 @@ repos: - id: style-files - id: lintr verbose: true + - repo: https://github.com/igorshubovych/markdownlint-cli + rev: v0.32.0 + hooks: + - id: markdownlint-fix + exclude: (SAGEMAKER.md|.github/ISSUE_TEMPLATE/bug_report.md|.github/pull_request_template.md) diff --git a/.pre-commit.r_requirements.txt b/.pre-commit.r_requirements.txt deleted file mode 100644 index c7e83bab3d..0000000000 --- a/.pre-commit.r_requirements.txt +++ /dev/null @@ -1,5 +0,0 @@ -docopt@0.7.1 -git2r@0.30.1 -lintr@2.0.1 -styler@1.7.0 -tibble@3.1.7 diff --git a/CODE_OF_CONDUCT.md b/CODE_OF_CONDUCT.md index 556ae0f482..7d26fcefac 100644 --- a/CODE_OF_CONDUCT.md +++ b/CODE_OF_CONDUCT.md @@ -68,9 +68,9 @@ members of the project's leadership. ## Attribution This Code of Conduct is adapted from the [Contributor Covenant][homepage], version 1.4, -available at https://www.contributor-covenant.org/version/1/4/code-of-conduct.html +available at [homepage]: https://www.contributor-covenant.org For answers to common questions about this code of conduct, see -https://www.contributor-covenant.org/faq + diff --git a/CONTRIBUTING.md b/CONTRIBUTING.md index a083e2a50d..4442a45674 100644 --- a/CONTRIBUTING.md +++ b/CONTRIBUTING.md @@ -1,63 +1,80 @@ # Contributing to Open Problems for Single Cell Analysis -There are many ways to contribute to `openproblems`, with the most common ones -being contribution of datasets, methods, metrics, or new tasks. Improving the -documentation is no less important than improving the library itself. If you -find a typo in the documentation, or have made improvements, do not hesitate to -submit a GitHub pull request. +There are many ways to contribute to `openproblems`, with the most common ones being +contribution of datasets, methods, metrics, or new tasks. Improving the documentation is +no less important than improving the library itself. If you find a typo in the +documentation, or have made improvements, do not hesitate to submit a GitHub pull +request. -But there are many other ways to help. In particular answering queries on the -[issue tracker](https://github.com/openproblems-bio/openproblems/issues), -investigating bugs, and [reviewing other developers' pull requests](https://github.com/openproblems-bio/openproblems/pulls) -are very valuable contributions that decrease the burden on the project -maintainers. +But there are many other ways to help. In particular answering queries on the [issue +tracker](https://github.com/openproblems-bio/openproblems/issues), investigating bugs, +and [reviewing other developers' pull +requests](https://github.com/openproblems-bio/openproblems/pulls) are very valuable +contributions that decrease the burden on the project maintainers. -Another way to contribute is to report issues you're facing, and give a "thumbs -up" on issues that others reported and that are relevant to you. It also helps -us if you spread the word: reference the project from your blog and articles, -link to it from your website, or simply star it in GitHub to say "I use it". +Another way to contribute is to report issues you're facing, and give a "thumbs up" on +issues that others reported and that are relevant to you. It also helps us if you spread +the word: reference the project from your blog and articles, link to it from your +website, or simply star it in GitHub to say "I use it". - +[](https://github.com/openproblems-bio/openproblems/stargazers) -### Table of Contents +### Table of Contents * [Submitting New Features](#submitting-new-features) * [API](#api) - + [Writing functions in R](#writing-functions-in-r) - + [Adding package dependencies](#adding-package-dependencies) - + [Adding a new dataset](#adding-a-new-dataset) - + [Adding a dataset / method / metric to a task](#adding-a-dataset--method--metric-to-a-task) - + [Adding a new task](#adding-a-new-task) - + [Adding a new Docker container](#adding-a-new-docker-container) + * [Writing functions in R](#writing-functions-in-r) + * [Adding package dependencies](#adding-package-dependencies) + * [Adding a new dataset](#adding-a-new-dataset) + * [Adding a dataset / method / metric to a + task](#adding-a-dataset--method--metric-to-a-task) + * [Adding a new task](#adding-a-new-task) + * [Adding a new Docker container](#adding-a-new-docker-container) * [Code Style and Testing](#code-style-and-testing) * [Code of Conduct](#code-of-conduct) * [Attribution](#attribution) - - + ## Submitting New Features -To submit new features to Open Problems for Single Cell Analysis, follow the steps below: - -1. Search through the [GitHub Issues](https://github.com/openproblems-bio/openproblems/issues) tracker to make sure there isn't someone already working on the feature you'd like to add. If someone is working on this, post in that issue that you'd like to help or reach out to one of the contributors working on the issue directly. -2. If there isn't an existing issue tracking this feature, create one! There are several templates you can choose one depending on what type of feature you'd like to add. -3. Fork https://github.com/openproblems-bio/openproblems into your account. If you're new to `git`, you might find the [Fork a repo](https://docs.github.com/en/github/getting-started-with-github/fork-a-repo) documentation helpful. - +To submit new features to Open Problems for Single Cell Analysis, follow the steps +below: + +1. Search through the [GitHub + Issues](https://github.com/openproblems-bio/openproblems/issues) tracker to make sure + there isn't someone already working on the feature you'd like to add. If someone is + working on this, post in that issue that you'd like to help or reach out to one of + the contributors working on the issue directly. +2. If there isn't an existing issue tracking this feature, create one! There are several + templates you can choose one depending on what type of feature you'd like to add. +3. Fork into your account. If you're + new to `git`, you might find the [Fork a + repo](https://docs.github.com/en/github/getting-started-with-github/fork-a-repo) + documentation helpful. + ![](" width=400px> 4. Set up [tower.nf](https://tower.nf) and make sure you have access to - [`openproblems-bio`](https://tower.nf/orgs/openproblems-bio/workspaces/openproblems-bio/watch). If you do not have - access, please contact us at [singlecellopenproblems@protonmail.com](mailto:singlecellopenproblems@protonmail.com). -5. Create repository secrets (*not environment secrets*) at [https://github.com/USERNAME/openproblems/settings/secrets](https://github.com/USERNAME/openproblems/settings/secrets) - * *AWS_ACCESS_KEY_ID and AWS_SECRET_ACCESS_KEY are included in your AWS login details. If you do not have these, please contact us at [singlecellopenproblems@protonmail.com](mailto:singlecellopenproblems@protonmail.com).* - * *TOWER_ACCESS_KEY: log in with GitHub to https://tower.nf and create a token at https://tower.nf/tokens.* + [`openproblems-bio`](https://tower.nf/orgs/openproblems-bio/workspaces/openproblems-bio/watch). + If you do not have access, please contact us at + [singlecellopenproblems@protonmail.com](mailto:singlecellopenproblems@protonmail.com). +5. Create repository secrets (*not environment secrets*) + [https://github.com/USERNAME/openproblems/settings/secrets](https://github.com/USERNAME/openproblems/settings/secrets) + * *AWS_ACCESS_KEY_ID and AWS_SECRET_ACCESS_KEY are included in your AWS login + details. If you do not have these, please contact us at + [singlecellopenproblems@protonmail.com](mailto:singlecellopenproblems@protonmail.com).* + * *TOWER_ACCESS_KEY: log in with GitHub to and create a token at + .* * When you are done with this step, your page should look like this: ![AWS secrets success](static/img/AWS_secret.png) - -6. Enable workflows at [https://github.com/USERNAME/openproblems/actions](https://github.com/USERNAME/openproblems/actions). -7. Set up your git repository to fetch branches from `base` at `openproblems-bio/openproblems` - ``` +6. Enable workflows at + [https://github.com/USERNAME/openproblems/actions](https://github.com/USERNAME/openproblems/actions). +7. Set up your git repository to fetch branches from `base` at + `openproblems-bio/openproblems` + + ```shell git clone git@github.com:/openproblems.git cd openproblems git remote add base git@github.com:openproblems-bio/openproblems.git @@ -65,27 +82,46 @@ To submit new features to Open Problems for Single Cell Analysis, follow the ste git branch --set-upstream-to base/main git pull ``` - **Note:** If you haven't set up SSH keys with your GitHub account, you may see this error message when adding `base` to your repository: - ``` + + **Note:** If you haven't set up SSH keys with your GitHub account, you may see this + error message when adding `base` to your repository: + + ```text git@github.com: Permission denied (publickey). fatal: Could not read from remote repository. Please make sure you have the correct access rights ``` - To generate an SSH key and add it to your GitHub account, follow [this tutorial from GitHub](https://docs.github.com/en/github/authenticating-to-github/adding-a-new-ssh-key-to-your-github-account). - -8. Create a new branch for your task (**no underscores or spaces allowed**). It is best to coordinate with other people working on the same feature as you so that there aren't clases in images uploaded to our ECR. Here we're creating a branch called `method-method-name-task-name`, but if you were creating a new metric you might use `metric-metric-name-task-name`. In practice you should actually use the name of your method or metric, like `method-meld-differential-abundance` or `metric-mse-label-projection`. - **Note:** This pushes the branch to your fork, _not to_ `base`. You will create a PR to merge your branch to `base` only after all tests are passing. + To generate an SSH key and add it to your GitHub account, follow [this tutorial from + GitHub](https://docs.github.com/en/github/authenticating-to-github/adding-a-new-ssh-key-to-your-github-account). - **Warning:** Do not edit the `main` branch on your fork! This will not work as expected, and will never pass tests. - ``` +8. Create a new branch for your task (**no underscores or spaces allowed**). It is best + to coordinate with other people working on the same feature as you so that there + aren't clases in images uploaded to our ECR. Here we're creating a branch called + `method-method-name-task-name`, but if you were creating a new metric you might use + `metric-metric-name-task-name`. In practice you should actually use the name of your + method or metric, like `method-meld-differential-abundance` or + `metric-mse-label-projection`. + + **Note:** This pushes the branch to your fork, *not to* `base`. You will create a PR + to merge your branch to `base` only after all tests are passing. + + **Warning:** Do not edit the `main` branch on your fork! This will not work as + expected, and will never pass tests. + + ```shell # IMPORTANT: choose a new branch name, e.g. git checkout -b method-method-name-task-name # or metric-new-metric-name, etc git push -u origin method-method-name-task-name ``` -9. Sometimes, changes might be made to the openproblems `base` repository that you want to incorporate into your fork. To sync your fork from `base`, use the following code adapted from the [Syncing a Fork](https://docs.github.com/en/github/collaborating-with-issues-and-pull-requests/syncing-a-fork) tutorial from GitHub. - ``` +9. Sometimes, changes might be made to the openproblems `base` repository that you want + to incorporate into your fork. To sync your fork from `base`, use the following code + adapted from the [Syncing a + Fork](https://docs.github.com/en/github/collaborating-with-issues-and-pull-requests/syncing-a-fork) + tutorial from GitHub. + + ```shell # Fetch the branches and their respective commits from the upstream repository git fetch base @@ -98,31 +134,46 @@ To submit new features to Open Problems for Single Cell Analysis, follow the ste # Push the changes to your fork git push -u origin ``` - You can now create a [Pull Request](https://guides.github.com/activities/hello-world/#pr) from the default branch on your fork, `main`, into your working branch, e.g. `method-method-name-task-name`. -10. Wait for all tests to pass on your new branch before pushing changes (as this will allow GitHub Actions to cache the workflow setup, which speeds up testing.) + You can now create a [Pull + Request](https://guides.github.com/activities/hello-world/#pr) from the default + branch on your fork, `main`, into your working branch, e.g. + `method-method-name-task-name`. + +10. Wait for all tests to pass on your new branch before pushing changes (as this will + allow GitHub Actions to cache the workflow setup, which speeds up testing.) ## API Each task consists of datasets, methods, and metrics. -Datasets should take no arguments and return an AnnData object. If `test is True`, then the method should load the full dataset, but only return a small version of the same data (preferably <200 cells and <500 genes) for faster downstream analysis. +Datasets should take no arguments and return an AnnData object. If `test is True`, then +the method should load the full dataset, but only return a small version of the same +data (preferably <200 cells and <500 genes) for faster downstream analysis. -``` +```text function dataset(bool test=False) -> AnnData adata ``` -Methods should take an AnnData object and store a) the output in `adata.obs` / `adata.obsm` / etc., according to the specification of the task and b) the version of the package used to run the method in `adata.uns["method_code_version"]`. If `test is True`, you may modify hyperparameters (e.g. number of iterations) to make the method run faster. +Methods should take an AnnData object and store a) the output in `adata.obs` / +`adata.obsm` / etc., according to the specification of the task and b) the version of +the package used to run the method in `adata.uns["method_code_version"]`. If `test is +True`, you may modify hyperparameters (e.g. number of iterations) to make the method run +faster. -``` +```text function method(AnnData adata, bool test=False) -> AnnData adata ``` -If your method takes hyperparameters, set them as keyword arguments in the method definition. If the hyperparameters change depending on the value of `test`, set the keyword argument to `None` and set them to your chosen defaults only if the passed value is `None`. For an example, see [harmonic alignment](openproblems/tasks/multimodal_data_integration/methods/harmonic_alignment.py). +If your method takes hyperparameters, set them as keyword arguments in the method +definition. If the hyperparameters change depending on the value of `test`, set the +keyword argument to `None` and set them to your chosen defaults only if the passed value +is `None`. For an example, see [harmonic +alignment](openproblems/tasks/multimodal_data_integration/methods/harmonic_alignment.py). Metrics should take an AnnData object and return a `float`. -``` +```text function metric(AnnData adata) -> float ``` @@ -133,7 +184,12 @@ Task-specific APIs are described in the README for each task. ### Writing functions in R -Metrics and methods can also be written in R. AnnData Python objects are converted to and from `SingleCellExperiment` R objects using [`anndata2ri`](https://icb-anndata2ri.readthedocs-hosted.com/en/latest/). R methods should be written in a `.R` file which assumes the existence of a `SingleCellExperiment` object called `sce`, and should return the same object. A simple method implemented in R could be written as follows: +Metrics and methods can also be written in R. AnnData Python objects are converted to +and from `SingleCellExperiment` R objects using +[`anndata2ri`](https://icb-anndata2ri.readthedocs-hosted.com/en/latest/). R methods +should be written in a `.R` file which assumes the existence of a `SingleCellExperiment` +object called `sce`, and should return the same object. A simple method implemented in R +could be written as follows: ```{R} ### tasks//methods/pca.R @@ -172,14 +228,25 @@ def pca(adata): return _pca(adata) ``` -See the [`anndata2ri` docs](https://icb-anndata2ri.readthedocs-hosted.com/en/latest/) for API details. For a more detailed example of how to use this functionality, see our implementation of fastMNN batch correction ([mnn.R](openproblems/tasks/multimodal_data_integration/methods/mnn.R), [mnn.py](openproblems/tasks/multimodal_data_integration/methods/mnn.py)). +See the [`anndata2ri` docs](https://icb-anndata2ri.readthedocs-hosted.com/en/latest/) +for API details. For a more detailed example of how to use this functionality, see our +implementation of fastMNN batch correction +([mnn.R](openproblems/tasks/multimodal_data_integration/methods/mnn.R), +[mnn.py](openproblems/tasks/multimodal_data_integration/methods/mnn.py)). ### Adding package dependencies -If you are unable to write your method using our base dependencies, you may add to our existing Docker images, or create your own. The image you wish to use (if you are not using the base image) should be specified in the `image` keyword argument of the method/metric decorator. See the [Docker images README](docker/README.md) for details. +If you are unable to write your method using our base dependencies, you may add to our +existing Docker images, or create your own. The image you wish to use (if you are not +using the base image) should be specified in the `image` keyword argument of the +method/metric decorator. See the [Docker images README](docker/README.md) for details. -For the target method or metric, image-specific packages can then be imported in the associated python file (i.e. `f2.py`). Importantly, the import statement must be located in the function that calls the method or metric. If this is the `f2` function, the first few lines of the function may be structured as follows: -``` +For the target method or metric, image-specific packages can then be imported in the +associated python file (i.e. `f2.py`). Importantly, the import statement must be located +in the function that calls the method or metric. If this is the `f2` function, the first +few lines of the function may be structured as follows: + +```python def f2(adata): import package1 import package2 @@ -187,29 +254,58 @@ def f2(adata): ### Adding a new dataset -Datasets are loaded under `openproblems/data`. Each data loading function should download the appropriate dataset from a stable location (e.g. from Figshare) be decorated with `openproblems.data.utils.loader(data_url="https://data.link")` in order to cache the result. +Datasets are loaded under `openproblems/data`. Each data loading function should +download the appropriate dataset from a stable location (e.g. from Figshare) be +decorated with `openproblems.data.utils.loader(data_url="https://data.link", +data_reference="https://doi.org/10.0/123")` in order to cache the result. -Data should be provided in a raw count format. We assume that `adata.X` contains the raw (count) data for the primary modality; this will also be copied to `adata.layers["counts"]` for permanent access to the raw data. Additional modalities should be stored in `adata.obsm`. Prenormalized data (if available) can be stored in `adata.layers`, preferably using a name corresponding to the equivalent [normalization function](./openproblems/tools/normalize.py) (e.g., `adata.layers["log_scran_pooling"]`). +Data should be provided in a raw count format. We assume that `adata.X` contains the raw +(count) data for the primary modality; this will also be copied to +`adata.layers["counts"]` for permanent access to the raw data. Additional modalities +should be stored in `adata.obsm`. Prenormalized data (if available) can be stored in +`adata.layers`, preferably using a name corresponding to the equivalent [normalization +function](./openproblems/tools/normalize.py) (e.g., +`adata.layers["log_scran_pooling"]`). -To see a gold standard loader, look at [openproblems/data/Wagner_2018_zebrafish_embryo_CRISPR.py](./openproblems/data/Wagner_2018_zebrafish_embryo_CRISPR.py) +To see a gold standard loader, look at +[openproblems/data/Wagner_2018_zebrafish_embryo_CRISPR.py](./openproblems/data/Wagner_2018_zebrafish_embryo_CRISPR.py) -This file name should match `[First Author Last Name]_[Year Published]_short_Description_of_data.py`. E.g. the dataset of zebrafish embryos perturbed with CRISPR published in 2018 by Wagner _et al._ becomes `Wagner_2018_zebrafish_embryo_CRISPR.py` +This file name should match +`[First Author Last Name]_[Year Published]_short_Description_of_data.py`. E.g. the +dataset of zebrafish embryos perturbed with CRISPR published in 2018 by Wagner *et al.* +becomes `Wagner_2018_zebrafish_embryo_CRISPR.py` ### Adding a dataset / method / metric to a task -To add a dataset, method, or metric to a task, simply create a new `.py` file corresponding to your proposed new functionality and import the main function in the corresponding `__init__.py`. E.g., to add a "F2" metric to the label projection task, we would create `openproblems/tasks/label_projection/metrics/f2.py` and add a line -``` +To add a dataset, method, or metric to a task, simply create a new `.py` file +corresponding to your proposed new functionality and import the main function in the +corresponding `__init__.py`. E.g., to add a "F2" metric to the label projection task, we +would create `openproblems/tasks/label_projection/metrics/f2.py` and add a line + +```python from .f2 import f2 ``` -to [`openproblems/tasks/label_projection/metrics/__init__.py`](openproblems/tasks/label_projection/metrics/__init__.py). -For datasets in particular, these should be loaded using a `loader` function from `openproblems.data`, with only task-specific annotations added in the task-specific data file. +to +[`openproblems/tasks/label_projection/metrics/__init__.py`](openproblems/tasks/label_projection/metrics/__init__.py). -Datasets, methods, and metrics should all be decorated with the appropriate function in `openproblems.tools.decorators` to include metadata required for the evaluation and presentation of results. +For datasets in particular, these should be loaded using a `loader` function from +`openproblems.data`, with only task-specific annotations added in the task-specific data +file. -Note that data is not normalized in the data loader; normalization should be performed as part of each method or in the task dataset function if stated in the task API. For ease of use, we provide a collection of common normalization functions in [`openproblems.tools.normalize`](openproblems/tools/normalize.py). The original data stored in `adata.X` is automatically stored in `adata.layers["counts"]` for later reference in the case the a metric needs to access the unnormalized data. +Datasets, methods, and metrics should all be decorated with the appropriate function in +`openproblems.tools.decorators` to include metadata required for the evaluation and +presentation of results. -To test the performance of a dataset, method, or metric, you can use the command-line interface: +Note that data is not normalized in the data loader; normalization should be performed +as part of each method or in the task dataset function if stated in the task API. For +ease of use, we provide a collection of common normalization functions in +[`openproblems.tools.normalize`](openproblems/tools/normalize.py). The original data +stored in `adata.X` is automatically stored in `adata.layers["counts"]` for later +reference in the case the a metric needs to access the unnormalized data. + +To test the performance of a dataset, method, or metric, you can use the command-line +interface: ```shell openproblems-cli test --help @@ -219,8 +315,8 @@ openproblems-cli test --help The task directory structure is as follows -``` -opensproblems/ +```text +openproblems/ - tasks/ - task_name/ - README.md @@ -244,60 +340,81 @@ opensproblems/ `api.py` should implement the following functions: -``` + +```text check_dataset(AnnData adata) -> bool # checks that a dataset fits the task-specific schema check_method(AnnData adata) -> bool # checks that the output from a method fits the task-specific schema sample_dataset() -> AnnData adata # generates a simple dataset the fits the expected API sample_method(AnnData adata) -> AnnData adata # applies a simple modification that fits the method API ``` + -`README.md` should contain a description of the task as will be displayed on the website, followed by a description of the task API for dataset/method/metric authors. Note: everything after `## API` will be discarded in generating the webpage for the task. +`README.md` should contain a description of the task as will be displayed on the +website, followed by a description of the task API for dataset/method/metric authors. +Note: everything after `## API` will be discarded in generating the webpage for the +task. For adding datasets, methods and metrics, see above. ### Adding a new Docker container -Datasets, methods and metrics run inside Docker containers. We provide a few to start with, but if you have specific requirements, you may need to modify an existing image or add a new one. See the [Docker README](docker/README.md) for details. +Datasets, methods and metrics run inside Docker containers. We provide a few to start +with, but if you have specific requirements, you may need to modify an existing image or +add a new one. See the [Docker README](docker/README.md) for details. ## Code Style and Testing -`openproblems` is maintained at close to 100% code coverage. For datasets, methods, and metrics, tests are generated programatically from each task's `api.py`. See the [Adding a new task](#adding-a-new-task) section for instructions on creating this file. +`openproblems` is maintained at close to 100% code coverage. For datasets, methods, and +metrics, tests are generated programatically from each task's `api.py`. See the [Adding +a new task](#adding-a-new-task) section for instructions on creating this file. -For additions outside this core functionality, contributors are encouraged to write tests for their code -- but if you do not know how to do so, please do not feel discouraged from contributing code! Others can always help you test your contribution. +For additions outside this core functionality, contributors are encouraged to write +tests for their code -- but if you do not know how to do so, please do not feel +discouraged from contributing code! Others can always help you test your contribution. -Code is tested by GitHub Actions when you push your changes. However, if you wish to test locally, you can do so with the following command: -``` +Code is tested by GitHub Actions when you push your changes. However, if you wish to +test locally, you can do so with the following command: + +```shell cd openproblems pip install --editable .[test,r] pytest -v ``` -You may run specific tests with +You may run specific tests quickly with ```shell -pytest -k my_task +PYTEST_MAX_RETRIES=0 pytest -k my_task ``` The test suite also requires Python>=3.7, R>=4.0, and Docker to be installed. -The benchmarking suite is tested by GitHub Actions when you push your changes. However, if you wish to test locally, you can do so with the following command: -``` +The benchmarking suite is tested by GitHub Actions when you push your changes. However, +if you wish to test locally, you can do so with the following command: + +```shell cd workflow snakemake -j 4 docker cd .. nextflow run -resume -profile test,docker openproblems-bio/nf-openproblems ``` -The benchmarking suite also requires Python>=3.7, snakemake, nextflow, and Docker to be installed. +The benchmarking suite also requires Python>=3.7, snakemake, nextflow, and Docker to be +installed. -Code style is dictated by [`black`](https://pypi.org/project/black/#installation-and-usage) and [`flake8`](https://flake8.pycqa.org/en/latest/) with [`hacking`](https://github.com/openstack/hacking). Code is automatically reformatted by [`pre-commit`](https://pre-commit.com/) when you push to GitHub. +Code style is dictated by +[`black`](https://pypi.org/project/black/#installation-and-usage) and +[`flake8`](https://flake8.pycqa.org/en/latest/) with +[`hacking`](https://github.com/openstack/hacking). Code is automatically reformatted by +[`pre-commit`](https://pre-commit.com/) when you push to GitHub. ## Code of Conduct We abide by the principles of openness, respect, and consideration of others -of the Python Software Foundation: https://www.python.org/psf/codeofconduct/ and by +of the Python Software Foundation: and by the Contributor Covenant. See our [Code of Conduct](CODE_OF_CONDUCT.md) for details. ## Attribution -This `CONTRIBUTING.md` was adapted from [scikit-learn](https://github.com/scikit-learn/scikit-learn/blob/master/CONTRIBUTING.md). +This `CONTRIBUTING.md` was adapted from +[scikit-learn](https://github.com/scikit-learn/scikit-learn/blob/main/CONTRIBUTING.md). diff --git a/README.md b/README.md index c65712041a..5b5a3799a3 100644 --- a/README.md +++ b/README.md @@ -12,38 +12,64 @@ Formalizing and benchmarking open problems in single-cell genomics. [**Visit the Open Problems Website.**](https://openproblems.bio/) - ## Guides + * For contributing guidelines, see [CONTRIBUTING.md](CONTRIBUTING.md) * For instructions on editing Docker images, see [docker/README.md](docker/README.md) -* For instructions on using the `openproblems-cli`, see [openproblems/api/README.md](https://github.com/singlecellopenproblems/SingleCellOpenProblems/tree/master/openproblems/api) -* For a walkthrough of the GitHub Actions workflows and AWS Sagemaker, see [SAGEMAKER.md](SAGEMAKER.md) +* For instructions on using the `openproblems-cli`, see + [openproblems/api/README.md](https://github.com/singlecellopenproblems/SingleCellOpenProblems/tree/master/openproblems/api) +* For a walkthrough of the GitHub Actions workflows and AWS Sagemaker, see + [SAGEMAKER.md](SAGEMAKER.md) * For a description of existing an proposed tasks, see [TASKS.md](TASKS.md) * For a video introduction to this GitHub repository, watch our [Repository introduction](https://www.youtube.com/watch?v=tHempZCdXyA) * For a video tutorial on adding new tasks, watch our [How to add a new task tutorial](https://www.youtube.com/watch?v=tgVG3Hp6mBc) ## The team -**Core**: -* Scott Gigante (@scottgigante) -* Daniel Burkhardt (@dburkhardt) -* Malte Luecken (@LuckyMD) -* Angela Pisco (@aopisco) -* Olga Botvinnik (@olgabot) - -**Task authors** (_alphabetically_): -* Emma Dann (@emdann) - Differential abundance -* Wes Lewis (@weslewis) - Differential abundance and data denoising -* Mohammad Lotfallahi (@M0hammadL) - Label projection task -* Qian Qin (@qinqian) - Predicting gene expression from chromatin accessibility -* Daniel Strobl (@danielStrobl) - Batch integration -* Michael Vinyard (@mvinyard) - Stress preservation in dimensionality reduction -* Florian Wagner (@flo-compbio) - Data denoising +**Core** (_alphabetically_): + +* Daniel Burkhardt (@dburkhardt), Cellarity +* Robrecht Cannoodt (@rcannoodt), Data Intuitive +* Scott Gigante (@scottgigante-immunai), Immunai +* Christopher Lance (@xlancelottx), Helmholtz Munich +* Malte Luecken (@LuckyMD), Helmholtz Munich +* Angela Pisco (@aopisco), CZ Biohub + +**Task leaders** (_alphabetically_): + +Full tasks (>1 datasets, >5 methods): + +* Batch integration - Daniel Strobl (@danielStrobl) +* Cell-cell communication - Daniel Dimitrov (@dbdimitrov) +* Dimensionality reduction - Michael Vinyard (@mvinyard) and Luke Zappia (@lazappi) +* Label projection - Nick Markov (@mxposed) +* Spatial deconvolution - Alma Anderson (@almaan) and Giovanni Palla (@giovp) + +Task stubs: + +* Data denoising - Wes Lewis (@weslewis) +* Multimodal data integration - The Open Problems core team + +Tasks in discussion: + +* ATAC denoising - Dominik Otto (@katosh) +* Differential abundance - Emma Dann (@emdann) +* Regulatory effect prediction - Qian (Alvin) Qin (@qinqian) **Supervision** (_alphabetically_): + +* Jonathan Bloom (@jbloom22) * Smita Krishnaswamy, Yale * Fabian Theis, Helmholtz Munich **Chan Zuckerberg Initiative Support** (_alphabetically_): + * Jonah Cool * Fiona Griffin +* Ivana Williams + +**Contributors**: + +See the long list of all those who contributed datasets, methods, metrics, or +infrastructure code +[here](https://github.com/openproblems-bio/openproblems/graphs/contributors) diff --git a/SAGEMAKER.md b/SAGEMAKER.md index 46c6cf6e05..15b0146d83 100644 --- a/SAGEMAKER.md +++ b/SAGEMAKER.md @@ -15,28 +15,30 @@ This document is a guide to using the Open Problems in Single-Cell Analysis GitH In addition to this guide, we've produced a video tutorial, [**Using GitHub and AWS for Open Problems in Single-Cell Analysis**](https://www.youtube.com/watch?v=mNu8-KR7UFY), which walks through every step of the process. In the video, we provide details that are helpful for troubleshooting if you're having issues. The video also performs all of the steps in the process from start to finish so you can see what needs to happen at each step. However, because of the troubleshooting tips and included wait times, the video is 40 minutes long. To make it quicker to get started, we've written out the steps in the process here and included time-stamped links to the video in case you'd like to see a specific step performed live. You also might find it helpful to consult AWS Documentation for bringing your own image to AWS SageMaker. You have two options using either the command-line interface (CLI) or a web interface (Console): + * [Custom Image with SageMaker Studio (CLI)](https://docs.aws.amazon.com/sagemaker/latest/dg/studio-byoi-create-sdk.html) * [Custom Image with SageMaker Studio (Console)](https://docs.aws.amazon.com/sagemaker/latest/dg/studio-byoi.html). There is a 1:1 correspondence between the steps to set up SageMaker using the CLI and the Console UI. We use the Console in our video and this guide, but feel free to use the CLI if you are more comfortable with it. ### Table of Contents -- [Introduction to the GitHub repository](#introduction-to-the-github-repository) + +* [Introduction to the GitHub repository](#introduction-to-the-github-repository) * [Why Docker containers?](#why-docker-containers) * [Prototyping within Docker containers](#prototyping-within-docker-containers) -- [Getting started and forking the GitHub repository](#getting-started-and-forking-the-github-repository) -- [Editing a Dockerfile](#editing-a-dockerfile) -- [Find your Docker container on the Elastic Container Registry](#find-your-docker-container-on-the-elastic-container-registry) -- [Attach your Image to SageMaker Studio](#attach-your-image-to-sagemaker-studio) -- [Add user to SageMaker Studio](#add-user-to-sagemaker-studio) -- [Open SageMaker Studio and Launch a Notebook using a Custom Image](#open-sagemaker-studio-and-launch-a-notebook-using-a-custom-image) +* [Getting started and forking the GitHub repository](#getting-started-and-forking-the-github-repository) +* [Editing a Dockerfile](#editing-a-dockerfile) +* [Find your Docker container on the Elastic Container Registry](#find-your-docker-container-on-the-elastic-container-registry) +* [Attach your Image to SageMaker Studio](#attach-your-image-to-sagemaker-studio) +* [Add user to SageMaker Studio](#add-user-to-sagemaker-studio) +* [Open SageMaker Studio and Launch a Notebook using a Custom Image](#open-sagemaker-studio-and-launch-a-notebook-using-a-custom-image) * [Selecting an instance type](#selecting-an-instance-type) * [Failed to start kernel (image does not exist)](#failed-to-start-kernel-image-does-not-exist) - ## Introduction to the GitHub repository + [Watch this section of the tutorial starting at [0:00]](https://www.youtube.com/watch?v=mNu8-KR7UFY&t=0s) Open Problems in Single-Cell Analysis is a project to aggregate and benchmark solutions to formalized problems in single-cell analysis. @@ -47,46 +49,51 @@ To facilitate comparing methods designed for a particular task, we've created a Because methods developers use different packages with their own sets dependencies and language requirements, we run each method, metric, and dataset loader in a Docker container. You can configure these containers to use a specific operating system, programming language, and any dependencies needed for a method. Each Docker containers is specified by a `Dockerfile` within the [`docker/`](https://github.com/singlecellopenproblems/SingleCellOpenProblems/tree/master/docker) directory of the Open Problems GitHub repository. Consult the [Docker documentation](https://docs.docker.com/engine/reference/builder/) for guidance on creating a `Dockerfile`. -You can find instructions for adding a new Docker image in our [Docker README.md](https://github.com/singlecellopenproblems/SingleCellOpenProblems/blob/master/docker/README.md). - +You can find instructions for adding a new Docker image in our [Docker README.md](https://github.com/openproblems-bio/openproblems/blob/main/docker/README.md). ### Prototyping within Docker containers + To facilitate prototyping within Docker containers, we've set up the Open Problems workflows to automatically compile containers and upload them to the Amazon Web Services (AWS) [Elastic Container Registry](https://aws.amazon.com/ecr/). You can attach these Images to an AWS SageMaker Domain. From there, you can launch Jupyter notebooks using your custom image. You can then test out code contributions within the image before committing to the GitHub repository. ## Getting started and forking the GitHub repository + [Watch this section of the tutorial starting at [1:20]](https://www.youtube.com/watch?v=mNu8-KR7UFY&t=80s) The goal for this section is to create a fork, activate GitHub Actions, configure your AWS secrets, edit the GitHub repository locally, and push to your fork. -Before you get started, please read our [Contributor Guide](https://github.com/singlecellopenproblems/SingleCellOpenProblems/blob/master/CONTRIBUTING.md). It contains important up-to-date information about how to best contribute to Open Problems. +Before you get started, please read our [Contributor Guide](https://github.com/openproblems-bio/openproblems/blob/main/CONTRIBUTING.md). It contains important up-to-date information about how to best contribute to Open Problems. -Detailed instructions for the forking process can be found in the [Submitting new features](https://github.com/singlecellopenproblems/SingleCellOpenProblems/blob/master/CONTRIBUTING.md#submitting-new-features) section of our Contributor Guide. +Detailed instructions for the forking process can be found in the [Submitting new features](https://github.com/openproblems-bio/openproblems/blob/main/CONTRIBUTING.md#submitting-new-features) section of our Contributor Guide. ## Editing a Dockerfile + [Watch this section of the tutorial starting at [10:08]](https://youtu.be/mNu8-KR7UFY?t=607) All the Open Problems Docker containers are specified by Dockerfiles in the `docker/` folder. -To edit your custom Docker image on your fork, follow the instructions for editing Docker images in our [Docker Guide](https://github.com/singlecellopenproblems/SingleCellOpenProblems/blob/master/docker/README.md). +To edit your custom Docker image on your fork, follow the instructions for editing Docker images in our [Docker Guide](https://github.com/openproblems-bio/openproblems/blob/main/docker/README.md). Note, if you're interested in creating a container for R, you need to inherit from the `openproblems-r-base` image. Additional resources + * [SageMaker Studio Custom Image Samples](https://github.com/aws-samples/sagemaker-studio-custom-image-samples) - This GitHub contains example images that already work with SageMaker Studio * [Bringing your own R environment to Amazon SageMaker Studio](https://aws.amazon.com/blogs/machine-learning/bringing-your-own-r-environment-to-amazon-sagemaker-studio/) - This AWS ML Blog details the start to finish process of creating an R environment for SageMaker ## Find your Docker container on the Elastic Container Registry + [Watch this section starting at [15:08]](https://www.youtube.com/watch?v=mNu8-KR7UFY&t=905s) Once the *Run Benchmarks* workflow has successfully completed the Upload Docker job, your docker images are uploaded to the AWS [Elastic Container Registry (ECR)](https://aws.amazon.com/ecr/). You can navigate to the ECR from the AWS Console. You should have received an email with your AWS User Credentials that includes a Console Login URL. Enter your username and password to login. You can then search for the `Elastic Container Registry` service in the search bar at the top of the screen. -Next, follow the steps to locate your image on the ECR in the [Building Docker Images using Github Actions workflows](https://github.com/singlecellopenproblems/SingleCellOpenProblems/blob/master/docker/README.md#building-docker-images-through-github-actions-workflows) section of our Docker guide. +Next, follow the steps to locate your image on the ECR in the [Building Docker Images using Github Actions workflows](https://github.com/openproblems-bio/openproblems/blob/main/docker/README.md#building-docker-images-through-github-actions-workflows) section of our Docker guide. ## Attach your Image to SageMaker Studio + [Watch this section of the tutorial starting at [18:50]](https://www.youtube.com/watch?v=mNu8-KR7UFY&t=1130s) To use your image in a SageMake Studio notebook, you need to attach the image to the domain running the SageMaker app. @@ -96,10 +103,12 @@ First, navigate to the SageMaker Studio control panel from the AWS Console. You Next, follow the steps in the AWS Documentation to [Attach a Custom Image to an Existing SageMaker Studio Domain](https://docs.aws.amazon.com/sagemaker/latest/dg/studio-byoi-attach.html). If you have already launched SageMaker Studio, you will need to restart your SageMaker Studio app so it can see the new image: + 1. Follow the steps in the [Shut Down SageMaker Resources](https://docs.aws.amazon.com/sagemaker/latest/dg/notebooks-run-and-manage-shut-down.html) tutorial. -2. Follow the steps below to [Open SageMaker Studio](https://github.com/singlecellopenproblems/SingleCellOpenProblems/blob/master/SAGEMAKER.md#open-sagemaker-studio-and-launch-a-notebook-using-a-custom-image). +2. Follow the steps below to [Open SageMaker Studio](https://github.com/openproblems-bio/openproblems/blob/main/SAGEMAKER.md#open-sagemaker-studio-and-launch-a-notebook-using-a-custom-image). A few caveats to watch out for during this step: + * The Image name should be the Image Tag from the ECR. **Use only lower case letters and dashes** (should match RegEx `[a-z\-]`). No underscores or other special characters are allowed. * When editing the Image name and Image display name, you must click outside the box to validate the text. * The `IAM role` should be the `AmazonSageMaker-ExecutionRole` available within the dropdown. @@ -108,12 +117,13 @@ A few caveats to watch out for during this step: * If you see an error after you click Submit stating that the domain already being updated, just wait a few seconds. Someone else tried to attach an image to the same domain at the same time as you, and the domain can only process one request at a time. Additional resources + * [Custom SageMaker image specifications](https://docs.aws.amazon.com/sagemaker/latest/dg/studio-byoi-specs.html) - specifications for SageMaker Studio Images * [Field Notes: Accelerate Research with Managed Jupyter on Amazon SageMaker](https://aws.amazon.com/blogs/architecture/field-notes-accelerate-research-with-managed-jupyter-on-amazon-sagemaker/) - a start to finish blog on creating a SageMaker environment with custom images * [SageMaker Custom Images - Issue #9](https://github.com/aws-samples/sagemaker-studio-custom-image-samples/issues/9) - an issue @dburkhardt encountered while creating images for this project. SageMaker does some wonky modifications of the images before launching a notebook. This may be a hangup for you if you try to install a custom Python environment. - ## Add user to SageMaker Studio + [Watch this section of the tutorial starting at [27:16]](https://www.youtube.com/watch?v=mNu8-KR7UFY&t=1635s) You only need to do this step if you haven't already created a user on the SageMaker Studio console for the domain you're using. @@ -128,12 +138,11 @@ The user configuration page should look like this: - To create the user, hit Submit. If you see an error about the domain status, wait a few seconds and try again. One only user can be added to a domain at a given time. ## Open SageMaker Studio and Launch a Notebook using a Custom Image -[Watch this section of the tutorial starting at [28:06]](https://www.youtube.com/watch?v=mNu8-KR7UFY&t=1685s) +[Watch this section of the tutorial starting at [28:06]](https://www.youtube.com/watch?v=mNu8-KR7UFY&t=1685s) To launch SageMaker, click on the Open Studio button next to your user name. @@ -148,6 +157,7 @@ If you haven’t launched the Studio before, you should see a loading screen for Once it’s working, you should see a Jupyter Lab interface. For more information on the UI, please consult the AWS [Guide to the SageMaker Studio Interface](https://docs.aws.amazon.com/sagemaker/latest/dg/studio-ui.html). To launch a Notebook with your custom image: + 1. Scroll down to the "Notebooks and Compute Resources" 2. Click on the Select a SageMaker Image dropdown 3. Select your custom image @@ -185,7 +195,6 @@ We've selected three instances to use during the Jamboree. Note, it is possible To change your instance, follow the [Change Instance Type](https://docs.aws.amazon.com/sagemaker/latest/dg/notebooks-run-and-manage-switch-instance-type.html) tutorial from AWS. - ### Failed to start kernel (image does not exist) If you see the following error: @@ -195,6 +204,7 @@ If you see the following error: Don't fret! This means that the image you were using has been deleted from ECR. We do this when new images are uploaded with the same tag to save space. To add the new image version + 1. Go back to the ECR, and find the URI for the newest version of the image that you want to work on. 2. From the SageMaker Studio Control Panel, find the attached image that you'd like to revise at the bottom of the window. 3. Click on "Attach version" next to the image. diff --git a/TASKS.md b/TASKS.md index 4e1e08ce39..6ed2ad358f 100644 --- a/TASKS.md +++ b/TASKS.md @@ -1,94 +1,253 @@ # Benchmarking Task Descriptions Table of Contents - * [Predicting gene expression from chromatin accessibility](#predicting-gene-expression-from-chromatin-accessibility) - * [Data denoising and imputation](#data-denoising-and-imputation) - * [Dimensionality reduction for visualization](#dimensionality-reduction-for-visualization) - * [Cell type label projection from a reference atlas](#cell-type-label-projection-from-a-reference-atlas) - * [Multimodal data integration (e.g. combining CITE-seq with RNA-seq)](#multimodal-data-integration--eg-combining-cite-seq-with-rna-seq-) - * [Differential abundance from experimental perturbations](#differential-abundance-from-experimental-perturbations) - * [Data integration and batch normalization](#data-integration-and-batch-normalization) - * [Further tasks](#further-tasks) +* [Predicting gene expression from chromatin accessibility + ](#predicting-gene-expression-from-chromatin-accessibility) +* [Data denoising and imputation](#data-denoising-and-imputation) +* [Dimensionality reduction for visualization + ](#dimensionality-reduction-for-visualization) +* [Cell type label projection from a reference atlas + ](#cell-type-label-projection-from-a-reference-atlas) +* [Multimodal data integration](#multimodal-data-integration) +* [Differential abundance from experimental perturbations + ](#differential-abundance-from-experimental-perturbations) +* [Data integration and batch normalization](#data-integration-and-batch-normalization) +* [Further tasks](#further-tasks) + +## Implemented Tasks ### Predicting gene expression from chromatin accessibility -Several recently published techniques allow for simultaneous measurement of chromatin accessibility and gene expression in the single-cell level. For example, [SHARE-seq](https://pubmed.ncbi.nlm.nih.gov/33098772/) uses droplet-based sequencing to measure both modalities, [sci-CAR](https://doi.org/10.1126/science.aau0730) uses plate-based method to sequence both information. One challenge to analyze this data is to link the chromatin accessibility to gene expression in single-cells, which is useful to understand the single-cell trajectory and regulatory effect. - -Here, we compare methods for calculating gene scores (regulatory effect or chromatin potential) from the peak matrix of single-cell ATAC-seq modality, and evaluate their gene expression matrix prediction performance of single-cell RNA-seq by measuring consistency between the gene scores and gene expression across cells. - -Two representative software [ArchR](https://www.archrproject.com/bookdown/calculating-gene-scores-in-archr.html) and [MAESTRO](https://github.com/liulab-dfci/MAESTRO/blob/d58fc186a4329febde3b0d5b213c2d0edf8de44c/example/Gene_activity_modelling/Gene_activity_modelling.md), each has different way of assigning peak weights upon genes include a set of methods, but also share some common property. A typical algorithm includes the following common steps: - -1) Given a peak matrix (cells x peaks), calculate the peaks weight matrix based on distances between gene transcription start site (TSS) and the peaks, this might involve reweighting potential Pol II elongation effect and masking of closest gene effect. -2) Dot product between the peak matrix and peak weight matrix to get the gene score matrix (cells x genes). -3) Evaluation of the regulatory effect in the single-cell levels by calculating the following metrics across genes for each single-cell, alternatively, the gene expression matrix and gene score matrix can be imputed by aggregating within nearest neighboring cells or cells within a cluster to get the pseudo-bulk gene expression matrix and pseudo-bulk gene score matrix (bulk/cluster x genes), and evaluate the metrics across genes for each bulk/cluster. +Several recently published techniques allow for simultaneous measurement of chromatin +accessibility and gene expression in the single-cell level. For example, +[SHARE-seq](https://pubmed.ncbi.nlm.nih.gov/33098772/) uses droplet-based +sequencing to measure both modalities, +[sci-CAR](https://doi.org/10.1126/science.aau0730) uses plate-based method to sequence +both information. One challenge to analyze this data is to link the chromatin +accessibility to gene expression +in single-cells, which is useful to understand the single-cell trajectory and regulatory +effect. + +Here, we compare methods for calculating gene scores (regulatory effect or chromatin +potential) from the peak matrix of single-cell ATAC-seq modality, and evaluate their +gene expression matrix prediction performance of single-cell RNA-seq by measuring +consistency between the gene scores and gene expression across cells. + +Two representative software +[ArchR](https://www.archrproject.com/bookdown/calculating-gene-scores-in-archr.html) and +[MAESTRO](https://github.com/liulab-dfci/MAESTRO/blob/d58fc186a4329febde3b0d5b213c2d0edf8de44c/example/Gene_activity_modelling/Gene_activity_modelling.md), +each has different way of assigning peak weights upon genes include a set of methods, +but also share some common property. A typical algorithm includes the following common +steps: + +1) Given a peak matrix (cells x peaks), calculate the peaks weight matrix based on + distances between gene transcription start site (TSS) and the peaks, this might + involve reweighting potential Pol II elongation effect and masking of closest gene + effect. +2) Dot product between the peak matrix and peak weight matrix to get the gene score + matrix (cells x genes). +3) Evaluation of the regulatory effect in the single-cell levels by calculating the + following metrics across genes for each single-cell, alternatively, the gene + expression matrix and gene score matrix can be imputed by aggregating within nearest + neighboring cells or cells within a cluster to get the pseudo-bulk gene expression + matrix and pseudo-bulk gene score matrix (bulk/cluster x genes), and evaluate the + metrics across genes for each bulk/cluster. ### Data denoising and imputation -Despite the proposed application of these protocols for describing per-cell information within a tissue or cell fraction of interest, single-cell RNA-Seq protocols only detect a small fraction of the mRNA molecules present in each cell. As a result, the measurements (UMI counts) observed for each gene and each cell are associated with generally high levels of technical noise ([Grün et al., 2014](https://www.nature.com/articles/nmeth.2930)). Denoising describes the task of estimating the true expression level of each gene in each cell. In the single-cell literature, this task is also referred to as imputation, a term which is typically used for missing data problems in statistics. Similar to the use of the terms "dropout", "missing data", and "technical zeros", this terminology can create confusion about the underlying measurement process ([Sarkar and Stephens, 2020](https://www.biorxiv.org/content/10.1101/2020.04.07.030007v2)). +Despite the proposed application of these protocols for describing per-cell information +within a tissue or cell fraction of interest, single-cell RNA-Seq protocols only detect +a small fraction of the mRNA molecules present in each cell. As a result, the +measurements (UMI counts) observed for each gene and each cell are associated with +generally high levels of technical noise +([Grün et al., 2014](https://www.nature.com/articles/nmeth.2930)). Denoising describes +the task of estimating the true expression level of each gene in each cell. In the +single-cell literature, this task is also referred to as imputation, a term which is +typically used for missing data problems in statistics. Similar to the use of the terms +"dropout", "missing data", and "technical zeros", this terminology can create confusion +about the underlying measurement process ([Sarkar and Stephens, +2020](https://www.biorxiv.org/content/10.1101/2020.04.07.030007v2)). + +A key challenge in evaluating denoising methods is the general lack of a ground truth. +A recent benchmark study ([Hou et al., +2020](https://genomebiology.biomedcentral.com/articles/10.1186/s13059-020-02132-x)) +relied on flow-sorted datasets, mixture control experiments +([Tian et al., 2019](https://www.nature.com/articles/s41592-019-0425-8)), and +comparisons with bulk RNA-Seq data. Since each of these approaches suffers from specific +limitations, it is difficult to combine these different approaches into a single +quantitative measure of denoising accuracy. Here, we instead rely on an approach termed +molecular cross-validation (MCV), which was specifically developed to quantify denoising +accuracy in the absence of a ground truth +([Batson et al., 2019](https://www.biorxiv.org/content/10.1101/786269v1)). In MCV, the +observed molecules in a given scRNA-Seq dataset are first partitioned between a training +and a test dataset. Next, a denoising method is applied to the training dataset. +Finally, denoising accuracy is measured by comparing the result to the test dataset. The +authors show that both in theory and in practice, the measured denoising accuracy is +representative of the accuracy that would be obtained on a ground truth dataset. - -A key challenge in evaluating denoising methods is the general lack of a ground truth. A recent benchmark study ([Hou et al., 2020](https://genomebiology.biomedcentral.com/articles/10.1186/s13059-020-02132-x)) relied on flow-sorted datasets, mixture control experiments ([Tian et al., 2019](https://www.nature.com/articles/s41592-019-0425-8)), and comparisons with bulk RNA-Seq data. Since each of these approaches suffers from specific limitations, it is difficult to combine these different approaches into a single quantitative measure of denoising accuracy. Here, we instead rely on an approach termed molecular cross-validation (MCV), which was specifically developed to quantify denoising accuracy in the absence of a ground truth ([Batson et al., 2019](https://www.biorxiv.org/content/10.1101/786269v1)). In MCV, the observed molecules in a given scRNA-Seq dataset are first partitioned between a training and a test dataset. Next, a denoising method is applied to the training dataset. Finally, denoising accuracy is measured by comparing the result to the test dataset. The authors show that both in theory and in practice, the measured denoising accuracy is representative of the accuracy that would be obtained on a ground truth dataset. - #### The metrics -Metrics for evaluating denoising of single cell datasets aim to characterize how well a denoised training subset compares to a held out testing subset, or otherwise to a secondary measurement (RNA-seq, single-cell atlas datasets, purified cell fractions). +Metrics for evaluating denoising of single cell datasets aim to characterize how well a +denoised training subset compares to a held out testing subset, or otherwise to a +secondary measurement (RNA-seq, single-cell atlas datasets, purified cell fractions). -- MSE: - - Let f(i)∈F be the denoised scRNA-seq measurement of cell i in the training set. Let g(j)∈G be the scRNA-seq measurement of cell j in the test set. MSE calculates the squared error difference between the observed cells in F and those in G. -- Poisson Negative Log Likelihood Loss (Poisson NLLL): - - Let f(i)∈F be the denoised scRNA-seq measurement of cell i in the training set. Let g(j)∈G be the scRNA-seq measurement of cell j in the test set. Poisson NLLL calculates the loss metric associated with likelihoods that F and G are sampled from the same Poisson distribution. +* MSE: + * Let f(i)∈F be the denoised scRNA-seq measurement of cell i in the training set. Let + g(j)∈G be the scRNA-seq measurement of cell j in the test set. MSE calculates the + squared error difference between the observed cells in F and those in G. +* Poisson Negative Log Likelihood Loss (Poisson NLLL): + * Let f(i)∈F be the denoised scRNA-seq measurement of cell i in the training set. + Let g(j)∈G be the scRNA-seq measurement of cell j in the test set. Poisson NLLL + calculates the loss metric associated with likelihoods that F and G are sampled from + the same Poisson distribution. ### Dimensionality reduction for visualization -Dimensional reduction is one of the key challenges in single-cell data representation. Routine single-cell RNA sequencing (scRNA-seq) experiments measure cells in roughly 20,000-30,000 dimensions (i.e., features - mostly gene transcripts but also other functional elements encoded in mRNA such as lncRNAs.) Since its inception, scRNA-seq experiments have been growing in terms of the number of cells measured. Originally, cutting-edge SmartSeq experiments would yield a few hundred cells, at best. Now, it is not uncommon to see experiments that yield over 100,000 cells or even > 1 million cells. - -Each *feature* in a dataset functions as a single dimension. While each of the ~30,000 dimensions measured in each cell (not accounting for roughly 75-90% data dropout per cell, another issue entirely), likely contribute to some sort of data structure, the overall structure of the data is diluted due to the *"curse of dimensionality"*. In short, it's difficult to visualize the contribution of each individual gene in a way that makes sense to the human eye, i.e., two or three dimensions (at most). Thus, we need to find a way to dimensionally reduce the data for visualization and interpretation. +Dimensional reduction is one of the key challenges in single-cell data representation. +Routine single-cell RNA sequencing (scRNA-seq) experiments measure cells in roughly +20,000-30,000 dimensions (i.e., features - mostly gene transcripts but also other +functional elements encoded in mRNA such as lncRNAs.) Since its inception, scRNA-seq +experiments have been growing in terms of the number of cells measured. Originally, +cutting-edge SmartSeq experiments would yield a few hundred cells, at best. Now, it is +not uncommon to see experiments that yield over [100,000 +cells](https://www.nature.com/articles/s41586-018-0590-4) or even [>1 million +cells.](https://www.10xgenomics.com/blog/our-13-million-single-cell-dataset-is-ready-to-download) + +Each *feature* in a dataset functions as a single dimension. While each of the ~30,000 +dimensions measured in each cell (not accounting for roughly 75-90% data dropout per +cell, another issue entirely), likely contribute to some sort of data structure, the +overall structure of the data is diluted due to the +[*"curse of dimensionality"*](https://en.wikipedia.org/wiki/Curse_of_dimensionality). In +short, it's difficult to visualize the contribution of each individual gene in a way +that makes sense to the human eye, i.e., two or three dimensions (at most). Thus, we +need to find a way to [dimensionally +reduce](https://en.wikipedia.org/wiki/Dimensionality_reduction) the data for +visualization and interpretation. ### Cell type label projection from a reference atlas -Label projection refers to the automatic identification of cell identity labels in a test or query dataset based on a reference dataset (or datasets) that typically contains curated, manually labeled cells. This process enables the rapid annotation of new datasets, which is becoming increasingly important as data generation becomes easier and larger in scale. - -Label projection methods range from logistic regression which ignores batch information, to methods that perform projection based on a batch-integrated embedding. For a review, see [Abdelaal et al. (2019)](https://doi.org/10.1186/s13059-019-1795-z). - -### Multimodal data integration (e.g. combining CITE-seq with RNA-seq) - -Multimodal data integration refers to the task of combining together two datasets of different modalities of measurements (e.g., single-cell RNA sequencing and single-cell ATAC sequencing) on different observations of the same biological system. Integrating such measurements allows us to analyze the interaction between the different modalities, without requiring an explicitly joint measurement like [sci-CAR](https://doi.org/10.1126/science.aau0730) or [CITE-seq](https://doi.org/10.1038/nmeth.4380). - -Here, we use such joint measurement modalities as a gold standard for multimodal data alignment; since we know the joint measurements are obtained from the same cells, a method which successfully realigns cells from one modality to their paired measurements from another modality (without prior knowledge of the shared cell identity) is known to be performant on the given dataset. - -### Differential abundance from experimental perturbations - -An increasing number of single-cell studies are designed to compare reference single-cell atlases to phenotypes measured in disease, development, aging or upon experimental treatments. Perturbed cell states can be detected as shifts in abundance of cells on the transcriptional state space. -Our goal is to evaluate the accuracy and sensitivity of approaches to quantify Differential Abundance (DA). - -There are several challenges associated with this analysis, that will likely require different subtasks: - -- Testing on cell clusters or populations defined _a priori_ can reduce the sensitivity to detect shifts in small cell subpopulations and when cellular states form a continuum -- Accounting for compositional biases: with relatively small numbers of cells profiled per experimental sample, the depletion of a single cell population leads to a relative increase in frequency of other populations. Models that test abundance changes of different populations independently may falsely detect these enrichment as true effects (see [Buettner et al. 2020](https://www.biorxiv.org/content/10.1101/2020.12.14.422688v2.full.pdf) for a detailed description of this bias). -- Replicate measurements from the same biological condition can show substantial variance in cell abundances -- Technical and confounding sources of variation can introduce differences in cell numbers between biological samples. This is especially true for comparative studies on human tissues, which we expect will scale up in size and experimental design complexity in the near future. - -**Datasets:** An additional challenge is the lack of a ground-truth. To overcome this problem, we will simulate condition labels on true single-cell datasets based by generating a smooth underlying condition probability. This will require defining minima and maxima of condition probability and interpolating smoothly between these points, with an approach such as [Inverse Distance Weighting](https://en.wikipedia.org/wiki/Inverse_distance_weighting). - -**Methods:** This task is commonly approached by testing for differences in composition of predefined and discrete cell type clusters ([Haber et al. 2017](https://www.nature.com/articles/nature24489), [scDC](https://bmcbioinformatics.biomedcentral.com/articles/10.1186/s12859-019-3211-9), [scCODA](https://www.biorxiv.org/content/10.1101/2020.12.14.422688v2.full.pdf)), while methods quantify differences over KNN graphs ([MELD](https://www.nature.com/articles/s41587-020-00803-5), [Milo](https://www.biorxiv.org/content/10.1101/2020.11.23.393769v1.full), [DAseq](https://www.biorxiv.org/content/10.1101/711929v3)) or differentiation trajectories ([_condiments_](https://www.biorxiv.org/content/10.1101/2021.03.09.433671v1)). - -**Metrics:** during the jamboree we will define which subtasks to prioritize. In general, we aim to include metrics that assess both the accuracy of the estimate of the effect size of differential abundance (such as the Mean Squared Error between the precited effect size for DA and the true condition probability) as well as the ability to detect subpopulations of cells that are similar in gene expression space and similarly enriched or depleted by an experimental perturbation (this is practically useful to enable downstream investigation of condition-specific cell states). +Label projection refers to the automatic identification of cell identity labels in a +test or query dataset based on a reference dataset (or datasets) that typically contains +curated, manually labeled cells. This process enables the rapid annotation of new +datasets, which is becoming increasingly important as data generation becomes easier and +larger in scale. + +Label projection methods range from logistic regression which ignores batch information, +to methods that perform projection based on a batch-integrated embedding. For a review, +see [Abdelaal et al. (2019)](https://doi.org/10.1186/s13059-019-1795-z). + +### Multimodal data integration + +Multimodal data integration refers to the task of combining together two datasets of +different modalities of measurements (e.g., single-cell RNA sequencing and single-cell +ATAC sequencing) on different observations of the same biological system. Integrating +such measurements allows us to analyze the interaction between the different modalities, +without requiring an explicitly joint measurement like +[sci-CAR](https://doi.org/10.1126/science.aau0730) or +[CITE-seq](https://doi.org/10.1038/nmeth.4380). + +Here, we use such joint measurement modalities as a gold standard for multimodal data +alignment; since we know the joint measurements are obtained from the same cells, a +method which successfully realigns cells from one modality to their paired measurements +from another modality (without prior knowledge of the shared cell identity) is known to +be performant on the given dataset. + +### Differential abundance from experimental perturbations + +An increasing number of single-cell studies are designed to compare reference +single-cell atlases to phenotypes measured in disease, development, aging or upon +experimental treatments. Perturbed cell states can be detected as shifts in abundance of +cells on the transcriptional state space. +Our goal is to evaluate the accuracy and sensitivity of approaches to quantify +Differential Abundance (DA). + +There are several challenges associated with this analysis, that will likely require +different subtasks: + +* Testing on cell clusters or populations defined *a priori* can reduce the sensitivity + to detect shifts in small cell subpopulations and when cellular states form a + continuum +* Accounting for compositional biases: with relatively small numbers of cells profiled + per experimental sample, the depletion of a single cell population leads to a relative + increase in frequency of other populations. Models that test abundance changes of + different populations independently may falsely detect these enrichment as true + effects (see [Buettner et al. 2020 + ](https://www.biorxiv.org/content/10.1101/2020.12.14.422688v2.full.pdf) for a detailed + description of this bias). +* Replicate measurements from the same biological condition can show substantial + variance in cell abundances +* Technical and confounding sources of variation can introduce differences in cell + numbers between biological samples. This is especially true for comparative studies + on human tissues, which we expect will scale up in size and experimental design + complexity in the near future. + +**Datasets:** An additional challenge is the lack of a ground-truth. To overcome this +problem, we will simulate condition labels on true single-cell datasets based by +generating a smooth underlying condition probability. This will require defining minima +and maxima of condition probability and interpolating smoothly between these points, +with an approach such as [Inverse Distance +Weighting](https://en.wikipedia.org/wiki/Inverse_distance_weighting). + +**Methods:** This task is commonly approached by testing for differences in composition +of predefined and discrete cell type clusters ([Haber et al. +2017](https://www.nature.com/articles/nature24489), +[scDC](https://bmcbioinformatics.biomedcentral.com/articles/10.1186/s12859-019-3211-9), +[scCODA](https://www.biorxiv.org/content/10.1101/2020.12.14.422688v2.full.pdf)), while +methods quantify differences over KNN graphs +([MELD](https://www.nature.com/articles/s41587-020-00803-5), +[Milo](https://www.biorxiv.org/content/10.1101/2020.11.23.393769v1.full), +[DAseq](https://www.biorxiv.org/content/10.1101/711929v3)) or differentiation +trajectories +([*condiments*](https://www.biorxiv.org/content/10.1101/2021.03.09.433671v1)). + +**Metrics:** during the jamboree we will define which subtasks to prioritize. In +general, we aim to include metrics that assess both the accuracy of the estimate of the +effect size of differential abundance (such as the Mean Squared Error between the +predicted effect size for DA and the true condition probability) as well as the ability +to detect subpopulations of cells that are similar in gene expression space and +similarly enriched or depleted by an experimental perturbation (this is practically +useful to enable downstream investigation of condition-specific cell states). ### Data integration and batch normalization -Batch (or data) integration methods integrate datasets across batches that arise from various biological (e.g., tissue, location, individual, species) and technical (e.g., ambient RNA, lab, protocol) sources. The goal of a batch integration method is to remove unwanted batch effects in the data, while retaining biologically-meaningful variation that can help us to detect cell identities, fit cellular trajectories, or understand patterns of gene or pathway activity. +Batch (or data) integration methods integrate datasets across batches that arise from +various biological (e.g., tissue, location, individual, species) and technical (e.g., +ambient RNA, lab, protocol) sources. The goal of a batch integration method is to remove +unwanted batch effects in the data, while retaining biologically-meaningful variation +that can help us to detect cell identities, fit cellular trajectories, or understand +patterns of gene or pathway activity. -Methods that integrate batches typically have one of three different types of output: a corrected feature matrix, a joint embedding across batches, and/or an integrated cell-cell similarity graph (e.g., a kNN graph). In order to define a consistent input and output for each method and metric, we have divided the batch integration task into three subtasks. These subtasks are: +Methods that integrate batches typically have one of three different types of output: a +corrected feature matrix, a joint embedding across batches, and/or an integrated +cell-cell similarity graph (e.g., a kNN graph). In order to define a consistent input +and output for each method and metric, we have divided the batch integration task into +three subtasks. These subtasks are: * Batch integration graphs * Batch integration embeddings * Batch integrated feature matrices -These subtasks collate methods that have the same data output type and metrics that evaluate this output. As corrected feature matrices can be turned into embeddings, which in turn can be processed into integrated graphs, methods overlap between the tasks. All methods are added to the graph subtask and imported into other subtasks from there. - -Metrics for this task can be divided into those that assess the removal of batch effects, and assessments of the conservation of biological variation. This can be a helpful distinction when devising new metrics. This task, including the subtask structure, was taken from a [benchmarking study of data integration methods](https://www.biorxiv.org/content/10.1101/2020.05.22.111161v2). This is a useful reference for more background reading on this task and the above concepts. - -### Further tasks - -Next to the already initiated tasks, there are many open problems in computational single-cell research that we have not yet captured. These include spatial transcriptomics tasks (e.g., inferring spatial graphs, detecting spatially variable genes, deconvolving spots), perturbation modeling (e.g., predicting perturbed transcriptome distributions), or inferring gene regulatory networks. These tasks would benefit from a well-defined task definition and an associated standardized benchmarking setup. If you are motivated, please don’t hesitate to find a group of kindred spirits to initiate one of these tasks! +These subtasks collate methods that have the same data output type and metrics that +evaluate this output. As corrected feature matrices can be turned into embeddings, +which in turn can be processed into integrated graphs, methods overlap between the +tasks. All methods are added to the graph subtask and imported into other subtasks from +there. + +Metrics for this task can be divided into those that assess the removal of batch +effects, and assessments of the conservation of biological variation. This can be a +helpful distinction when devising new metrics. This task, including the subtask +structure, was taken from a [benchmarking study of data integration +methods](https://www.biorxiv.org/content/10.1101/2020.05.22.111161v2). This is a useful +reference for more background reading on this task and the above concepts. + +## Further tasks + +Next to the already initiated tasks, there are many open problems in computational +single-cell research that we have not yet captured. These include spatial +transcriptomics tasks (e.g., inferring spatial graphs, detecting spatially variable +genes, deconvolving spots), perturbation modeling (e.g., predicting perturbed +transcriptome distributions), or inferring gene regulatory networks. These tasks would +benefit from a well-defined task definition and an associated standardized benchmarking +setup. If you are motivated, please don’t hesitate to find a group of kindred spirits to +initiate one of these tasks! diff --git a/TROUBLESHOOTING.md b/TROUBLESHOOTING.md index 26dfff25fd..b3ec751331 100644 --- a/TROUBLESHOOTING.md +++ b/TROUBLESHOOTING.md @@ -1,11 +1,13 @@ # Recommended steps for Troubleshooting -## Adding new code: +## Adding new code + 1. Read the logs of run_tests / run_benchmark: the python traceback should be shown 2. If this does not help, try running pytest locally per the instructions in CONTRIBUTING.md 3. If this does not help, try running nextflow locally per the instructions in CONTRIBUTING.md -## Adding new containers: +## Adding new containers + 1. Read the logs of run_tests / run_benchmark: the docker output should be shown 2. If this does not help, try building the images locally per the instructions in CONTRIBUTING.md diff --git a/docker/README.md b/docker/README.md index 4b7e0923b5..d5949faf26 100644 --- a/docker/README.md +++ b/docker/README.md @@ -1,34 +1,48 @@ # Guide to Docker images -This guide provides instructions on editing the Docker images used to run `methods`, `metrics`, and load `datasets` for the Open Problems benchmarking infrastructure. -Note, all images must comply to the [AWS SageMaker Custom Image Specifications](https://docs.aws.amazon.com/sagemaker/latest/dg/studio-byoi-specs.html). +This guide provides instructions on editing the Docker images used to run `methods`, +`metrics`, and load `datasets` for the Open Problems benchmarking infrastructure. -**Table of Contents** +Note, all images must comply to the [AWS SageMaker Custom Image +Specifications](https://docs.aws.amazon.com/sagemaker/latest/dg/studio-byoi-specs.html). + +### Table of Contents - [About Docker images](#about-docker-images) - [Available images](#available-images) - * [openproblems](#openproblems) - * [openproblems-r-base](#openproblems-r-base) - * [openproblems-r-extras](#openproblems-r-extras) - * [openproblems-python-extras](#openproblems-python-extras) + - [openproblems](#openproblems) + - [openproblems-r-base](#openproblems-r-base) + - [openproblems-r-extras](#openproblems-r-extras) + - [openproblems-python-extras](#openproblems-python-extras) - [Adding a package to the available images](#adding-a-package-to-the-available-images) - [Adding new images](#adding-new-images) - [Building Docker images locally](#building-docker-images-locally) -- [Building Docker images through GitHub Actions workflows](#building-docker-images-through-github-actions-workflows) -- [Pulling images from the ECR to your local machine](#pulling-images-from-the-ecr-to-your-local-machine) +- [Building Docker images through GitHub Actions workflows + ](#building-docker-images-through-github-actions-workflows) +- [Pulling images from the ECR to your local machine + ](#pulling-images-from-the-ecr-to-your-local-machine) - [Running Docker images locally](#running-docker-images-locally) - + + +#### Additional resources -**Additional resources** -* [Dockerfile Reference](https://docs.docker.com/engine/reference/builder/) - Documentation from Docker on how to write Dockerfiles -* [SageMaker Studio Custom Image Samples](https://github.com/aws-samples/sagemaker-studio-custom-image-samples/) - Example images from AWS designed for compatibility with SageMaker +- [Dockerfile Reference](https://docs.docker.com/engine/reference/builder/) + - Documentation from Docker on how to write Dockerfiles +- [SageMaker Studio Custom Image Samples + ](https://github.com/aws-samples/sagemaker-studio-custom-image-samples/) + - Example images from AWS designed for compatibility with SageMaker ## About Docker images -By default, all methods, metrics, and dataset loaders run in the `openproblems` docker image. If you require additional dependencies, you can either add them to an existing docker image, or if this is not possible due to conflicts, add a new one. +By default, all methods, metrics, and dataset loaders run in the `openproblems` docker +image. If you require additional dependencies, you can either add them to an existing +docker image, or if this is not possible due to conflicts, add a new one. -To define which image is to be used in a method or metric, simply set the `image` parameter in the method decorator to match the name of the folder containing the Dockerfile (e.g., `image="openproblems-r-base"`). +To define which image is to be used in a method or metric, simply set the `image` +parameter in the method decorator to match the name of the folder containing the +Dockerfile (e.g., `image="openproblems-r-base"`). ## Available images @@ -44,104 +58,149 @@ Our base R image. Do not add dependencies unless you know what you are doing. Our R image that accepts additional dependencies. -To add R packages (CRAN or Bioc), add them to `r_requirements.txt`. Syntax is dictated by [`renv`](https://rstudio.github.io/renv/reference/install.html#examples). +To add R packages (CRAN or Bioc), add them to `r_requirements.txt`. Syntax is dictated +by [`renv`](https://rstudio.github.io/renv/reference/install.html#examples). -To add Python packages (PyPi or Github), add them to `requirements.txt`. Syntax is dictated by [`pip`](https://packaging.python.org/tutorials/installing-packages/). +To add Python packages (PyPi or Github), add them to `requirements.txt`. Syntax is +dictated by [`pip`](https://packaging.python.org/tutorials/installing-packages/). ### openproblems-python-extras -To add Python packages (PyPi or Github), add them to `requirements.txt`. Syntax is dictated by [`pip`](https://packaging.python.org/tutorials/installing-packages/). +To add Python packages (PyPi or Github), add them to `requirements.txt`. Syntax is +dictated by [`pip`](https://packaging.python.org/tutorials/installing-packages/). ## Adding a package to the available images -Most packages should be able to be added in the Open Problems by editing one of the available images listed above. If there are conflicting dependencies between the package you would like to add and the packages already in the available images, follow the [Adding new images](#adding-new-images) steps below. +Most packages should be able to be added in the Open Problems by editing one of the +available images listed above. If there are conflicting dependencies between the package +you would like to add and the packages already in the available images, follow the +[Adding new images](#adding-new-images) steps below. -Assuming there are no conflicting dependencies, you can simply amend the relevant `requirements.txt` file in the directory for the Docker image you would like to edit. +Assuming there are no conflicting dependencies, you can simply amend the relevant +`requirements.txt` file in the directory for the Docker image you would like to edit. -1. Select a Docker image to edit. If you're adding a Python package, start with `openproblems-python-extras`. If you're adding an R package, start with the `openproblems-r-extras`. +1. Select a Docker image to edit. If you're adding a Python package, start with + `openproblems-python-extras`. If you're adding an R package, start with the + `openproblems-r-extras`. 2. Edit the relevant `requirements.txt` file. - * Adding an R package: - * Edit the `r_requirements.txt` file. - * The syntax to add a package is defined by [renv](https://rstudio.github.io/renv/reference/install.html#examples). - * Packages from Bioconductor: `bioc::packagename` - * Packages from CRAN: `packagename@` - * Packages from Git: `username/packagename` - * More complex package installation will require editing the `Dockerfile`. - * Adding a Python package: - * Edit the `requirements.txt` file. - * The syntax to add a package is defined by `pip` - * Packages from PyPI: `packagename==version` - * Packages from Git: `git+https://github.com/username/repositoryname` - * More complex package installation will require editing the `Dockerfile`. -3. Add the `packagename` to the `README.md` file in the directory specifying the Docker image. This helps keep track of which packages and versions are installed in each Docker image. -4. Commit your changes to the Docker image and push to your fork following the instructions in the [Contributing Guide](https://github.com/singlecellopenproblems/SingleCellOpenProblems/blob/master/CONTRIBUTING.md). + - Adding an R package: + - Edit the `r_requirements.txt` file. + - The syntax to add a package is defined by [renv + ](https://rstudio.github.io/renv/reference/install.html#examples). + - Packages from Bioconductor: `bioc::packagename` + - Packages from CRAN: `packagename@` + - Packages from Git: `username/packagename` + - More complex package installation will require editing the `Dockerfile`. + - Adding a Python package: + - Edit the `requirements.txt` file. + - The syntax to add a package is defined by `pip` + - Packages from PyPI: `packagename==version` + - Packages from Git: `git+https://github.com/username/repositoryname` + - More complex package installation will require editing the `Dockerfile`. +3. Add the `packagename` to the `README.md` file in the directory specifying the Docker + image. This helps keep track of which packages and versions are installed in each + Docker image. +4. Commit your changes to the Docker image and push to your fork following the + instructions in the [Contributing Guide + ](https://github.com/openproblems-bio/openproblems/blob/main/CONTRIBUTING.md). ## Adding new images To add a new image, create a new folder containing the following files: -* `Dockerfile` -* `README.md` -* `requirements.txt` (optional) -* `r_requirements.txt` (optional) +- `Dockerfile` +- `README.md` +- `requirements.txt` (optional) +- `r_requirements.txt` (optional) -The easiest way to do this is to copy the `openproblems-python-extras` or `openproblems-r-extras` folder. +The easiest way to do this is to copy the `openproblems-python-extras` or +`openproblems-r-extras` folder. ## Building Docker images locally -If you have Docker installed, you can build containers locally for prototyping. For example, to install the `openproblems` base container, you can run the following. -``` +If you have Docker installed, you can build containers locally for prototyping. For +example, to install the `openproblems` base container, you can run the following. + +```shell docker build -f docker/openproblems/Dockerfile -t singlecellopenproblems/openproblems . ``` + or to update all available Docker images: -``` + +```shell cd workflow && snakemake -j 10 docker ``` + or if you wish to override the automatic change detection, -``` + +```shell cd workflow && snakemake -j 10 docker_build ``` ## Building Docker images through GitHub Actions workflows -Docker images are built by the `run_benchmarks` GitHub Actions workflow on both the base repository and on forks. As long as you have AWS secrets configured properly for your repository (see our [Contributing Guide](https://github.com/singlecellopenproblems/SingleCellOpenProblems/blob/master/CONTRIBUTING.md#submitting-new-features)), these images will be uploaded to Amazon Web Services [Elastic Container Registry](https://aws.amazon.com/ecr/) (ECR). You can then download the image locally or attach to AWS SageMaker Studio. +Docker images are built by the `run_benchmarks` GitHub Actions workflow on both the base +repository and on forks. As long as you have AWS secrets configured properly for your +repository (see our [Contributing +Guide](https://github.com/openproblems-bio/openproblems/blob/main/CONTRIBUTING.md#submitting-new-features)), +these images will be uploaded to Amazon Web Services [Elastic Container +Registry](https://aws.amazon.com/ecr/) (ECR). You can then download the image locally or +attach to AWS SageMaker Studio. -Once your Run Benchmark has completed successfully, you should see a pane in the GitHub Actions tab of your fork that looks like this: +Once your Run Benchmark has completed successfully, you should see a pane in the GitHub +Actions tab of your fork that looks like this: -image +![Successful Actions run](https://user-images.githubusercontent.com/8322751/112719533-c508e100-8ecf-11eb-91b0-6f99ccee2e3f.png) If that workflow failed, you should look at the workflow logs to find the error. -You can find your successfully uploaded images on the ECR. To navigate to the ECR, search the AWS console for "ECR" and click on "Repositories" and then click on `openproblems`. You should also see a `nextflow` repository that's used for your benchmarking backend, but you can ignore that for now. +You can find your successfully uploaded images on the ECR. To navigate to the ECR, +search the AWS console for "ECR" and click on "Repositories" and then click on +`openproblems`. You should also see a `nextflow` repository that's used for your +benchmarking backend, but you can ignore that for now. -As you can see below, images uploaded to the ECR have Image Tags in the following format `openproblems:[first 6 characters of username]-[branch name]-[image name]`. For example, `danielStrobl` recently pushed his `batch-integration` branch containing a `openproblems-python37-scgen` image. This is converted to an Image Tag `daniel-batch-integration-openproblems-python37-scgen`. +As you can see below, images uploaded to the ECR have Image Tags in the following format +`openproblems:[first 6 characters of username]-[branch name]-[image name]`. For example, +`danielStrobl` recently pushed his `batch-integration` branch containing a +`openproblems-python37-scgen` image. This is converted to an Image Tag +`daniel-batch-integration-openproblems-python37-scgen`. - -Untitled +![ECR example screen](https://user-images.githubusercontent.com/8322751/112719414-43b14e80-8ecf-11eb-8fe2-5588e42c77c5.png) ## Pulling images from the ECR to your local machine -To pull images from the ECR using `docker pull`, first download and setup the [`amazon-ecr-credential-helper`](https://github.com/awslabs/amazon-ecr-credential-helper) using the same AWS secrets that you used to set up your fork repository. With that set up you can use the following command to pull the image: +To pull images from the ECR using `docker pull`, first download and setup the +[`amazon-ecr-credential-helper`](https://github.com/awslabs/amazon-ecr-credential-helper) +using the same AWS secrets that you used to set up your fork repository. With that set +up you can use the following command to pull the image: -``` +```shell docker pull .dkr.ecr.us-west-2.amazonaws.com/openproblems: ``` -If you would like to attach this image to AWS SageMaker, you can follow our [SageMaker and ECR tutorial.](https://github.com/singlecellopenproblems/SingleCellOpenProblems/blob/master/SAGEMAKER.md) +If you would like to attach this image to AWS SageMaker, you can follow our [SageMaker +and ECR +tutorial.](https://github.com/openproblems-bio/openproblems/blob/main/SAGEMAKER.md) -You can also pull base images from [DockerHub](https://hub.docker.com/r/singlecellopenproblems/openproblems): -``` +You can also pull base images from +[DockerHub](https://hub.docker.com/r/singlecellopenproblems/openproblems): + +```shell docker pull singlecellopenproblems/openproblems-python-extras:latest ``` - ## Running Docker images locally -To run Docker images on your local machine, you must have `docker` installed. Follow the Docker guide to [Install Docker](https://docs.docker.com/get-docker/). +To run Docker images on your local machine, you must have `docker` installed. Follow the +Docker guide to [Install Docker](https://docs.docker.com/get-docker/). -Once you've either built Docker images locally or pulled them from ECR or the [singlecellopenproblems DockerHub](https://hub.docker.com/r/singlecellopenproblems/openproblems), you can see installed images using `docker images`. +Once you've either built Docker images locally or pulled them from ECR or the +[singlecellopenproblems +DockerHub](https://hub.docker.com/r/singlecellopenproblems/openproblems), you can see +installed images using `docker images`. -``` + +```shell > docker images REPOSITORY TAG IMAGE ID CREATED SIZE singlecellopenproblems/openproblems-python-extras latest f86e1c5ce9d0 14 hours ago 3.94GB @@ -150,16 +209,20 @@ singlecellopenproblems/openproblems-r-extras latest singlecellopenproblems/openproblems latest 14974cbd2f58 5 days ago 2.1GB 490915662541.dkr.ecr.us-west-2.amazonaws.com/openproblems batch_integration_docker-openproblems 3a1ce37e85f2 6 days ago 2.06GB ``` + -You can then run commands within a docker container using `docker run`. Consult the [Docker documentation](https://docs.docker.com/engine/reference/commandline/run/) to learn more about the `run` command. +You can then run commands within a docker container using `docker run`. Consult the +[Docker documentation](https://docs.docker.com/engine/reference/commandline/run/) to +learn more about the `run` command. **Using `IMAGE ID`** -``` + +```shell docker run -it 90a9110c7d69 /bin/bash ``` **Using `RESPOSITORY:TAG`** -``` -docker run -it singlecellopenproblems/openproblems-python-extras:latest /bin/bash +```shell +docker run -it singlecellopenproblems/openproblems-python-extras:latest /bin/bash ``` diff --git a/docker/openproblems-github-actions/Dockerfile b/docker/openproblems-github-actions/Dockerfile index c0df3c9464..e5ce9085e1 100644 --- a/docker/openproblems-github-actions/Dockerfile +++ b/docker/openproblems-github-actions/Dockerfile @@ -4,22 +4,22 @@ USER root WORKDIR / # Install Docker -RUN sudo apt-get update -RUN sudo apt-get install -y ca-certificates curl gnupg lsb-release -RUN sh -c 'curl -fsSL https://download.docker.com/linux/ubuntu/gpg | sudo gpg --dearmor -o /usr/share/keyrings/docker-archive-keyring.gpg' +RUN apt-get update +RUN apt-get install -y ca-certificates curl gnupg lsb-release +RUN sh -c 'curl -fsSL https://download.docker.com/linux/ubuntu/gpg | gpg --dearmor -o /usr/share/keyrings/docker-archive-keyring.gpg' RUN sh -c 'echo \ "deb [arch=$(dpkg --print-architecture) signed-by=/usr/share/keyrings/docker-archive-keyring.gpg] https://download.docker.com/linux/debian \ - $(lsb_release -cs) stable" | sudo tee /etc/apt/sources.list.d/docker.list' -RUN sudo apt-get update -RUN sudo apt-get install -y docker-ce docker-ce-cli containerd.io + $(lsb_release -cs) stable" | tee /etc/apt/sources.list.d/docker.list' +RUN apt-get update +RUN apt-get install -y docker-ce docker-ce-cli containerd.io # install Python packages COPY ./docker/openproblems-github-actions/requirements.txt ./requirements.txt RUN pip install --no-cache-dir -r requirements.txt # Install R packages -COPY ./.pre-commit.r_requirements.txt ./r_requirements.txt -RUN sudo R -e "renv::install(scan('r_requirements.txt', sep='\n', what=character()))" +COPY ./docker/openproblems-github-actions/r_requirements.txt ./r_requirements.txt +RUN R -e "source(\"install_renv.R\"); install_renv(\"r_requirements.txt\")" # Install Python packages -RUN sudo pip install --no-cache-dir -U /usr/src/singlecellopenproblems[test,r,evaluate] +RUN pip install --no-cache-dir -U /usr/src/singlecellopenproblems[test,r,evaluate] diff --git a/docker/openproblems-github-actions/r_requirements.txt b/docker/openproblems-github-actions/r_requirements.txt new file mode 100644 index 0000000000..7693ca4226 --- /dev/null +++ b/docker/openproblems-github-actions/r_requirements.txt @@ -0,0 +1,6 @@ +backports@1.4.1 +docopt@0.7.1 +git2r@0.30.1 +lintr@3.0.1 +styler@1.7.0 +tibble@3.1.8 diff --git a/docker/openproblems-python-batch-integration/requirements.txt b/docker/openproblems-python-batch-integration/requirements.txt index 31d3329111..6f1fe411b3 100644 --- a/docker/openproblems-python-batch-integration/requirements.txt +++ b/docker/openproblems-python-batch-integration/requirements.txt @@ -1,6 +1,6 @@ -annoy==1.16.3 +annoy==1.17.1 bbknn==1.5.* -git+https://github.com/pinin4fjords/mnnpy@6c0b2c0 # branch: fix_for_numba_jit +git+https://github.com/scottgigante-immunai/mnnpy@eb4c551 # branch: patch-2 git+https://github.com/theislab/scib@a35e300 -scanorama==1.7.* +scanorama==1.7.0 scvi-tools~=0.16 # pinned in #313 diff --git a/docker/openproblems-python-extras/README.md b/docker/openproblems-python-extras/README.md index beaef1b28a..7e060243fc 100644 --- a/docker/openproblems-python-extras/README.md +++ b/docker/openproblems-python-extras/README.md @@ -15,9 +15,13 @@ Python packages: * molecular-cross-validation * MulticoreTSNE * phate +* scvi-tools +* cell2location * neuralee +* tangram-sc * torch * tensorflow-cpu * ivis * umap-learn * xgboost +* knn_smooth diff --git a/docker/openproblems-python-extras/requirements.txt b/docker/openproblems-python-extras/requirements.txt index 35efa114c5..71b8137fa1 100644 --- a/docker/openproblems-python-extras/requirements.txt +++ b/docker/openproblems-python-extras/requirements.txt @@ -1,12 +1,16 @@ -cmake==3.18.4 # pinned for #327 +cmake==3.24.1.1 +git+https://github.com/BayraktarLab/cell2location.git@7e7aa231cc61ff460da14402fa3b9a1fa3ec69ac git+https://github.com/czbiohub/molecular-cross-validation@04d9df0 +git+https://github.com/jorvis/Multicore-TSNE@6832575 git+https://github.com/KrishnaswamyLab/harmonic-alignment@v0.1#subdirectory=python git+https://github.com/michalk8/neuralee@8946abf # contains gradient error fix +git+https://github.com/scottgigante-immunai/knn-smoothing@python_package magic-impute==3.0.* -MulticoreTSNE==0.1 phate==1.0.* pybedtools==0.9.* pyensembl==2.0.* -tensorflow-cpu==2.7.* -torch==1.11.* +scvi-tools==0.16.* +tangram-sc==1.0.* +tensorflow-cpu==2.9.* +torch==1.12.* xgboost==1.6.* diff --git a/docker/openproblems-python-scvi/requirements.txt b/docker/openproblems-python-scvi/requirements.txt index 667996a187..cd95e5cd0d 100644 --- a/docker/openproblems-python-scvi/requirements.txt +++ b/docker/openproblems-python-scvi/requirements.txt @@ -1,5 +1,5 @@ -jax==0.3.6 # pinned in #313 -jaxlib==0.3.5 # pinned in #313 +jax==0.3.20 +jaxlib==0.3.20 scikit-misc==0.1.* -scvi-tools~=0.16 # pinned in #313 +scvi-tools~=0.17 # pinned in #313 xgboost==1.6.* diff --git a/docker/openproblems-python-tf2.4/README.md b/docker/openproblems-python-tf2.4/README.md index 2704835345..f08a69ea20 100644 --- a/docker/openproblems-python-tf2.4/README.md +++ b/docker/openproblems-python-tf2.4/README.md @@ -8,7 +8,6 @@ Python: 3.8 Python packages: - * keras >=2.4,<2.6 * tensorflow >=2.4,<2.5 * dca diff --git a/docker/openproblems-python-tf2.4/requirements.txt b/docker/openproblems-python-tf2.4/requirements.txt index de16462e13..9dedbff275 100644 --- a/docker/openproblems-python-tf2.4/requirements.txt +++ b/docker/openproblems-python-tf2.4/requirements.txt @@ -1,4 +1,4 @@ dca==0.3.* keras>=2.4,<2.6 # pinned in dca -pyyaml==5.4.1 # pinned in #431 +pyyaml==6.0 # pinned in #431 tensorflow-cpu==2.4.* # pinned in dca diff --git a/docker/openproblems-r-base/Dockerfile b/docker/openproblems-r-base/Dockerfile index 0309db7a4e..5c70677019 100644 --- a/docker/openproblems-r-base/Dockerfile +++ b/docker/openproblems-r-base/Dockerfile @@ -30,8 +30,9 @@ ENV R_HOME=/usr/lib/R # Install R packages RUN R -e "install.packages('renv'); renv::consent(TRUE)" +COPY ./scripts/install_renv.R ./ COPY ./docker/openproblems-r-base/r_requirements.txt ./r_requirements.txt -RUN R -e "renv::install(scan('r_requirements.txt', sep='\n', what=character()))" +RUN R -e "source(\"install_renv.R\"); install_renv(\"r_requirements.txt\")" # Install kernelspec RUN R -e "IRkernel::installspec(user = FALSE);" diff --git a/docker/openproblems-r-base/r_requirements.txt b/docker/openproblems-r-base/r_requirements.txt index 3ec3d6f7af..1b94692a0c 100644 --- a/docker/openproblems-r-base/r_requirements.txt +++ b/docker/openproblems-r-base/r_requirements.txt @@ -1,3 +1,3 @@ -bioc::scran@1.24.0 +bioc::scran@1.24.1 IRkernel@1.3 RcppAnnoy@0.0.19 diff --git a/docker/openproblems-r-extras/Dockerfile b/docker/openproblems-r-extras/Dockerfile index 338a0ff012..e67fe8eb09 100644 --- a/docker/openproblems-r-extras/Dockerfile +++ b/docker/openproblems-r-extras/Dockerfile @@ -8,7 +8,9 @@ ARG NB_GID="100" USER root WORKDIR / -RUN apt-get update && apt-get install -y libhdf5-dev hdf5-tools libgeos-dev +RUN apt-get update && apt-get install -y \ + libhdf5-dev hdf5-tools libgeos-dev \ + libharfbuzz-dev libfribidi-dev RUN apt-get clean autoclean && \ apt-get autoremove --yes && \ @@ -17,13 +19,14 @@ RUN apt-get clean autoclean && \ # Install R packages COPY ./docker/openproblems-r-extras/r_requirements.txt ./r_requirements.txt -RUN \ - R -e "renv::install(scan('r_requirements.txt', sep='\n', what=character()))" +RUN R -e "source(\"install_renv.R\"); install_renv(\"r_requirements.txt\")" # Install Python packages COPY ./docker/openproblems-r-extras/requirements.txt ./requirements.txt RUN pip install --no-cache-dir -r requirements.txt +# Fix permissions +RUN chown -R $NB_USER:$NB_GID /home/$NB_USER USER $NB_UID WORKDIR /home/$NB_USER diff --git a/docker/openproblems-r-extras/README.md b/docker/openproblems-r-extras/README.md index 2e0420d7e4..b5cf1e1e75 100644 --- a/docker/openproblems-r-extras/README.md +++ b/docker/openproblems-r-extras/README.md @@ -13,6 +13,8 @@ R packages: * batchelor * sparsesvd * dplyr +* spacexr +* Seurat Python packages: diff --git a/docker/openproblems-r-extras/r_requirements.txt b/docker/openproblems-r-extras/r_requirements.txt index 560aabc12d..ebfd924607 100644 --- a/docker/openproblems-r-extras/r_requirements.txt +++ b/docker/openproblems-r-extras/r_requirements.txt @@ -1,35 +1,54 @@ bioc::batchelor@1.12.3 -bioc::ComplexHeatmap@2.12.0 -caret@6.0-92 -cli@3.3.0 -conos@1.4.6 -crayon@1.5.1 -dbplyr@2.1.1 -devtools@2.3.2 -dplyr@1.0.9 +bioc::ComplexHeatmap@2.12.1 +bioc::scater@1.24.0 +bioc::scran@1.24.1 +bioc::scuttle@1.6.3 +bslib@0.4.0 +caret@6.0-93 +cli@3.4.1 +conos@1.4.9 +crayon@1.5.2 +dbplyr@2.2.1 +devtools@2.4.5 +dmcable/spacexr@eeb02a2 # master +downlit@0.4.2 +dplyr@1.0.10 e1071@1.7-11 ellipsis@0.3.2 -forecast@8.14 +forecast@8.17.0 hardhat@1.1.0 +here@1.0.1 hexbin@1.28.2 -htmltools@0.4.0 -htmlwidgets@1.5.3 -igraph@1.2.11 -lifecycle@1.0.1 -Matrix@1.4-1 -pkgload@1.2.4 +htmltools@0.5.3 +htmlwidgets@1.5.4 +igraph@1.3.5 +lifecycle@1.0.2 +Matrix@1.5-1 +pkgdown@2.0.6 +pkgload@1.3.0 +profvis@0.3.7 proxy@0.4-27 -Rcpp@1.0.7 -reticulate@1.18 -rlang@1.0.2 +ragg@1.2.2 +Rcpp@1.0.9 +RcppTOML@0.1.7 +reticulate@1.26 +rlang@1.0.5 rliger@1.0.0 rmarkdown@2.2 RSQLite@2.2.4 +saezlab/liana@0.1.6 +saezlab/OmnipathR@679bb79 # master +sass@0.4.2 +sctransform@0.3.4 Seurat@4.1.1 +SeuratObject@4.1.1 shiny@1.4.0.2 sparsesvd@0.2 +systemfonts@1.0.4 +textshaping@0.3.6 tibble@3.1.7 tidymodels@0.1.2 tidyverse@1.3.0 tune@0.2.0 +urlchecker@1.0.1 welch-lab/liger@v0.5.0 diff --git a/docker/openproblems-r-pytorch/Dockerfile b/docker/openproblems-r-pytorch/Dockerfile index 86289d9289..d8300572f7 100644 --- a/docker/openproblems-r-pytorch/Dockerfile +++ b/docker/openproblems-r-pytorch/Dockerfile @@ -10,8 +10,7 @@ WORKDIR / # Install R packages COPY ./docker/openproblems-r-pytorch/r_requirements.txt ./r_requirements.txt -RUN \ - R -e "renv::install(scan('r_requirements.txt', sep='\n', what=character()))" +RUN R -e "source(\"install_renv.R\"); install_renv(\"r_requirements.txt\")" # Install Python packages COPY ./docker/openproblems-r-pytorch/requirements.txt ./requirements.txt diff --git a/docker/openproblems-r-pytorch/requirements.txt b/docker/openproblems-r-pytorch/requirements.txt index bc7fb8d14d..7121777e82 100644 --- a/docker/openproblems-r-pytorch/requirements.txt +++ b/docker/openproblems-r-pytorch/requirements.txt @@ -1,3 +1,3 @@ git+https://github.com/theislab/scib@v1.0.2 harmony-pytorch==0.1.* -torch==1.11.* +torch==1.12.* diff --git a/docker/openproblems/requirements.txt b/docker/openproblems/requirements.txt index 4f9f724244..709868274c 100644 --- a/docker/openproblems/requirements.txt +++ b/docker/openproblems/requirements.txt @@ -1,7 +1,7 @@ boto3==1.24.* -cmake==3.22.* +cmake==3.22.* # pinned in #607 jupyter==1.0.* pip -sagemaker==2.94.* +sagemaker==2.112.* setuptools wheel diff --git a/openproblems/api/README.md b/openproblems/api/README.md index 388311f624..69e9dd5284 100644 --- a/openproblems/api/README.md +++ b/openproblems/api/README.md @@ -1,10 +1,11 @@ # openproblems-cli -This CLI provides an interface to the `openproblems` Python package from the command line, primarily for the testing and evaluation workflow. +This CLI provides an interface to the `openproblems` Python package from the command +line, primarily for the testing and evaluation workflow. ## Usage -``` +```text usage: openproblems-cli [-h] [--parallel] {tasks,list,image,load,run,evaluate} ... @@ -27,10 +28,14 @@ optional arguments: ``` ## Example (without docker) -Running the CLI requires commands to be run in a specific order: `load` -> `run` -> `evaluate`. + +Running the CLI requires commands to be run in a specific order: `load` -> `run` -> +`evaluate`. For example: -``` + + +```shell # Download a task-specific dataset and save it to `dataset.h5ad` openproblems-cli load --task label_projection --output dataset.h5ad pancreas_batch # Run a method on a datasets and save output to `method.h5ad` @@ -38,9 +43,11 @@ openproblems-cli run --task label_projection --input dataset.h5ad --output metho # Evaluate the performance of a previously run method using the `accuracy` metric openproblems-cli evaluate --task label_projection --input method.h5ad accuracy ``` + You can list available tasks using `openproblems-cli tasks` -``` + +```shell > openproblems-cli tasks denoising dimensionality_reduction @@ -49,8 +56,10 @@ multimodal_data_integration regulatory_effect_prediction ``` -You can then list the avaiable datasets, methods, and metrics for a partiular task using `openproblems-cli list --[datasets|methods|metrics] --task [task_name]` -``` +You can then list the avaiable datasets, methods, and metrics for a partiular task using +`openproblems-cli list --[datasets|methods|metrics] --task [task_name]` + +```shell > openproblems-cli list --datasets --task label_projection pancreas_batch pancreas_random @@ -71,11 +80,13 @@ f1 f1_micro ``` -The output of these commands are allowable arguments to the respective `load`, `run`, and `evaluate` commands. +The output of these commands are allowable arguments to the respective `load`, `run`, +and `evaluate` commands. ### Sample output -``` + +```shell $ openproblems-cli tasks chromatin_potential denoising @@ -98,10 +109,12 @@ $ openproblems-cli list --metrics --task label_projection $ openproblems-cli evaluate --task label_projection --input method.h5ad accuracy 0.9521233432512848 ``` + ## Example (with docker) -``` + +```shell openproblems-cli tasks openproblems-cli list --datasets --task label_projection openproblems-cli image --datasets --task label_projection pancreas_batch @@ -113,10 +126,12 @@ openproblems-cli list --metrics --task label_projection openproblems-cli image --metrics --task label_projection accuracy openproblems-cli evaluate --task label_projection --input method.h5ad accuracy ``` + -### Sample output +### Sample output (with docker) -``` + +```shell $ openproblems-cli tasks chromatin_potential denoising @@ -148,3 +163,4 @@ openproblems $ docker run -dt singlecellopenproblems/openproblems openproblems-cli evaluate --task label_projection --input method.h5ad accuracy 0.9521233432512848 ``` + diff --git a/openproblems/api/evaluate.py b/openproblems/api/evaluate.py index c1ff58d2ab..7d3a550061 100644 --- a/openproblems/api/evaluate.py +++ b/openproblems/api/evaluate.py @@ -13,4 +13,8 @@ def main(args): """Run the ``evaluate`` subcommand.""" adata = anndata.read_h5ad(args.input) result = evaluate_metric(adata, args.task, args.name) - return result + if args.output is not None: + with open(args.output, "w") as handle: + handle.write(str(result)) + else: + return result diff --git a/openproblems/api/hash.py b/openproblems/api/hash.py index 57f3239a47..89eac0d8fa 100644 --- a/openproblems/api/hash.py +++ b/openproblems/api/hash.py @@ -1,8 +1,8 @@ -from ..tools.utils import check_version from . import utils import hashlib import importlib +import json import os import scprep import subprocess @@ -10,6 +10,19 @@ _MODULE = type(os) +def _run(args, **kwargs): + p = subprocess.run( + args, + stdout=subprocess.PIPE, + stderr=subprocess.PIPE, + **kwargs, + ) + if p.returncode != 0: + raise RuntimeError(p.stderr.decode()) + else: + return p.stdout.decode() + + def get_module(fun): """Get the Python module in which a function is defined.""" if hasattr(fun, "__wrapped__"): @@ -19,16 +32,57 @@ def get_module(fun): def git_hash(file): """Get the git commit hash associated with a file.""" - p = subprocess.run( + return _run( ["git", "log", "-n", "1", "--pretty=format:%H", "--", file], - stdout=subprocess.PIPE, - stderr=subprocess.PIPE, cwd=os.path.dirname(__file__), ) - if p.returncode != 0: - raise RuntimeError(p.stderr.decode()) - else: - return p.stdout.decode() + + +def docker_token(image_name): + output = json.loads( + _run( + [ + "curl", + "--silent", + f"https://auth.docker.io/token?scope=repository:{image_name}:" + "pull&service=registry.docker.io", + ] + ) + ) + return output["token"] + + +def docker_labels_from_api(image_name, tag="latest"): + token = docker_token(image_name) + output = json.loads( + _run( + [ + "curl", + "--silent", + "--header", + f"Authorization: Bearer {token}", + f"https://registry-1.docker.io/v2/{image_name}/manifests/{tag}", + ] + ) + ) + v1_compat_output = json.loads(output["history"][0]["v1Compatibility"]) + return v1_compat_output["config"]["Labels"] + + +def docker_hash(image_name): + """Get the docker image hash associated with an image.""" + try: + return _run( + [ + "docker", + "inspect", + "-f='{{ index .Config.Labels \"bio.openproblems.hash\"}}'", + image_name, + ] + ) + except (RuntimeError, FileNotFoundError): # pragma: nocover + # docker is unavailable or the image is not locally available; use the API + return docker_labels_from_api(image_name)["bio.openproblems.hash"] def get_context(obj, context=None): @@ -51,25 +105,19 @@ def get_context(obj, context=None): if isinstance(obj, _MODULE): module_name = obj.__name__ else: - if isinstance(obj, scprep.run.RFunction): - try: + if isinstance(obj, scprep.run.RFunction) and hasattr(obj, "__r_file__"): + if obj.__r_file__ not in context: context[obj.__r_file__] = git_hash(obj.__r_file__) - except AttributeError: - pass + if hasattr(obj, "metadata") and "image" in obj.metadata: + image_name = f"singlecellopenproblems/{obj.metadata['image']}" + if image_name not in context: + context[image_name] = docker_hash(image_name) try: module_name = get_module(obj) except AttributeError: # doesn't belong to a module return context - if module_name in context: - # already done - pass - elif not module_name.startswith("openproblems"): - # use version number of external libs - version = check_version(module_name.split(".")[0]) - if version != "ModuleNotFound": - context[module_name] = version - else: + if module_name.startswith("openproblems") and module_name not in context: module = importlib.import_module(module_name) context[module_name] = git_hash(module.__file__) for member_name in dir(module): diff --git a/openproblems/api/main.py b/openproblems/api/main.py index 5790e95adc..6ee11637b5 100644 --- a/openproblems/api/main.py +++ b/openproblems/api/main.py @@ -35,8 +35,7 @@ def _main(args=None): elif args.subcommand in SUBCOMMANDS: # Since printing the output to stdout is important here, # we redirect all other stdout to stderr. - with utils.RedirectStdout(): - return SUBCOMMANDS[args.subcommand].main(args) + return SUBCOMMANDS[args.subcommand].main(args) else: raise NotImplementedError diff --git a/openproblems/api/parser.py b/openproblems/api/parser.py index 7d6b633928..2ef917c9cc 100644 --- a/openproblems/api/parser.py +++ b/openproblems/api/parser.py @@ -4,12 +4,14 @@ def filepath(string): """Require an argument type to be a valid path.""" + if string is None: + return string path = pathlib.Path(string) if path.exists and path.is_dir(): raise argparse.ArgumentTypeError("Value must not be a directory") if not path.parent.is_dir(): try: - path.parent.mkdir(exists_ok=True, parents=True) + path.parent.mkdir(exist_ok=True, parents=True) except OSError as e: raise argparse.ArgumentTypeError( "Failed to create directory {}: {}".format(path.parent, e) @@ -55,13 +57,13 @@ def parse_input(parser): ) -def parse_output(parser): +def parse_output(parser, required=True): """Parse the output file argument.""" parser.add_argument( "--output", "-o", type=filepath, - required=True, + required=required, help="Output file path", ) @@ -147,6 +149,12 @@ def create_run_parser(subparsers): ) parse_input(parser) parse_output(parser) + parser.add_argument( + "--version-file", + type=str, + help="Write method version to a file", + default=None, + ) parser.add_argument("name", type=str, help="Name of the selected method") parse_test(parser) @@ -162,6 +170,7 @@ def create_evaluate_parser(subparsers): required=True, ) parse_input(parser) + parse_output(parser, required=False) parser.add_argument("name", type=str, help="Name of the selected metric") diff --git a/openproblems/api/run.py b/openproblems/api/run.py index 736426fcd1..77f4d9dcb9 100644 --- a/openproblems/api/run.py +++ b/openproblems/api/run.py @@ -24,4 +24,6 @@ def main(args): adata = anndata.read_h5ad(args.input) adata = run_method(adata, args.task, args.name, args.test) utils.write_h5ad(adata, args.output) - return adata.uns["method_code_version"] + if args.version_file is not None: + with open(args.version_file, "w") as handle: + handle.write(adata.uns["method_code_version"]) diff --git a/openproblems/api/utils.py b/openproblems/api/utils.py index cdd28476d0..a6bc3c4222 100644 --- a/openproblems/api/utils.py +++ b/openproblems/api/utils.py @@ -1,24 +1,9 @@ import openproblems import os -import sys -class RedirectStdout(object): - def __init__(self): - self._stdout = sys.stdout - self._stderr = sys.stderr - - def _flush(self): - self._stdout.flush() - self._stderr.flush() - - def __enter__(self): - self._flush() - sys.stdout = self._stderr - - def __exit__(self, exc_type, exc_value, traceback): - self._flush() - sys.stdout = self._stdout +class NoSuchFunctionError(RuntimeError): + pass def module_to_str(module): @@ -49,7 +34,7 @@ def get_function(task_name, function_type, function_name): fun = getattr(functions, function_name) assert callable(fun) except (AssertionError, AttributeError): - raise RuntimeError( + raise NoSuchFunctionError( "Task {} has no {} '{}'".format( task_name, function_type[:-1], function_name ) diff --git a/openproblems/data/Wagner_2018_zebrafish_embryo_CRISPR.py b/openproblems/data/Wagner_2018_zebrafish_embryo_CRISPR.py index 55f2c2c78f..ef7d56a343 100644 --- a/openproblems/data/Wagner_2018_zebrafish_embryo_CRISPR.py +++ b/openproblems/data/Wagner_2018_zebrafish_embryo_CRISPR.py @@ -4,7 +4,10 @@ import scprep -@utils.loader(data_url="https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE112294") +@utils.loader( + data_url="https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE112294", + data_reference="https://doi.org/10.1126/science.aar4362", +) def load_zebrafish_chd_tyr(test=False): """Download zebrafish data from GEO accession GSE112294""" diff --git a/openproblems/data/allen_brain_atlas.py b/openproblems/data/allen_brain_atlas.py new file mode 100644 index 0000000000..66412172ee --- /dev/null +++ b/openproblems/data/allen_brain_atlas.py @@ -0,0 +1,52 @@ +from . import utils + +import numpy as np +import os +import scanpy as sc +import scprep +import tempfile + +URL = "https://figshare.com/ndownloader/files/36509385" + + +@utils.loader(data_url=URL, data_reference="https://doi.org/10.1038/nn.4216") +def load_mouse_brain_atlas(test=False): + """Download Allen Brain (Taisc et al.,2016) data from Figshare. + + Further information how we generated the assumed truth and the reference + to the dataset is available at: + https://figshare.com/articles/dataset/allen_brain_h5ad/20338089 + """ + if test: + # load full data first, cached if available + adata = load_mouse_brain_atlas(test=False) + + # Subsample data to random cell from each cell type + msk = adata.obs_names.isin(adata.obs.groupby("label").head(20).index) + adata = adata[msk, :] + + # Basic filtering + utils.filter_genes_cells(adata) + + # Keep only 500 genes + adata = adata[:, 30000:30500].copy() + + # remove missing labels for tests + labels = adata.obs["label"].cat.categories + source_mask = ~adata.uns["ccc_target"]["target"].isin(labels) + target_mask = ~adata.uns["ccc_target"]["target"].isin(labels) + msk = np.logical_not(source_mask | target_mask) + adata.uns["ccc_target"] = adata.uns["ccc_target"][msk] + + return adata + + else: + with tempfile.TemporaryDirectory() as tempdir: + filepath = os.path.join(tempdir, "mouse_brain_atlas.h5ad") + scprep.io.download.download_url(URL, filepath) + adata = sc.read(filepath) + + # Ensure there are no cells or genes with 0 counts + utils.filter_genes_cells(adata) + + return adata diff --git a/openproblems/data/cengen.py b/openproblems/data/cengen.py index 6113810cb2..e47f312fa4 100644 --- a/openproblems/data/cengen.py +++ b/openproblems/data/cengen.py @@ -5,13 +5,16 @@ import scprep import tempfile +# TODO(@LuckyMD): document relevant link at figshare.com/articles/* URL = ( "https://github.com/Munfred/wormcells-data/" "releases/download/taylor2020/taylor2020.h5ad" ) -@utils.loader(data_url=URL) +@utils.loader( + data_url=URL, data_reference="https://doi.org/10.1016/j.neuron.2018.07.042" +) def load_cengen(test=False): """Download CeNGEN data from GitHub. diff --git a/openproblems/data/immune_cells.py b/openproblems/data/immune_cells.py index 2ebcc8cbf4..abed1d0a28 100644 --- a/openproblems/data/immune_cells.py +++ b/openproblems/data/immune_cells.py @@ -5,10 +5,11 @@ import scprep import tempfile -URL = "https://ndownloader.figshare.com/files/25717328" +# sparsified from https://figshare.com/articles/dataset/Benchmarking_atlas-level_data_integration_in_single-cell_genomics_-_integration_task_datasets_Immune_and_pancreas_/12420968/2 # noqa: E501 +URL = "https://ndownloader.figshare.com/files/36086786" -@utils.loader(data_url=URL) +@utils.loader(data_url=URL, data_reference="https://doi.org/10.1038/s41592-021-01336-8") def load_immune(test=False): """Download immune human data from figshare.""" if test: @@ -33,9 +34,8 @@ def load_immune(test=False): scprep.io.download.download_url(URL, filepath) adata = sc.read(filepath) - # Note: anndata.X contains scran log-normalized data, - # so we're storing it in layers['log_scran'] - adata.layers["log_scran"] = adata.X + # NOTE: adata.X contains log-normalized data, so we're moving it + adata.layers["log_normalized"] = adata.X adata.X = adata.layers["counts"] del adata.layers["counts"] diff --git a/openproblems/data/mouse_blood_olssen_labelled.py b/openproblems/data/mouse_blood_olssen_labelled.py index 5a8b4c7546..d04ecb353f 100644 --- a/openproblems/data/mouse_blood_olssen_labelled.py +++ b/openproblems/data/mouse_blood_olssen_labelled.py @@ -5,17 +5,19 @@ import scprep import tempfile -URL = "https://ndownloader.figshare.com/files/27346712" +# sparsified from https://ndownloader.figshare.com/files/27346712 +# TODO(@LuckyMD): change link to figshare.com/articles/* +URL = "https://figshare.com/ndownloader/files/36872214" -@utils.loader(data_url=URL) +@utils.loader(data_url=URL, data_reference="https://doi.org/10.1038/nature19348") def load_olsson_2016_mouse_blood(test=False): """Download Olsson, 2016_mouse_blood, Nature, 2016 data from Figshare.""" if test: # load full data first, cached if available adata = load_olsson_2016_mouse_blood(test=False) - # Select 700 genes expressed on most cells + # Select 700 genes expressed in most cells sc.pp.calculate_qc_metrics(adata, inplace=True) top = list(adata.var.nlargest(700, "mean_counts").index.values) adata = adata[:, top].copy() diff --git a/openproblems/data/human_blood_nestorowa2016.py b/openproblems/data/mouse_hspc_nestorowa2016.py similarity index 70% rename from openproblems/data/human_blood_nestorowa2016.py rename to openproblems/data/mouse_hspc_nestorowa2016.py index 4a2679d342..81218ef9f0 100644 --- a/openproblems/data/human_blood_nestorowa2016.py +++ b/openproblems/data/mouse_hspc_nestorowa2016.py @@ -5,15 +5,19 @@ import scprep import tempfile -URL = "https://ndownloader.figshare.com/files/25555751" +# sparsified from https://ndownloader.figshare.com/files/25555751 +# TODO(@LuckyMD): change link to figshare.com/articles/* +URL = "https://ndownloader.figshare.com/files/36088649" -@utils.loader(data_url=URL) -def load_human_blood_nestorowa2016(test=False): +@utils.loader( + data_url=URL, data_reference="https://doi.org/10.1182/blood-2016-05-716480" +) +def load_mouse_hspc_nestorowa2016(test=False): """Download Nesterova data from Figshare.""" if test: # load full data first, cached if available - adata = load_human_blood_nestorowa2016(test=False) + adata = load_mouse_hspc_nestorowa2016(test=False) # Subsample data adata = adata[:, :500].copy() diff --git a/openproblems/data/multimodal/citeseq.py b/openproblems/data/multimodal/citeseq.py index 64300d66e9..7bf3a509c6 100644 --- a/openproblems/data/multimodal/citeseq.py +++ b/openproblems/data/multimodal/citeseq.py @@ -15,7 +15,10 @@ ) -@loader(data_url="https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE100866") +@loader( + data_url="https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE100866", + data_reference="https://doi.org/10.1038/nmeth.4380", +) def load_citeseq_cbmc(test=False): """Download CITEseq data from GEO.""" if test: diff --git a/openproblems/data/multimodal/sample.py b/openproblems/data/multimodal/sample.py index 506d971081..3348b038b9 100644 --- a/openproblems/data/multimodal/sample.py +++ b/openproblems/data/multimodal/sample.py @@ -11,7 +11,10 @@ import scipy.sparse -@loader(data_url="https://openproblems.bio") +@loader( + data_url="https://openproblems.bio", + data_reference="https://github.com/openproblems-bio/openproblems", +) def load_sample_data(test=True): """Create a simple dataset to use for testing in multimodal applications.""" assert test diff --git a/openproblems/data/multimodal/scicar/base.py b/openproblems/data/multimodal/scicar/base.py index f20b903e45..4ff018f649 100644 --- a/openproblems/data/multimodal/scicar/base.py +++ b/openproblems/data/multimodal/scicar/base.py @@ -6,6 +6,8 @@ import scprep import tempfile +DATA_REFERENCE = "https://doi.org/10.1126/science.aau0730" + def load_scicar( rna_url, diff --git a/openproblems/data/multimodal/scicar/cell_lines.py b/openproblems/data/multimodal/scicar/cell_lines.py index afb9ef19b4..419bfd825f 100644 --- a/openproblems/data/multimodal/scicar/cell_lines.py +++ b/openproblems/data/multimodal/scicar/cell_lines.py @@ -1,5 +1,6 @@ from ...utils import loader from ..utils import subset_joint_data +from .base import DATA_REFERENCE from .base import load_scicar rna_url = ( @@ -28,7 +29,10 @@ ) -@loader(data_url="https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE117089") +@loader( + data_url="https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE117089", + data_reference=DATA_REFERENCE, +) def load_scicar_cell_lines(test=False): """Download sci-CAR cell lines data from GEO.""" if test: diff --git a/openproblems/data/multimodal/scicar/mouse_kidney.py b/openproblems/data/multimodal/scicar/mouse_kidney.py index d633d0ce4a..409ef3e476 100644 --- a/openproblems/data/multimodal/scicar/mouse_kidney.py +++ b/openproblems/data/multimodal/scicar/mouse_kidney.py @@ -1,5 +1,6 @@ from ...utils import loader from ..utils import subset_joint_data +from .base import DATA_REFERENCE from .base import load_scicar rna_url = ( @@ -28,7 +29,10 @@ ) -@loader(data_url="https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE117089") +@loader( + data_url="https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE117089", + data_reference=DATA_REFERENCE, +) def load_scicar_mouse_kidney(test=False): """Download sci-CAR mouse kidney data from GEO.""" if test: diff --git a/openproblems/data/pancreas.py b/openproblems/data/pancreas.py index bd67f59277..91288961ee 100644 --- a/openproblems/data/pancreas.py +++ b/openproblems/data/pancreas.py @@ -1,23 +1,22 @@ from . import utils +import anndata as ad +import numpy as np import os import scanpy as sc import scprep import tempfile -URL = "https://ndownloader.figshare.com/files/24539828" +# sparsified from https://figshare.com/articles/dataset/Benchmarking_atlas-level_data_integration_in_single-cell_genomics_-_integration_task_datasets_Immune_and_pancreas_/12420968/2 # noqa: E501 +URL = "https://ndownloader.figshare.com/files/36086813" -@utils.loader(data_url=URL) -def load_pancreas(test=False): +@utils.loader(data_url=URL, data_reference="https://doi.org/10.1038/s41592-021-01336-8") +def load_pancreas(test=False, integer_only=False): """Download pancreas data from figshare.""" if test: # load full data first, cached if available - adata = load_pancreas(test=False) - - # Subsample pancreas data - adata = adata[:, :500].copy() - utils.filter_genes_cells(adata) + adata = load_pancreas(test=False, integer_only=integer_only) keep_celltypes = adata.obs["celltype"].dtype.categories[[0, 3]] keep_techs = adata.obs["tech"].dtype.categories[[0, -3, -2]] @@ -25,25 +24,64 @@ def load_pancreas(test=False): keep_celltype_idx = adata.obs["celltype"].isin(keep_celltypes) adata = adata[keep_tech_idx & keep_celltype_idx].copy() - sc.pp.subsample(adata, n_obs=500) + # Subsample pancreas data + adata = adata[:, :500].copy() # Note: could also use 200-500 HVGs rather than 200 random genes + utils.filter_genes_cells(adata) + + # select 250 cells from each celltype + keep_cell_idx = np.concatenate( + [ + np.random.choice( + np.argwhere(adata.obs["celltype"].to_numpy() == ct).flatten(), + min(250, np.sum(adata.obs["celltype"].to_numpy() == ct)), + replace=False, + ) + for ct in keep_celltypes + ] + ) + adata = adata[adata.obs.index[keep_cell_idx]] # Ensure there are no cells or genes with 0 counts utils.filter_genes_cells(adata) return adata - else: - with tempfile.TemporaryDirectory() as tempdir: - filepath = os.path.join(tempdir, "pancreas.h5ad") - scprep.io.download.download_url(URL, filepath) - adata = sc.read(filepath) + with tempfile.TemporaryDirectory() as tempdir: + filepath = os.path.join(tempdir, "pancreas.h5ad") + scprep.io.download.download_url(URL, filepath) + adata = sc.read(filepath) - # NOTE: X contains counts that are normalized with scran - adata.layers["log_scran"] = adata.X - adata.X = adata.layers["counts"] - del adata.layers["counts"] - # Ensure there are no cells or genes with 0 counts - utils.filter_genes_cells(adata) + # NOTE: adata.X contains log-normalized data, so we're moving it + adata.layers["log_normalized"] = adata.X + adata.X = adata.layers["counts"] + del adata.layers["counts"] - return adata + if integer_only: + adata = _get_pancreas_integer(adata) + + # Ensure there are no cells or genes with 0 counts + utils.filter_genes_cells(adata) + + return adata + + +def _get_pancreas_integer(adata: ad.AnnData): + """Transform counts to integer. + + For some platforms the pancreas data set only have processed counts. + Here we grab those with integer counts. + See https://github.com/theislab/scib-reproducibility/tree/main/notebooks/data_preprocessing/pancreas # noqa: E501 + """ + is_int = ["smartseq2"] + is_int += ["inDrop{}".format(x) for x in range(1, 5)] + + keep = np.zeros(len(adata)).astype(bool) + + for tech in is_int: + idx = adata.obs.tech.values == tech + keep = keep | idx + + adata = adata[keep, :].copy() + + return adata diff --git a/openproblems/data/pbmc.py b/openproblems/data/pbmc.py deleted file mode 100644 index cea1eec1e6..0000000000 --- a/openproblems/data/pbmc.py +++ /dev/null @@ -1,25 +0,0 @@ -from . import utils - -import os -import scanpy as sc -import scprep -import tempfile - -URL = "https://ndownloader.figshare.com/files/24974582" - - -@utils.loader(data_url=URL) -def load_pbmc(test=False): - """Download PBMC data from Figshare.""" - with tempfile.TemporaryDirectory() as tempdir: - filepath = os.path.join(tempdir, "pbmc.h5ad") - scprep.io.download.download_url(URL, filepath) - adata = sc.read_h5ad(filepath) - utils.filter_genes_cells(adata) - - if test: - sc.pp.subsample(adata, n_obs=100) - adata = adata[:, :1000] - utils.filter_genes_cells(adata) - - return adata diff --git a/openproblems/data/sample.py b/openproblems/data/sample.py index 6122b80361..008406807f 100644 --- a/openproblems/data/sample.py +++ b/openproblems/data/sample.py @@ -8,18 +8,20 @@ import scipy.sparse -@loader(data_url="https://openproblems.bio") +@loader( + data_url="https://openproblems.bio", + data_reference="https://github.com/openproblems-bio/openproblems", +) def load_sample_data(test=True): """Create a simple dataset to use for testing in multimodal applications.""" assert test - genes = pd.read_csv(rna_genes_url, low_memory=False, index_col=0).iloc[:500] - cells = pd.read_csv(rna_cells_url, low_memory=False, index_col=0).iloc[:200] + genes = pd.read_csv(rna_genes_url, low_memory=False, index_col=0, nrows=500) + cells = pd.read_csv(rna_cells_url, low_memory=False, index_col=0, nrows=200) rna_data = scipy.sparse.csr_matrix( - np.random.poisson(0.3, (cells.shape[0], genes.shape[0])) + np.random.poisson(0.3, (cells.shape[0], genes.shape[0])).astype(np.float32) ) adata = anndata.AnnData(rna_data, obs=cells, var=genes) - adata.X = adata.X.astype(np.float64) return adata diff --git a/openproblems/data/tabula_muris_senis.py b/openproblems/data/tabula_muris_senis.py index d2d053608e..28f709ea88 100644 --- a/openproblems/data/tabula_muris_senis.py +++ b/openproblems/data/tabula_muris_senis.py @@ -59,7 +59,10 @@ def make_anndata_list(subset_df, test): return adata_list -@utils.loader(data_url="https://tabula-muris-senis.ds.czbiohub.org/") +@utils.loader( + data_url="https://tabula-muris-senis.ds.czbiohub.org/", + data_reference="https://doi.org/10.1038/s41586-020-2496-1", +) def load_tabula_muris_senis(test=False, method_list=None, organ_list=None): """Load tubula_muris_senis datasets into 1 anndata object based on user input. diff --git a/openproblems/data/tenx.py b/openproblems/data/tenx.py new file mode 100644 index 0000000000..73cccee21c --- /dev/null +++ b/openproblems/data/tenx.py @@ -0,0 +1,64 @@ +from . import utils + +import os +import scanpy as sc +import scprep +import tempfile + +# sparsified from https://ndownloader.figshare.com/files/24974582 +# TODO(@LuckyMD): change link to figshare.com/articles/* +PBMC_1K_URL = "https://ndownloader.figshare.com/files/36088667" + +# TODO(@LuckyMD): document relevant link at figshare.com/articles/* +PBMC_5K_URL = "https://ndownloader.figshare.com/files/25555739" +REFERENCE_URL = "https://www.10xgenomics.com/resources/datasets" + + +@utils.loader(data_url=PBMC_1K_URL, data_reference=REFERENCE_URL) +def load_tenx_1k_pbmc(test=False): + """Download PBMC data from Figshare.""" + if test: + adata = load_tenx_1k_pbmc(test=False) + sc.pp.subsample(adata, n_obs=100) + adata = adata[:, :1000] + utils.filter_genes_cells(adata) + else: + with tempfile.TemporaryDirectory() as tempdir: + filepath = os.path.join(tempdir, "pbmc.h5ad") + scprep.io.download.download_url(PBMC_1K_URL, filepath) + adata = sc.read_h5ad(filepath) + utils.filter_genes_cells(adata) + return adata + + +@utils.loader(data_url=PBMC_5K_URL, data_reference=REFERENCE_URL) +def load_tenx_5k_pbmc(test=False): + """Download 5k PBMCs from 10x Genomics.""" + if test: + # load full data first, cached if available + adata = load_tenx_5k_pbmc(test=False) + + # Subsample pancreas data + adata = adata[:, :500].copy() + utils.filter_genes_cells(adata) + + sc.pp.subsample(adata, n_obs=500) + # Note: could also use 200-500 HVGs rather than 200 random genes + + # Ensure there are no cells or genes with 0 counts + utils.filter_genes_cells(adata) + + return adata + + else: + with tempfile.TemporaryDirectory() as tempdir: + filepath = os.path.join(tempdir, "10x_5k_pbmc.h5ad") + scprep.io.download.download_url(PBMC_5K_URL, filepath) + adata = sc.read(filepath) + + adata.var_names_make_unique() + + # Ensure there are no cells or genes with 0 counts + utils.filter_genes_cells(adata) + + return adata diff --git a/openproblems/data/tenx_5k_pbmc.py b/openproblems/data/tenx_5k_pbmc.py deleted file mode 100644 index aa86b6c373..0000000000 --- a/openproblems/data/tenx_5k_pbmc.py +++ /dev/null @@ -1,41 +0,0 @@ -from . import utils - -import os -import scanpy as sc -import scprep -import tempfile - -URL = "https://ndownloader.figshare.com/files/25555739" - - -@utils.loader(data_url=URL) -def load_tenx_5k_pbmc(test=False): - """Download 5k PBMCs from 10x Genomics.""" - if test: - # load full data first, cached if available - adata = load_tenx_5k_pbmc(test=False) - - # Subsample pancreas data - adata = adata[:, :500].copy() - utils.filter_genes_cells(adata) - - sc.pp.subsample(adata, n_obs=500) - # Note: could also use 200-500 HVGs rather than 200 random genes - - # Ensure there are no cells or genes with 0 counts - utils.filter_genes_cells(adata) - - return adata - - else: - with tempfile.TemporaryDirectory() as tempdir: - filepath = os.path.join(tempdir, "10x_5k_pbmc.h5ad") - scprep.io.download.download_url(URL, filepath) - adata = sc.read(filepath) - - adata.var_names_make_unique() - - # Ensure there are no cells or genes with 0 counts - utils.filter_genes_cells(adata) - - return adata diff --git a/openproblems/data/tnbc_wu2021.py b/openproblems/data/tnbc_wu2021.py new file mode 100644 index 0000000000..58dcc53d8b --- /dev/null +++ b/openproblems/data/tnbc_wu2021.py @@ -0,0 +1,85 @@ +from . import utils + +import numpy as np +import os +import scanpy as sc +import scipy.sparse +import scprep +import tempfile + +URL = "https://figshare.com/ndownloader/files/37593188" + + +@utils.loader(data_url=URL, data_reference="https://doi.org/10.1038/s41588-021-00911-1") +def load_tnbc_data(test=False): + """Download TNBC data (Wu et al., 2021) from Figshare. + + Further information how we generated the assumed truth and the reference + to the dataset is available at: + https://figshare.com/articles/dataset/TNBC_Data_from_Wu_et_al_2021/20338536 + + """ + if test: + # load full data first, cached if available + adata = load_tnbc_data(test=False) + + # Keep only relevant response ligands + lr = list(set(adata.uns["ccc_target"].ligand)) + # add corresponding receptors + lr += [ + "IFNLR1", + "CD70", + "NGFR", + "CD53", + "IL20RA", + "IL20RB", + "IL2RG", + "SIGLEC6", + "EGFR", + "ITGB1", + "ITGB3", + "ACVRL1", + "CXCR4", + "SMAD3", + "CAV1", + "IL21R", + "IL4R", + "IL2RG", + "IL13RA1", + "IL13RA2", + ] + adata = adata[:, adata.var.index.isin(lr)].copy() + + # Keep only cells with nonzero relevant ligands/receptors + utils.filter_genes_cells(adata) + + # Subsample data to random cell from each cell type + adata = utils.subsample_even(adata, n_obs=500, even_obs="label") + + # Add noise to ensure no genes are empty -- filtering genes could remove ligands + empty_gene_idx = np.argwhere(adata.X.sum(axis=0).A.flatten() == 0).flatten() + random_cell_idx = np.random.choice(adata.shape[0], len(empty_gene_idx)) + adata.X += scipy.sparse.coo_matrix( + ( + np.ones(len(empty_gene_idx), dtype=adata.X.dtype), + (random_cell_idx, empty_gene_idx), + ), + shape=adata.shape, + dtype=adata.X.dtype, + ) + adata.X = adata.X.tocsr() + # update layers["counts"] with the new counts + adata.layers["counts"] = adata.X + else: + with tempfile.TemporaryDirectory() as tempdir: + filepath = os.path.join(tempdir, "brca_tnbc.h5ad") + scprep.io.download.download_url(URL, filepath) + adata = sc.read(filepath) + + # Ensure there are no cells or genes with 0 counts + utils.filter_genes_cells(adata) + + # Remove unneeded cols + adata.uns["ccc_target"] = adata.uns["ccc_target"][["ligand", "target", "response"]] + + return adata diff --git a/openproblems/data/utils.py b/openproblems/data/utils.py index 4aa0d45c5d..ce1366ca92 100644 --- a/openproblems/data/utils.py +++ b/openproblems/data/utils.py @@ -33,13 +33,15 @@ def _cache_path(func, *args, **kwargs): return os.path.join(TEMPDIR, filename) -def loader(data_url): +def loader(data_url, data_reference): """Decorate a data loader function. Parameters ---------- data_url : str Link to the original source of the dataset + data_reference : str + Link to the paper describing how the dataset was generated """ def decorator(func): @@ -62,6 +64,8 @@ def apply_func(*args, **kwargs): adata = func(*args, **kwargs) adata.strings_to_categoricals() adata.uns["_from_cache"] = False + if "var_names_all" not in adata.uns: + adata.uns["var_names_all"] = adata.var.index.to_numpy() if "counts" not in adata.layers: adata.layers["counts"] = adata.X try: @@ -71,7 +75,7 @@ def apply_func(*args, **kwargs): adata.write_h5ad(filepath) return adata - apply_func.metadata = dict(data_url=data_url) + apply_func.metadata = dict(data_url=data_url, data_reference=data_reference) return apply_func return decorator @@ -79,6 +83,9 @@ def apply_func(*args, **kwargs): def filter_genes_cells(adata): """Remove empty cells and genes.""" + if "var_names_all" not in adata.uns: + # fill in original var names before filtering + adata.uns["var_names_all"] = adata.var.index.to_numpy() sc.pp.filter_genes(adata, min_cells=1) sc.pp.filter_cells(adata, min_counts=2) @@ -100,15 +107,14 @@ def subsample_even(adata, n_obs, even_obs): Subsampled AnnData object """ values = adata.obs[even_obs].unique() - adata_out = None + adatas = [] n_obs_per_value = n_obs // len(values) for v in values: adata_subset = adata[adata.obs[even_obs] == v].copy() sc.pp.subsample(adata_subset, n_obs=min(n_obs_per_value, adata_subset.shape[0])) - if adata_out is None: - adata_out = adata_subset - else: - adata_out = adata_out.concatenate(adata_subset, batch_key="_obs_batch") + adatas.append(adata_subset) + + adata_out = anndata.concat(adatas, label="_obs_batch") adata_out.uns = adata.uns adata_out.varm = adata.varm diff --git a/openproblems/data/zebrafish.py b/openproblems/data/zebrafish.py index a9475b9792..630b9be0a2 100644 --- a/openproblems/data/zebrafish.py +++ b/openproblems/data/zebrafish.py @@ -5,12 +5,13 @@ import scprep import tempfile +# TODO(@LuckyMD): document relevant link at figshare.com/articles/* URL = ( "https://ndownloader.figshare.com/files/24566651?private_link=e3921450ec1bd0587870" ) -@utils.loader(data_url=URL) +@utils.loader(data_url=URL, data_reference="https://doi.org/10.1126/science.aar4362") def load_zebrafish(test=False): """Download zebrafish data from figshare.""" with tempfile.TemporaryDirectory() as tempdir: @@ -22,6 +23,6 @@ def load_zebrafish(test=False): if test: adata = utils.subsample_even(adata, n_obs=500, even_obs="lab") utils.filter_genes_cells(adata) - adata = adata[:, :100] + adata = adata[:, :100].copy() utils.filter_genes_cells(adata) return adata diff --git a/openproblems/tasks/__init__.py b/openproblems/tasks/__init__.py index c76fb91d22..37fe938346 100644 --- a/openproblems/tasks/__init__.py +++ b/openproblems/tasks/__init__.py @@ -3,4 +3,9 @@ from . import label_projection from . import multimodal_data_integration from . import regulatory_effect_prediction +from . import spatial_decomposition +from ._batch_integration import batch_integration_embed +from ._batch_integration import batch_integration_feature from ._batch_integration import batch_integration_graph +from ._cell_cell_communication import cell_cell_communication_ligand_target +from ._cell_cell_communication import cell_cell_communication_source_target diff --git a/openproblems/tasks/_batch_integration/README.md b/openproblems/tasks/_batch_integration/README.md index 3295374d4c..f67783341d 100644 --- a/openproblems/tasks/_batch_integration/README.md +++ b/openproblems/tasks/_batch_integration/README.md @@ -1,13 +1,32 @@ # Batch integration -Batch (or data) integration methods integrate datasets across batches that arise from various biological (e.g., tissue, location, individual, species) and technical (e.g., ambient RNA, lab, protocol) sources. The goal of a batch integration method is to remove unwanted batch effects in the data, while retaining biologically-meaningful variation that can help us to detect cell identities, fit cellular trajectories, or understand patterns of gene or pathway activity. +Batch (or data) integration methods integrate datasets across batches that arise from +various biological (e.g., tissue, location, individual, species) and technical (e.g., +ambient RNA, lab, protocol) sources. The goal of a batch integration method is to remove +unwanted batch effects in the data, while retaining biologically-meaningful variation +that can help us to detect cell identities, fit cellular trajectories, or understand +patterns of gene or pathway activity. -Methods that integrate batches typically have one of three different types of output: a corrected feature matrix, a joint embedding across batches, and/or an integrated cell-cell similarity graph (e.g., a kNN graph). In order to define a consistent input and output for each method and metric, we have divided the batch integration task into three subtasks. These subtasks are: +Methods that integrate batches typically have one of three different types of output: +a corrected feature matrix, a joint embedding across batches, and/or an integrated +cell-cell similarity graph (e.g., a kNN graph). In order to define a consistent input +and output for each method and metric, we have divided the batch integration task into +three subtasks. These subtasks are: * [Batch integration graphs](batch_integration_graph/), * [Batch integration embeddings](batch_integration_embed/), and -* [Batch integrated feature matrices]() +* [Batch integrated feature matrices](batch_integration_feature/) -These subtasks collate methods that have the same data output type and metrics that evaluate this output. As corrected feature matrices can be turned into embeddings, which in turn can be processed into integrated graphs, methods overlap between the tasks. All methods are added to the graph subtask and imported into other subtasks from there. Information on the task API for datasets, methods, and metrics can be found in the individual subtask pages. +These subtasks collate methods that have the same data output type and metrics that +evaluate this output. As corrected feature matrices can be turned into embeddings, which +in turn can be processed into integrated graphs, methods overlap between the tasks. All +methods are added to the graph subtask and imported into other subtasks from there. +Information on the task API for datasets, methods, and metrics can be found in the +individual subtask pages. -Metrics for this task can be divided into those that assess the removal of batch effects, and assessments of the conservation of biological variation. This can be a helpful distinction when devising new metrics. This task, including the subtask structure, was taken from a [benchmarking study of data integration methods](https://www.biorxiv.org/content/10.1101/2020.05.22.111161v2). This is a useful reference for more background reading on the task and the above concepts. +Metrics for this task can be divided into those that assess the removal of batch +effects, and assessments of the conservation of biological variation. This can be a +helpful distinction when devising new metrics. This task, including the subtask +structure, was taken from a [benchmarking study of data integration +methods](https://www.biorxiv.org/content/10.1101/2020.05.22.111161v2). This is a useful +reference for more background reading on the task and the above concepts. diff --git a/openproblems/tasks/_batch_integration/batch_integration_embed/README.md b/openproblems/tasks/_batch_integration/batch_integration_embed/README.md new file mode 100644 index 0000000000..8ca0f3b096 --- /dev/null +++ b/openproblems/tasks/_batch_integration/batch_integration_embed/README.md @@ -0,0 +1,107 @@ + + +# Batch integration embedding + +This is a sub-task of the overall batch integration task. Batch (or data) integration +integrates datasets across batches that arise from various biological and technical +sources. Methods that integrate batches typically have three different types of output: +a corrected feature matrix, a joint embedding across batches, and/or an integrated +cell-cell similarity graph (e.g., a kNN graph). This sub-task focuses on all methods +that can output joint embeddings, and includes methods that canonically output corrected +feature matrices with subsequent postprocessing to generate a joint embedding. Other +sub-tasks for batch integration can be found for: + +* [graphs](../batch_integration_graph/), and +* [corrected features](../batch_integration_features) + +This sub-task was taken from a +[benchmarking study of data integration +methods](https://www.biorxiv.org/content/10.1101/2020.05.22.111161v2). + +## API + +WARNING: other than most tasks, `adata.X` should contain log-normalized data. + This is the case as we are comparing the results of integration on the + features pre- and post-integration and the data comes from different technologies. + In this subtask, we are computing a pre-integration embedding on the normalized + features. + For UMI data, the data is scran-normalized, full-length data is TPM-normalized. + +Datasets should contain the following attributes: + +* `adata.obs["batch"]` with the batch covariate, and +* `adata.obs["label"]` with the cell identity label +* `adata.obsm['X_uni']` with a pre-integration embedding (PCA) +* `adata.layers['log_normalized']` with log-normalized data +* `adata.X` with log-normalized data + +Methods should assign output to `adata.obsm['X_emb']`. + +The `openproblems-python-batch-integration` docker container is used for the methods +that can be installed without package conflicts. For R methods, the +`openproblems-r-extras` container is used. + +Most methods in this task are run in four different scenarios that include scaling and +highly variable gene selection: + +* `full_unscaled` +* `hvg_unscaled` +* `full_scaled` +* `hvg_scaled` + +Where `full` refers to the full gene set that is used as input to the method. + +Metrics can compare: + +* `adata.obsm['X_emb']` to `adata.obsm['X_uni']`, +* `adata.obsm['X_emb']` to `adata.obs['label']`, and/or +* `adata.obsm['X_emb']` to `adata.obs['batch']`. + +To reuse metrics functions from `scIB`, [`metrics._utils._get_split`](metrics/_utils.py) +separates the combined anndata into an integrated and an unintegrated anndata object. + +## Metrics + +In the following, we will give a short description of the implemented metrics. We split +by metrics capturing batch correction meaning the removal of batch effects and metrics +describing biological conservation, meaning how well the biological differences between +cell states are conserved. + +### Batch correction metrics + +#### kBET + +The kBET algorithm (v.0.99.6, release 4c9dafa) determines whether the label composition +of a k nearest neighborhood of a cell is similar to the expected (global) label +composition (Buettner et al., Nat Meth 2019). The test is repeated for a random subset +of cells, and the results are summarized as a rejection rate over all tested +neighborhoods. + +#### Silhouette batch score + +We consider the absolute silhouette width, s(i), on +batch labels per cell i. Here, 0 indicates that batches are well mixed, and any +deviation from 0 indicates a batch effect. + +#### Principal component regression + +Compare the explained variance by before and after integration. Returns a score between +0 and 1 (scaled=True) with 0 if the variance contribution hasn’t changed. The larger the +score, the more different the variance contributions are before and after integration. + +### Biological conservation metrics + +#### Cell cycle score + +The cell-cycle conservation score evaluates how well the cell-cycle effect can be +captured before and after integration. + +#### Isolated label silhouette + +This score evaluates the compactness for the label(s) that is(are) shared by fewest +batches. This indicates how well rare cell types can be preserved after integration. + +#### Cell type ASW + +For the bio-conservation score, the ASW is computed on cell identity labels, measuring +their compactness diff --git a/openproblems/tasks/_batch_integration/batch_integration_embed/__init__.py b/openproblems/tasks/_batch_integration/batch_integration_embed/__init__.py new file mode 100644 index 0000000000..46ac33c90b --- /dev/null +++ b/openproblems/tasks/_batch_integration/batch_integration_embed/__init__.py @@ -0,0 +1,16 @@ +from .... import utils +from . import api +from . import datasets +from . import methods +from . import metrics + +_task_name = "Batch integration embed" +_task_summary = ( + "Removing batch effects while preserving biological variation (embedding output)" +) + +DEFAULT_LAYER = "log_normalized" + +DATASETS = utils.get_callable_members(datasets) +METHODS = utils.get_callable_members(methods) +METRICS = utils.get_callable_members(metrics) diff --git a/openproblems/tasks/_batch_integration/batch_integration_embed/api.py b/openproblems/tasks/_batch_integration/batch_integration_embed/api.py new file mode 100644 index 0000000000..e7e47528b7 --- /dev/null +++ b/openproblems/tasks/_batch_integration/batch_integration_embed/api.py @@ -0,0 +1,46 @@ +from ....data.sample import load_sample_data +from ....tools.decorators import dataset + +import numpy as np +import scanpy as sc + + +def check_dataset(adata): + """Check that dataset output fits expected API.""" + + assert "X_uni_pca" in adata.obsm + assert "batch" in adata.obs + assert "labels" in adata.obs + assert "log_normalized" in adata.layers + + return True + + +def check_method(adata): + """Check that method output fits expected API.""" + assert "X_emb" in adata.obsm + return True + + +@dataset() +def sample_dataset(): + """Create a simple dataset to use for testing methods in this task.""" + adata = load_sample_data() + + adata.var.index = adata.var.gene_short_name.astype(str) + sc.pp.normalize_total(adata) + sc.pp.log1p(adata) + adata.layers["log_normalized"] = adata.X + adata.obsm["X_uni_pca"] = sc.pp.pca(adata.X) + adata.obs["batch"] = np.random.choice(2, adata.shape[0], replace=True).astype(str) + adata.obs["labels"] = np.random.choice(5, adata.shape[0], replace=True).astype(str) + adata.var_names_make_unique() + adata.obs_names_make_unique() + return adata + + +def sample_method(adata): + """Create sample method output for testing metrics in this task.""" + + adata.obsm["X_emb"] = adata.obsm["X_uni_pca"] + return adata diff --git a/openproblems/tasks/_batch_integration/batch_integration_embed/datasets/__init__.py b/openproblems/tasks/_batch_integration/batch_integration_embed/datasets/__init__.py new file mode 100644 index 0000000000..4b86a1c17c --- /dev/null +++ b/openproblems/tasks/_batch_integration/batch_integration_embed/datasets/__init__.py @@ -0,0 +1,2 @@ +from ...batch_integration_graph.datasets.immune import immune_batch +from ...batch_integration_graph.datasets.pancreas import pancreas_batch diff --git a/openproblems/tasks/_batch_integration/batch_integration_embed/methods/__init__.py b/openproblems/tasks/_batch_integration/batch_integration_embed/methods/__init__.py new file mode 100644 index 0000000000..dffc02a1f9 --- /dev/null +++ b/openproblems/tasks/_batch_integration/batch_integration_embed/methods/__init__.py @@ -0,0 +1,30 @@ +from ...batch_integration_graph.methods.combat import combat_full_scaled +from ...batch_integration_graph.methods.combat import combat_full_unscaled +from ...batch_integration_graph.methods.combat import combat_hvg_scaled +from ...batch_integration_graph.methods.combat import combat_hvg_unscaled +from ...batch_integration_graph.methods.fastmnn import fastmnn_embed_full_scaled +from ...batch_integration_graph.methods.fastmnn import fastmnn_embed_full_unscaled +from ...batch_integration_graph.methods.fastmnn import fastmnn_embed_hvg_scaled +from ...batch_integration_graph.methods.fastmnn import fastmnn_embed_hvg_unscaled +from ...batch_integration_graph.methods.harmony import harmony_full_scaled +from ...batch_integration_graph.methods.harmony import harmony_full_unscaled +from ...batch_integration_graph.methods.harmony import harmony_hvg_scaled +from ...batch_integration_graph.methods.harmony import harmony_hvg_unscaled +from ...batch_integration_graph.methods.liger import liger_full_unscaled +from ...batch_integration_graph.methods.liger import liger_hvg_unscaled +from ...batch_integration_graph.methods.mnn import mnn_full_scaled +from ...batch_integration_graph.methods.mnn import mnn_full_unscaled +from ...batch_integration_graph.methods.mnn import mnn_hvg_scaled +from ...batch_integration_graph.methods.mnn import mnn_hvg_unscaled +from ...batch_integration_graph.methods.scanorama import scanorama_embed_full_scaled +from ...batch_integration_graph.methods.scanorama import scanorama_embed_full_unscaled +from ...batch_integration_graph.methods.scanorama import scanorama_embed_hvg_scaled +from ...batch_integration_graph.methods.scanorama import scanorama_embed_hvg_unscaled +from ...batch_integration_graph.methods.scanorama import scanorama_feature_full_scaled +from ...batch_integration_graph.methods.scanorama import scanorama_feature_full_unscaled +from ...batch_integration_graph.methods.scanorama import scanorama_feature_hvg_scaled +from ...batch_integration_graph.methods.scanorama import scanorama_feature_hvg_unscaled +from ...batch_integration_graph.methods.scanvi import scanvi_full_unscaled +from ...batch_integration_graph.methods.scanvi import scanvi_hvg_unscaled +from ...batch_integration_graph.methods.scvi import scvi_full_unscaled +from ...batch_integration_graph.methods.scvi import scvi_hvg_unscaled diff --git a/openproblems/tasks/_batch_integration/batch_integration_embed/metrics/__init__.py b/openproblems/tasks/_batch_integration/batch_integration_embed/metrics/__init__.py new file mode 100644 index 0000000000..215ac44937 --- /dev/null +++ b/openproblems/tasks/_batch_integration/batch_integration_embed/metrics/__init__.py @@ -0,0 +1,6 @@ +from .cc_score import cc_score +from .iso_label_sil import isolated_labels_sil +from .kBET import kBET +from .pcr import pcr +from .sil_batch import silhouette_batch +from .silhouette import silhouette diff --git a/openproblems/tasks/_batch_integration/batch_integration_embed/metrics/_utils.py b/openproblems/tasks/_batch_integration/batch_integration_embed/metrics/_utils.py new file mode 100644 index 0000000000..8a4b33cb72 --- /dev/null +++ b/openproblems/tasks/_batch_integration/batch_integration_embed/metrics/_utils.py @@ -0,0 +1,8 @@ +def _get_split(adata): + uni = adata + uni.obsm["X_pca"] = uni.obsm["X_uni_pca"] + + if "X_emb" not in adata.obsm: + adata.obsm["X_emb"] = adata.obsm["X_pca"] + + return (uni, adata) diff --git a/openproblems/tasks/_batch_integration/batch_integration_embed/metrics/cc_score.py b/openproblems/tasks/_batch_integration/batch_integration_embed/metrics/cc_score.py new file mode 100644 index 0000000000..322891b202 --- /dev/null +++ b/openproblems/tasks/_batch_integration/batch_integration_embed/metrics/cc_score.py @@ -0,0 +1,35 @@ +from .....tools.decorators import metric + +""" +The cell-cycle conservation score evaluates how well the cell-cycle effect can be +captured before and after integration. We computed cell-cycle scores using Scanpy’s +score_cell_cycle function with a reference gene set from Tirosh et al for the +respective cell-cycle phases. We used the same set of cell-cycle genes for mouse and +human data (using capitalization to convert between the gene symbols). We then computed +the variance contribution of the resulting S and G2/M phase scores using principal +component regression (Principal component regression), which was performed for each +batch separately. The differences in variance before, Varbefore, and after, Varafter, +integration were aggregated into a final score between 0 and 1, using the equation: +CCconservation=1−|Varafter−Varbefore|/Varbefore. + +In this equation, values close to 0 indicate lower conservation and 1 indicates complete +conservation of the variance explained by cell cycle. In other words, the variance +remains unchanged within each batch for complete conservation, while any deviation from +the preintegration variance contribution reduces the score.""" + + +@metric( + metric_name="Cell Cycle Score", + maximize=True, + image="openproblems-python-batch-integration", # only if required +) +def cc_score(adata, test=False): + from ._utils import _get_split + from scib.metrics import cell_cycle + + try: + cc = cell_cycle(*_get_split(adata), "batch", embed="X_emb", organism="human") + + except ValueError: + cc = 0 + return cc diff --git a/openproblems/tasks/_batch_integration/batch_integration_embed/metrics/iso_label_sil.py b/openproblems/tasks/_batch_integration/batch_integration_embed/metrics/iso_label_sil.py new file mode 100644 index 0000000000..c3575de5b8 --- /dev/null +++ b/openproblems/tasks/_batch_integration/batch_integration_embed/metrics/iso_label_sil.py @@ -0,0 +1,30 @@ +from .....tools.decorators import metric + +""" +Isolated cell labels are defined as the labels present in the least number +of batches in the integration task. The score evaluates how well these isolated labels +separate from other cell identities. + +The isolated label ASW score is obtained by computing the +ASW of isolated versus non-isolated labels on the PCA embedding (ASW metric above) and +scaling this score to be between 0 and 1. The final score for each metric version +consists of the mean isolated score of all isolated labels. +""" + + +@metric( + metric_name="Isolated label Silhouette", + maximize=True, + image="openproblems-python-batch-integration", # only if required +) +def isolated_labels_sil(adata): + from scib.metrics import isolated_labels + + return isolated_labels( + adata, + label_key="labels", + batch_key="batch", + embed="X_emb", + cluster=False, + verbose=False, + ) diff --git a/openproblems/tasks/_batch_integration/batch_integration_embed/metrics/kBET.py b/openproblems/tasks/_batch_integration/batch_integration_embed/metrics/kBET.py new file mode 100644 index 0000000000..271eeadbd6 --- /dev/null +++ b/openproblems/tasks/_batch_integration/batch_integration_embed/metrics/kBET.py @@ -0,0 +1,43 @@ +from .....tools.decorators import metric + +""" +The kBET algorithm (v.0.99.6, release 4c9dafa) determines whether the label composition +of a k nearest neighborhood of a cell is similar to the expected (global) label +composition (Buettner et al., Nat Meth 2019). The test is repeated for a random subset +of cells, and the results are summarized as a rejection rate over all tested +neighborhoods. Thus, kBET works on a kNN graph. + +We compute kNN graphs where k = 50 for joint embeddings and corrected feature outputs +via Scanpy preprocessing steps. To test for technical effects and to account for +cell-type frequency shifts across datasets, we applied kBET +separately on the batch variable for each cell identity label. Using the kBET defaults, +a k equal to the median of the number of cells per batch within each label is used for +this computation. Additionally, we set the minimum and maximum thresholds of k to 10 and +100, respectively. As kNN graphs that have been subset by cell identity labels may no +longer be connected, we compute kBET per connected component. If >25% of cells were +assigned to connected components too small for kBET computation (smaller than k × 3), +we assigned a kBET score of 1 to denote poor batch removal. Subsequently, kBET scores +for each label were averaged and subtracted from 1 to give a final kBET score. + +In Open Problems we do not run kBET on graph outputs to avoid computation-intensive +diffusion processes being run. +""" + + +@metric( + metric_name="kBET", + maximize=True, + image="openproblems-r-extras", +) +def kBET(adata): + from scib.metrics import kBET + + return kBET( + adata, + batch_key="batch", + label_key="labels", + type_="embed", + embed="X_emb", + scaled=True, + verbose=False, + ) diff --git a/openproblems/tasks/_batch_integration/batch_integration_embed/metrics/pcr.py b/openproblems/tasks/_batch_integration/batch_integration_embed/metrics/pcr.py new file mode 100644 index 0000000000..886f26078b --- /dev/null +++ b/openproblems/tasks/_batch_integration/batch_integration_embed/metrics/pcr.py @@ -0,0 +1,27 @@ +from .....tools.decorators import metric + +""" +Principal component regression, derived from PCA, has previously been used to quantify +batch removal. Briefly, the R2 was calculated from a linear regression of the +covariate of interest (for example, the batch variable B) onto each principal component. +The variance contribution of the batch effect per principal component was then +calculated as the product of the variance explained by the ith principal component (PC) +and the corresponding R2(PCi|B). The sum across all variance contributions by the batch +effects in all principal components gives the total variance explained by the batch +variable as follows: +Var(𝐶|𝐵)=∑𝑖=1𝐺Var(𝐶|PC𝑖)×𝑅2(PC𝑖|𝐵), + +where Var(C|PCi) is the variance of the data matrix C explained by the ith principal +component.""" + + +@metric( + metric_name="PC Regression", + maximize=True, + image="openproblems-python-batch-integration", # only if required +) +def pcr(adata): + from ._utils import _get_split + from scib.metrics import pcr_comparison + + return pcr_comparison(*_get_split(adata), "batch", embed="X_emb") diff --git a/openproblems/tasks/_batch_integration/batch_integration_embed/metrics/sil_batch.py b/openproblems/tasks/_batch_integration/batch_integration_embed/metrics/sil_batch.py new file mode 100644 index 0000000000..c02e5e42aa --- /dev/null +++ b/openproblems/tasks/_batch_integration/batch_integration_embed/metrics/sil_batch.py @@ -0,0 +1,33 @@ +from .....tools.decorators import metric + +""" +We consider the absolute silhouette width, s(i), on +batch labels per cell i. Here, 0 indicates that batches are well mixed, and any +deviation from 0 indicates a batch effect: +𝑠batch(𝑖)=|𝑠(𝑖)|. + +To ensure higher scores indicate better batch mixing, these scores are scaled by +subtracting them from 1. As we expect batches to integrate within cell identity +clusters, we compute the batchASWj score for each cell label j separately, +using the equation: +batchASW𝑗=1|𝐶𝑗|∑𝑖∈𝐶𝑗1−𝑠batch(𝑖), + +where Cj is the set of cells with the cell label j and |Cj| denotes the number of cells +in that set. + +To obtain the final batchASW score, the label-specific batchASWj scores are averaged: +batchASW=1|𝑀|∑𝑗∈𝑀batchASW𝑗. + +Here, M is the set of unique cell labels.""" + + +@metric( + metric_name="Batch ASW", + maximize=True, + image="openproblems-python-batch-integration", # only if required +) +def silhouette_batch(adata): + from scib.metrics import silhouette_batch + + sil = silhouette_batch(adata, batch_key="batch", group_key="labels", embed="X_emb") + return sil diff --git a/openproblems/tasks/_batch_integration/batch_integration_embed/metrics/silhouette.py b/openproblems/tasks/_batch_integration/batch_integration_embed/metrics/silhouette.py new file mode 100644 index 0000000000..36991e1d67 --- /dev/null +++ b/openproblems/tasks/_batch_integration/batch_integration_embed/metrics/silhouette.py @@ -0,0 +1,20 @@ +from .....tools.decorators import metric + +""" +For the bio-conservation score, the ASW was computed on cell identity labels and +scaled to a value between 0 and 1 using the equation: +celltypeASW=(ASW_C+1)/2, + +where C denotes the set of all cell identity labels. +For information about the batch silhouette score, check sil_batch.""" + + +@metric( + metric_name="Silhouette", + maximize=True, + image="openproblems-python-batch-integration", # only if required +) +def silhouette(adata): + from scib.metrics import silhouette + + return silhouette(adata, group_key="labels", embed="X_emb") diff --git a/openproblems/tasks/_batch_integration/batch_integration_feature/README.md b/openproblems/tasks/_batch_integration/batch_integration_feature/README.md new file mode 100644 index 0000000000..76ce3f1e33 --- /dev/null +++ b/openproblems/tasks/_batch_integration/batch_integration_feature/README.md @@ -0,0 +1,62 @@ + + +# Batch integration feature + +This is a sub-task of the overall batch integration task. Batch (or data) integration +integrates datasets across batches that arise from various biological and technical +sources. Methods that integrate batches typically have three different types of output: +a corrected feature matrix, a joint embedding across batches, and/or an integrated +cell-cell similarity graph (e.g., a kNN graph). This sub-task focuses on all methods +that can output feature matrices. Other sub-tasks for batch integration can be found for: + +* [graphs](../batch_integration_graph/), and +* [embeddings](../batch_integration_embed/) + +This sub-task was taken from a [benchmarking study of data integration +methods](https://www.biorxiv.org/content/10.1101/2020.05.22.111161v2). + +## API + +WARNING: other than most tasks, `adata.X` should contain log-normalized data. + This is the case as we are comparing the results of integration on the + features pre- and post-integration and the data comes from different technologies. + In this subtask, we are computing a pre-integration embedding on the normalized + features. + For UMI data, the data is scran-normalized, full-length data is TPM-normalized. + +Datasets should contain the following attributes: + +* `adata.obs["batch"]` with the batch covariate, and +* `adata.obs["label"]` with the cell identity label +* `adata.layers['counts']` with raw, integer UMI count data, +* `adata.layers['log_normalized']` with log-normalized data and +* `adata.X` with log-normalized data + +Methods should store their a batch-corrected gene expression matrix in `adata.X`. + +The `openproblems-python-batch-integration` docker container is used for the methods +that +can be installed without package conflicts. For R methods, the `openproblems-r-extras` +container is used. + +Most methods in the current task are run in four different scenarios that include scaling +and highly variable gene selection: + +* `full_unscaled` +* `hvg_unscaled` +* `full_scaled` +* `hvg_scaled` + +Metrics for this task compare: + +* `adata.X` (corrected adata) with `adata.layers['log_normalized']` (uncorrected data) + +To reuse metrics functions from `scIB`, [`metrics._utils._get_split`](metrics/_utils.py) +separates the combined anndata into an integrated and an unintegrated anndata object. + +## Metrics + +### HVG conservation + +Metric that computes the average percentage of overlapping highly variable genes per +batch before and after integration. diff --git a/openproblems/tasks/_batch_integration/batch_integration_feature/__init__.py b/openproblems/tasks/_batch_integration/batch_integration_feature/__init__.py new file mode 100644 index 0000000000..e775152084 --- /dev/null +++ b/openproblems/tasks/_batch_integration/batch_integration_feature/__init__.py @@ -0,0 +1,16 @@ +from .... import utils +from . import api +from . import datasets +from . import methods +from . import metrics + +_task_name = "Batch integration feature" +_task_summary = ( + "Removing batch effects while preserving biological variation (feature output)" +) + +DEFAULT_LAYER = "log_normalized" + +DATASETS = utils.get_callable_members(datasets) +METHODS = utils.get_callable_members(methods) +METRICS = utils.get_callable_members(metrics) diff --git a/openproblems/tasks/_batch_integration/batch_integration_feature/api.py b/openproblems/tasks/_batch_integration/batch_integration_feature/api.py new file mode 100644 index 0000000000..330a3a28b4 --- /dev/null +++ b/openproblems/tasks/_batch_integration/batch_integration_feature/api.py @@ -0,0 +1,51 @@ +from ....data.sample import load_sample_data +from ....tools.decorators import dataset + +import numpy as np +import scanpy as sc + + +def check_dataset(adata): + """Check that dataset output fits expected API.""" + + assert "batch" in adata.obs + assert "labels" in adata.obs + assert "log_normalized" in adata.layers + assert "counts" in adata.layers + assert adata.var_names.is_unique + assert adata.obs_names.is_unique + + return True + + +def check_method(adata): + """Check that method output fits expected API.""" + assert "log_normalized" in adata.layers + assert adata.layers["log_normalized"] is not adata.X + return True + + +@dataset() +def sample_dataset(): + """Create a simple dataset to use for testing methods in this task.""" + adata = load_sample_data() + + adata.var.index = adata.var.gene_short_name.astype(str) + sc.pp.normalize_total(adata) + + adata.obsm["X_uni"] = sc.pp.pca(adata.X) + adata.obs["batch"] = np.random.choice(2, adata.shape[0], replace=True).astype(str) + adata.obs["labels"] = np.random.choice(5, adata.shape[0], replace=True).astype(str) + adata.layers["counts"] = adata.X + adata.layers["log_normalized"] = adata.X.multiply( + 10000 / adata.X.sum(axis=1) + ).tocsr() + adata.var_names_make_unique() + adata.obs_names_make_unique() + return adata + + +def sample_method(adata): + """Create sample method output for testing metrics in this task.""" + adata.X = adata.X.multiply(2) + return adata diff --git a/openproblems/tasks/_batch_integration/batch_integration_feature/datasets/__init__.py b/openproblems/tasks/_batch_integration/batch_integration_feature/datasets/__init__.py new file mode 100644 index 0000000000..4b86a1c17c --- /dev/null +++ b/openproblems/tasks/_batch_integration/batch_integration_feature/datasets/__init__.py @@ -0,0 +1,2 @@ +from ...batch_integration_graph.datasets.immune import immune_batch +from ...batch_integration_graph.datasets.pancreas import pancreas_batch diff --git a/openproblems/tasks/_batch_integration/batch_integration_feature/methods/__init__.py b/openproblems/tasks/_batch_integration/batch_integration_feature/methods/__init__.py new file mode 100644 index 0000000000..f1243781f9 --- /dev/null +++ b/openproblems/tasks/_batch_integration/batch_integration_feature/methods/__init__.py @@ -0,0 +1,45 @@ +from ...batch_integration_graph.methods.combat import combat_full_scaled +from ...batch_integration_graph.methods.combat import combat_full_unscaled +from ...batch_integration_graph.methods.combat import combat_hvg_scaled +from ...batch_integration_graph.methods.combat import combat_hvg_unscaled + +# from ...batch_integration_graph.methods.desc import desc_full_scaled +# from ...batch_integration_graph.methods.desc import desc_full_unscaled +# from ...batch_integration_graph.methods.desc import desc_hvg_scaled +# from ...batch_integration_graph.methods.desc import desc_hvg_unscaled +from ...batch_integration_graph.methods.fastmnn import fastmnn_feature_full_scaled +from ...batch_integration_graph.methods.fastmnn import fastmnn_feature_full_unscaled +from ...batch_integration_graph.methods.fastmnn import fastmnn_feature_hvg_scaled +from ...batch_integration_graph.methods.fastmnn import fastmnn_feature_hvg_unscaled +from ...batch_integration_graph.methods.mnn import mnn_full_scaled +from ...batch_integration_graph.methods.mnn import mnn_full_unscaled +from ...batch_integration_graph.methods.mnn import mnn_hvg_scaled +from ...batch_integration_graph.methods.mnn import mnn_hvg_unscaled + +# from ...batch_integration_graph.methods.saucie import saucie_embed_full_scaled +# from ...batch_integration_graph.methods.saucie import saucie_embed_full_unscaled +# from ...batch_integration_graph.methods.saucie import saucie_embed_hvg_scaled +# from ...batch_integration_graph.methods.saucie import saucie_embed_hvg_unscaled +# from ...batch_integration_graph.methods.saucie import saucie_feature_full_scaled +# from ...batch_integration_graph.methods.saucie import saucie_feature_full_unscaled +# from ...batch_integration_graph.methods.saucie import saucie_feature_hvg_scaled +# from ...batch_integration_graph.methods.saucie import saucie_feature_hvg_unscaled +from ...batch_integration_graph.methods.scanorama import scanorama_feature_full_scaled +from ...batch_integration_graph.methods.scanorama import scanorama_feature_full_unscaled +from ...batch_integration_graph.methods.scanorama import scanorama_feature_hvg_scaled +from ...batch_integration_graph.methods.scanorama import scanorama_feature_hvg_unscaled + +# from ...batch_integration_graph.methods.scgen import scgen_full_scaled +# from ...batch_integration_graph.methods.scgen import scgen_full_unscaled +# from ...batch_integration_graph.methods.scgen import scgen_hvg_scaled +# from ...batch_integration_graph.methods.scgen import scgen_hvg_unscaled + +# from ...batch_integration_graph.methods.seurat_full import seurat_full_scaled +# from ...batch_integration_graph.methods.seurat_full import seurat_full_unscaled +# from ...batch_integration_graph.methods.seurat_full import seurat_hvg_scaled +# from ...batch_integration_graph.methods.seurat_full import seurat_hvg_unscaled + +# from ...batch_integration_graph.methods.seuratrpca import seuratrpca_full_scaled +# from ...batch_integration_graph.methods.seuratrpca import seuratrpca_full_unscaled +# from ...batch_integration_graph.methods.seuratrpca import seuratrpca_hvg_scaled +# from ...batch_integration_graph.methods.seuratrpca import seuratrpca_hvg_unscaled diff --git a/openproblems/tasks/_batch_integration/batch_integration_feature/metrics/__init__.py b/openproblems/tasks/_batch_integration/batch_integration_feature/metrics/__init__.py new file mode 100644 index 0000000000..fde16aa8c0 --- /dev/null +++ b/openproblems/tasks/_batch_integration/batch_integration_feature/metrics/__init__.py @@ -0,0 +1 @@ +from .hvg_conservation import hvg_conservation diff --git a/openproblems/tasks/_batch_integration/batch_integration_feature/metrics/hvg_conservation.py b/openproblems/tasks/_batch_integration/batch_integration_feature/metrics/hvg_conservation.py new file mode 100644 index 0000000000..d40b36b740 --- /dev/null +++ b/openproblems/tasks/_batch_integration/batch_integration_feature/metrics/hvg_conservation.py @@ -0,0 +1,32 @@ +from .....tools.decorators import metric + +""" +The HVG conservation score is a proxy for the preservation of +the biological signal. If the data integration method returned +a corrected data matrix, we computed the number of HVGs before +and after correction for each batch via Scanpy’s +highly_variable_genes function (using the ‘cell ranger’ flavor). +If available, we computed 500 HVGs per batch. If fewer than 500 +genes were present in the integrated object for a batch, +the number of HVGs was set to half the total genes in that batch. +The overlap coefficient is as follows: +overlap(𝑋,𝑌)=|𝑋∩𝑌|/min(|𝑋|,|𝑌|), + +where X and Y denote the fraction of preserved informative genes. +The overall HVG score is the mean of the per-batch HVG overlap +coefficients. +""" + + +@metric( + metric_name="HVG conservation", + maximize=True, + image="openproblems-python-batch-integration", +) +def hvg_conservation(adata): + from scib.metrics import hvg_overlap + + adata_unint = adata.copy() + adata_unint.X = adata_unint.layers["log_normalized"] + + return hvg_overlap(adata_unint, adata, "batch") diff --git a/openproblems/tasks/_batch_integration/batch_integration_graph/README.md b/openproblems/tasks/_batch_integration/batch_integration_graph/README.md index 5c67f7a363..97a30d8432 100644 --- a/openproblems/tasks/_batch_integration/batch_integration_graph/README.md +++ b/openproblems/tasks/_batch_integration/batch_integration_graph/README.md @@ -4,12 +4,20 @@ ## The task -This is a sub-task of the overall batch integration task. Batch (or data) integration methods integrate datasets across batches that arise from various biological and technical sources. Methods that integrate batches typically have three different types of output: a corrected feature matrix, a joint embedding across batches, and/or an integrated cell-cell similarity graph (e.g., a kNN graph). This sub-task focuses on all methods that can output integrated graphs, and includes methods that canonically output the other two data formats with subsequent postprocessing to generate a graph. Other sub-tasks for batch integration can be found for: +This is a sub-task of the overall batch integration task. Batch (or data) integration +methods integrate datasets across batches that arise from various biological and +technical sources. Methods that integrate batches typically have three different types +of output: a corrected feature matrix, a joint embedding across batches, and/or an +integrated cell-cell similarity graph (e.g., a kNN graph). This sub-task focuses on all +methods that can output integrated graphs, and includes methods that canonically output +the other two data formats with subsequent postprocessing to generate a graph. Other +sub-tasks for batch integration can be found for: * [embeddings](../batch_integration_embed/), and -* [corrected features]() +* [corrected features](../batch_integration_feature/) -This sub-task was taken from a [benchmarking study of data integration methods](https://www.biorxiv.org/content/10.1101/2020.05.22.111161v2). +This sub-task was taken from a [benchmarking study of data integration +methods](https://www.biorxiv.org/content/10.1101/2020.05.22.111161v2). ## The metrics @@ -20,6 +28,13 @@ Metrics for batch integration (graph) aim to TODO ## API +WARNING: other than most tasks, `adata.X` should contain log-normalized data. + This is the case as we are comparing the results of integration on the normalized + features pre- and post-integration and the data comes from different technologies. + In this subtask, we are computing a pre-integration graph on the normalized + features. + For UMI data, the data is scran-normalized, full-length data is TPM-normalized. + Datasets should contain the following attributes: * `adata.obs["batch"]` with the batch covariate, @@ -29,27 +44,34 @@ Datasets should contain the following attributes: * `adata.obsp['uni_connectivities']` with an unintegrated connectivity matrix generated by `scanpy.pp.neighbors()` * `adata.X` with log-normalized data +* `adata.layers['log_normalized']` with log-normalized data Methods can take anything from datasets as input and should assign output to: + * `adata.obsp['connectivities']` and `adata.obsp['distances']`, or * `adata.uns['neighbors']['connectivities']` and `adata.uns['neighbors']['distances']`. -Please note, that most methods do not use cell type labels, which improves their usability. +Please note, that most methods do not use cell type labels, which improves their +usability. -The `openproblems-python-batch-integration` docker container is used for the methods that -can be installed without package conflicts. (NOTE: add additional containers here) -For R methods, the `openproblems-r-extras` -container is used. +The `openproblems-python-batch-integration` docker container is used for the methods +that can be installed without package conflicts. (NOTE: add additional containers here) +For R methods, the `openproblems-r-extras` container is used. + +Methods are run in four different scenarios that include scaling and highly variable +gene selection: -Methods are run in four different scenarios that include scaling and highly variable gene selection: * `full_unscaled` * `hvg_unscaled` * `full_scaled` * `hvg_scaled` -Functions for scaling and highly variable gene selection per batch are reused from [`scib`](https://github.com/theislab/scib). Additionally, method wrappers are reused from scIB where possible. +Functions for scaling and highly variable gene selection per batch are reused from +[`scib`](https://github.com/theislab/scib). Additionally, method wrappers are reused +from scIB where possible. Metrics can compare: + * `adata.obsp['connectivities']` to `adata.obs['uni_connectivies']`, * `adata.obsp['connectivities']` to `adata.obs['label']`, and/or * `adata.obsp['connectivities']` to `adata.obs['batch']`. diff --git a/openproblems/tasks/_batch_integration/batch_integration_graph/__init__.py b/openproblems/tasks/_batch_integration/batch_integration_graph/__init__.py index 64f88cc046..11f9c5a100 100644 --- a/openproblems/tasks/_batch_integration/batch_integration_graph/__init__.py +++ b/openproblems/tasks/_batch_integration/batch_integration_graph/__init__.py @@ -5,7 +5,11 @@ from . import metrics _task_name = "Batch integration graph" -_task_summary = "TODO" +_task_summary = ( + "Removing batch effects while preserving biological variation (graph output)" +) + +DEFAULT_LAYER = "log_normalized" DATASETS = utils.get_callable_members(datasets) METHODS = utils.get_callable_members(methods) diff --git a/openproblems/tasks/_batch_integration/batch_integration_graph/api.py b/openproblems/tasks/_batch_integration/batch_integration_graph/api.py index bd4d7c2946..dd45a42aed 100644 --- a/openproblems/tasks/_batch_integration/batch_integration_graph/api.py +++ b/openproblems/tasks/_batch_integration/batch_integration_graph/api.py @@ -8,10 +8,11 @@ def check_dataset(adata): """Check that dataset output fits expected API.""" - assert "X_uni" in adata.obsm + assert "X_uni_pca" in adata.obsm assert "batch" in adata.obs assert "labels" in adata.obs assert "uni_connectivities" in adata.obsp + assert "log_normalized" in adata.layers return True @@ -27,11 +28,12 @@ def check_method(adata): def sample_dataset(): """Create a simple dataset to use for testing methods in this task.""" adata = load_sample_data() - adata.obsm["X_uni"] = sc.pp.pca(adata.X) + adata.obsm["X_uni_pca"] = sc.pp.pca(adata.X) + adata.layers["log_normalized"] = adata.X adata.obs["batch"] = np.random.choice(2, adata.shape[0], replace=True).astype(str) adata.obs["labels"] = np.random.choice(5, adata.shape[0], replace=True).astype(str) - sc.pp.neighbors(adata, use_rep="X_uni", key_added="uni") + sc.pp.neighbors(adata, use_rep="X_uni_pca", key_added="uni") return adata @@ -39,5 +41,5 @@ def sample_method(adata): """Create sample method output for testing metrics in this task.""" import scanpy as sc - sc.pp.neighbors(adata, use_rep="X_uni") + sc.pp.neighbors(adata, use_rep="X_uni_pca") return adata diff --git a/openproblems/tasks/_batch_integration/batch_integration_graph/datasets/immune.py b/openproblems/tasks/_batch_integration/batch_integration_graph/datasets/immune.py index 0d3e26e407..8c39c0fe28 100644 --- a/openproblems/tasks/_batch_integration/batch_integration_graph/datasets/immune.py +++ b/openproblems/tasks/_batch_integration/batch_integration_graph/datasets/immune.py @@ -7,7 +7,10 @@ @dataset( dataset_name="Immune (by batch)", data_url=load_immune.metadata["data_url"], - dataset_summary="TODO", + data_reference=load_immune.metadata["data_reference"], + dataset_summary="Human immune cells from peripheral blood and bone marrow " + "taken from 5 datasets comprising 10 batches across technologies (10X, " + "Smart-seq2).", image="openproblems", ) def immune_batch(test=False): @@ -16,13 +19,18 @@ def immune_batch(test=False): sc.pp.filter_genes(adata, min_counts=1) sc.pp.filter_genes(adata, min_cells=1) + adata.var_names_make_unique() + adata.obs_names_make_unique() + + adata.X = adata.layers["log_normalized"] sc.tl.pca( adata, svd_solver="arpack", return_info=True, ) - adata.obsm["X_uni"] = adata.obsm["X_pca"] + adata.obsm["X_uni_pca"] = adata.obsm["X_pca"] - sc.pp.neighbors(adata, use_rep="X_uni", key_added="uni") + sc.pp.neighbors(adata, use_rep="X_uni_pca", key_added="uni") + adata.var_names_make_unique() return adata diff --git a/openproblems/tasks/_batch_integration/batch_integration_graph/datasets/pancreas.py b/openproblems/tasks/_batch_integration/batch_integration_graph/datasets/pancreas.py index f441e1a8ac..6d8af4f505 100644 --- a/openproblems/tasks/_batch_integration/batch_integration_graph/datasets/pancreas.py +++ b/openproblems/tasks/_batch_integration/batch_integration_graph/datasets/pancreas.py @@ -7,7 +7,10 @@ @dataset( dataset_name="Pancreas (by batch)", data_url=load_pancreas.metadata["data_url"], - dataset_summary="TODO", + data_reference=load_pancreas.metadata["data_reference"], + dataset_summary="Human pancreatic islet scRNA-seq data from 6 datasets " + "across technologies (CEL-seq, CEL-seq2, Smart-seq2, inDrop, Fluidigm C1, " + "and SMARTER-seq).", image="openproblems", ) def pancreas_batch(test=False): @@ -18,13 +21,16 @@ def pancreas_batch(test=False): sc.pp.filter_genes(adata, min_counts=1) sc.pp.filter_genes(adata, min_cells=1) + adata.X = adata.layers["log_normalized"] + sc.tl.pca( adata, svd_solver="arpack", return_info=True, ) - adata.obsm["X_uni"] = adata.obsm["X_pca"] + adata.obsm["X_uni_pca"] = adata.obsm["X_pca"] - sc.pp.neighbors(adata, use_rep="X_uni", key_added="uni") + sc.pp.neighbors(adata, use_rep="X_uni_pca", key_added="uni") + adata.var_names_make_unique() return adata diff --git a/openproblems/tasks/_batch_integration/batch_integration_graph/methods/_utils.py b/openproblems/tasks/_batch_integration/batch_integration_graph/methods/_utils.py index e69c6be9a7..3f34a68054 100644 --- a/openproblems/tasks/_batch_integration/batch_integration_graph/methods/_utils.py +++ b/openproblems/tasks/_batch_integration/batch_integration_graph/methods/_utils.py @@ -21,5 +21,5 @@ def scale_batch(adata, batch_key): var = adata.var.copy() adata = scale_batch(adata, batch_key) - adata.var = var + adata.var = var.loc[adata.var_names] return adata diff --git a/openproblems/tasks/_batch_integration/batch_integration_graph/methods/combat.py b/openproblems/tasks/_batch_integration/batch_integration_graph/methods/combat.py index 4278fdde65..3043a552e1 100644 --- a/openproblems/tasks/_batch_integration/batch_integration_graph/methods/combat.py +++ b/openproblems/tasks/_batch_integration/batch_integration_graph/methods/combat.py @@ -20,6 +20,7 @@ def _combat(adata): adata = runCombat(adata, "batch") reduce_data(adata, umap=False) + adata.obsm["X_emb"] = adata.obsm["X_pca"] adata.uns["method_code_version"] = check_version("scanpy") return adata diff --git a/openproblems/tasks/_batch_integration/batch_integration_graph/methods/fastmnn.py b/openproblems/tasks/_batch_integration/batch_integration_graph/methods/fastmnn.py index 1d3d020c6b..1391d076e9 100644 --- a/openproblems/tasks/_batch_integration/batch_integration_graph/methods/fastmnn.py +++ b/openproblems/tasks/_batch_integration/batch_integration_graph/methods/fastmnn.py @@ -25,6 +25,7 @@ def _fastmnn( k: Optional[int] = None, n_pca: Optional[int] = None, ): + if test: k = k or 5 n_pca = n_pca or 10 @@ -65,6 +66,7 @@ def _fastmnn_feature( adata, batch, test=test, k=k, n_pca=n_pca, return_features=True ).T reduce_data(adata, umap=False) + adata.obsm["X_emb"] = adata.obsm["X_pca"] adata.uns["method_code_version"] = check_r_version("batchelor") return adata diff --git a/openproblems/tasks/_batch_integration/batch_integration_graph/methods/liger.R b/openproblems/tasks/_batch_integration/batch_integration_graph/methods/liger.R index 15f223d377..a1961db511 100644 --- a/openproblems/tasks/_batch_integration/batch_integration_graph/methods/liger.R +++ b/openproblems/tasks/_batch_integration/batch_integration_graph/methods/liger.R @@ -5,12 +5,12 @@ library(Seurat) # Only counts is converted to liger object. To pass our own normalized data, # store it in the "counts" slot sobj <- as.Seurat(sce, data = NULL) -sobj@assays$RNA <- sobj@assays$originalexp # nolint +sobj@assays$RNA <- sobj@assays$originalexp sobj@assays$RNA@counts <- sobj@assays$RNA@data # Create Liger object lobj <- seuratToLiger(sobj, - combined.seurat = T, meta.var = batch, renormalize = FALSE, + combined.seurat = TRUE, meta.var = batch, renormalize = FALSE, remove.missing = FALSE ) diff --git a/openproblems/tasks/_batch_integration/batch_integration_graph/methods/mnn.py b/openproblems/tasks/_batch_integration/batch_integration_graph/methods/mnn.py index 452cf03aa3..99dab39203 100644 --- a/openproblems/tasks/_batch_integration/batch_integration_graph/methods/mnn.py +++ b/openproblems/tasks/_batch_integration/batch_integration_graph/methods/mnn.py @@ -20,6 +20,7 @@ def _mnn(adata): adata = runMNN(adata, "batch") reduce_data(adata, umap=False) + adata.obsm["X_emb"] = adata.obsm["X_pca"] adata.uns["method_code_version"] = check_version("mnnpy") return adata diff --git a/openproblems/tasks/_batch_integration/batch_integration_graph/methods/scanorama.py b/openproblems/tasks/_batch_integration/batch_integration_graph/methods/scanorama.py index 4ec83b420a..d6e80162b5 100644 --- a/openproblems/tasks/_batch_integration/batch_integration_graph/methods/scanorama.py +++ b/openproblems/tasks/_batch_integration/batch_integration_graph/methods/scanorama.py @@ -14,22 +14,27 @@ ) -def _scanorama(adata, use_rep): - from scib.integration import runScanorama +def _scanorama(adata, use_rep, pca): + from scib.integration import scanorama from scib.preprocessing import reduce_data - adata = runScanorama(adata, "batch") - reduce_data(adata, umap=False, use_rep=use_rep) + # scanorama clears adata.layers + layers = adata.layers + adata = scanorama(adata, "batch") + adata.layers = layers + reduce_data(adata, umap=False, use_rep=use_rep, pca=pca) adata.uns["method_code_version"] = check_version("scanorama") return adata def _scanorama_embed(adata): - return _scanorama(adata, use_rep="X_emb") + return _scanorama(adata, use_rep="X_emb", pca=False) def _scanorama_full(adata): - return _scanorama(adata, use_rep="X_pca") + adata = _scanorama(adata, use_rep="X_pca", pca=True) + adata.obsm["X_emb"] = adata.obsm["X_pca"] + return adata @_scanorama_method(method_name="Scanorama (full/unscaled)") diff --git a/openproblems/tasks/_cell_cell_communication/README.md b/openproblems/tasks/_cell_cell_communication/README.md new file mode 100644 index 0000000000..c0247e73cb --- /dev/null +++ b/openproblems/tasks/_cell_cell_communication/README.md @@ -0,0 +1,144 @@ +# Cell-cell Communication + +## The task + +The growing availability of single-cell data has sparked an increased +interest in the inference of cell-cell communication (CCC), +with an ever-growing number of computational tools developed for this purpose. + +Different tools propose distinct preprocessing steps with diverse +scoring functions, that are challenging to compare and evaluate. +Furthermore, each tool typically comes with its own set of prior knowledge. +To harmonize these, [Dimitrov et +al, 2022](https://doi.org/10.1038/s41467-022-30755-0) recently developed the +[LIANA](https://github.com/saezlab/liana) framework, which was used +as a foundation for this task. + +The challenges in evaluating the tools are further exacerbated by the +lack of a gold standard to benchmark the performance of CCC methods. In an +attempt to address this, Dimitrov et al use alternative data modalities, including +the spatial proximity of cell types and inferred +downstream cytokine activities, to generate an inferred ground truth. However, +these modalities are only approximations of biological reality and come +with their own assumptions and limitations. In time, the inclusion of more +datasets with known ground truth interactions will become available, from +which the limitations and advantages of the different CCC methods will +be better understood. + +## The subtasks + +Subtasks for cell-cell communication are defined by which aspects of a communication +event are detected. Currently, two subtasks aimed at steady-state, or single-context, +CCC predictions are defined: + +* [Ligand-Target](./cell_cell_communication_ligand_target): interactions between + ligand molecules and target cell types; and +* [Source-Target](./cell_cell_communication_source_target): interactions between + source cell types and target cell types. + +More subtasks may be defined that infer communication events on any of the `source` +cell type, the `target` cell type, the `ligand` molecule, and the receptor. +More aspects of the communication may also be added in the future. + +## The metrics + +Metrics for cell-cell communication aim to characterize how good are +the different scoring methods at prioritizing assumed truth predictions. + +* **Odds ratio**: The odds ratio represents the ratio of true and false +positives within a set of prioritized interactions (top ranked hits) versus +the same ratio for the remainder of the interactions. Thus, in this +scenario odds ratios quantify the strength of association between the +ability of methods to prioritize interactions and those interactions +assigned to the positive class. + +## API + +### Datasets + +Datasets should include cell type annotations in `adata.obs["label"]`, along with some +assumed truth in `adata.uns["ccc_target"]`. The assumed truth could be derived from +various proxies; we refer the reader to [Dimitrov et +al](https://doi.org/10.1038/s41467-022-30755-0) for more details. + +`adata.uns["ccc_target"]` should be a Pandas DataFrame containing: + +* `response`: `int`, binary response variable indicating whether an interaction is + assumed to have occurred + +and at least one of the following columns: + +* `source`: `str`, name of source cell type in interaction +* `target`: `str`, name of target cell type in interaction +* `ligand`: `str`, gene symbol of the ligand in an interaction +* `receptor`: `str`, gene symbol of the receptor in an interaction + +The datasets should also include a +[NCBI taxonomy ID](https://www.ncbi.nlm.nih.gov/Taxonomy/Browser/wwwtax.cgi) +in `adata.uns["target_organism"]` - used to convert the (typically human) prior +knowledge of the CCC methods to the corresponding gene homologs. +`adata.X` should contain the raw counts matrix. + +For subtasks including ligands or receptors in the inferred interactions, provide a +prior-k + +### Methods + +Methods should predict interactions between cell types without using +`adata.uns["ccc_target"]`. Predicted interactions should be stored in +`adata.uns["ccc_pred"]` as a Pandas DataFrame containing all of the following columns: + +* `score`: `float`, score between `-inf` to `+inf` giving a predicted strength of the + inferred interaction +* `source`: `str`, name of source cell type in interaction +* `target`: `str`, name of target cell type in interaction +* `ligand`: `str`, gene symbol of the ligand in an interaction +* `receptor`: `str`, gene symbol of the receptor in an interaction + +Methods should infer a score for each _intersecting interaction_ in the harmonized +prior-knowledge resource provided by LIANA. We define _intersecting interactions_ as +those for which the relevant genes are both present in the dataset and the resource. + +The prior-knowledge resource is available via the +`cell_cell_communication.utils.ligand_receptor_resource` function, which returns a +DataFrame containing the columns `ligand_genesymbol` and `receptor_genesymbol`, which +correspond to the ligand and receptor genes, respectively. These may contain complexes +with subunits separated with `_`. Hence, **methods should be able to deal with +complex-containing interactions**. + +### Prior-knowledge Resource + +Each dataset should be supplemented with a prior knowledge resource of +ligand-receptor interactions, with matching feature IDs. +The resource used in the [Ligand-Target](./cell_cell_communication_ligand_target) +and [Source-Target](./cell_cell_communication_source_target) +tasks was generated as the consensus from multiple manually-curated human +ligand-receptor resources, and includes interactions from +[CellPhoneDB](https://www.nature.com/articles/s41596-020-0292-x), +[CellChatDB](https://www.nature.com/articles/s41467-021-21246-9#disqus_thread), +[ICELLNET](https://www.nature.com/articles/s41467-021-21244-x), +[connectomeDB2020](https://www.nature.com/articles/s41467-020-18873-z), +and [CellTalkDB](https://www.nature.com/articles/s41467-020-18873-z) resources. +All of these were queried via the +[OmniPath database](https://www.embopress.org/doi/full/10.15252/msb.20209923), +and are fixed for each version of LIANA. + +### Metrics + +Metrics should evaluate the concordance between `adata.uns["ccc_target"]` and +`adata.uns["ccc_pred"]` to evaluate the success of a method in predicting interactions. + +### Examples + +The triple [negative breast cancer dataset]( +https://www.nature.com/articles/s41588-021-00911-1) (`tnbc_wu2021`) portrays +benchmark truth in the form of inferred cytokine activities in the target cell +types, as such in addition to the `response` column `adata.uns["ccc_target"]`, +also contains `ligand` and `target` columns. + +In the case of the [murine brain dataset]( +https://www.nature.com/articles/nn.4216) (`allen_brain_atlas`), we assume that +spatially-adjacent cell types are more likely to interact, hence interactions +between them should be preferentially detected. +Consequently, `adata.uns["ccc_target"]` contains `source`, `target`, +and `response` columns, but no `ligand` or `receptor` columns. diff --git a/openproblems/tasks/_cell_cell_communication/__init__.py b/openproblems/tasks/_cell_cell_communication/__init__.py new file mode 100644 index 0000000000..e69de29bb2 diff --git a/openproblems/tasks/_cell_cell_communication/_common/__init__.py b/openproblems/tasks/_cell_cell_communication/_common/__init__.py new file mode 100644 index 0000000000..e69de29bb2 diff --git a/openproblems/tasks/_cell_cell_communication/_common/api.py b/openproblems/tasks/_cell_cell_communication/_common/api.py new file mode 100644 index 0000000000..f0fdd504df --- /dev/null +++ b/openproblems/tasks/_cell_cell_communication/_common/api.py @@ -0,0 +1,271 @@ +from ....data.sample import load_sample_data + +import numbers +import numpy as np +import pandas as pd +import scanpy as sc + +SAMPLE_RECEPTOR_NAMES = [ + "LGALS9", + "PTPRC", + "LRP1", + "CD47", + "CD44", + "COL1A1", + "ADAM10", + "SIRPA", + "COL4A1", + "THBS2", +] + + +def assert_is_subset( + subset, + superset, + subset_name="subset", + superset_name="superset", + prop_missing_allowed=0, +): + """Assert `np.all(np.isin(subset, superset))` with a more readable error message""" + subset = np.asarray(subset) + is_missing = ~np.isin(subset, superset) + prop_missing = np.sum(is_missing) / len(subset) + + if prop_missing > prop_missing_allowed: + if prop_missing_allowed == 0: + msg = f"{subset_name} is not a subset of {superset_name}. " + else: + msg = ( + f"Allowed proportion ({prop_missing_allowed}) of missing " + f"{subset_name} elements exceeded ({prop_missing:.2f}). " + ) + x_missing = ",".join([x for x in subset[is_missing]]) + raise AssertionError(msg + f"{x_missing} missing from {superset_name}") + + +# Helper function to split complex subunits +def flatten_complex_subunits(entities): + return np.unique( + np.array( + [ + entity + for entity_complex in entities + for entity in entity_complex.split("_") + ] + ) + ) + + +def check_dataset(adata, merge_keys): + """Check that dataset output fits expected API.""" + assert "label" in adata.obs + assert "ccc_target" in adata.uns + + # check target organism + assert "target_organism" in adata.uns + assert isinstance(adata.uns["target_organism"], numbers.Integral) + + # check response columns + assert "score" not in adata.uns["ccc_target"] + assert "response" in adata.uns["ccc_target"] + assert np.issubdtype(adata.uns["ccc_target"]["response"].dtype, int) + assert np.all(np.isin(adata.uns["ccc_target"]["response"], [0, 1])) + + # check against resource + if "ligand" in merge_keys or "receptor" in merge_keys: + assert "ligand_receptor_resource" in adata.uns + assert "receptor_genesymbol" in adata.uns["ligand_receptor_resource"] + assert "ligand_genesymbol" in adata.uns["ligand_receptor_resource"] + assert "var_names_all" in adata.uns + assert_is_subset( + flatten_complex_subunits( + adata.uns["ligand_receptor_resource"]["receptor_genesymbol"] + ), + adata.uns["var_names_all"], + "resource receptor names", + "gene names", + 0.1, + ) + assert_is_subset( + flatten_complex_subunits( + adata.uns["ligand_receptor_resource"]["ligand_genesymbol"] + ), + adata.uns["var_names_all"], + "resource ligand names", + "gene names", + 0.1, + ) + + # check merge keys + if "source" in merge_keys: + assert "source" in adata.uns["ccc_target"] + assert_is_subset( + adata.uns["ccc_target"]["source"].unique(), + adata.obs["label"].cat.categories, + "source cell", + "cell types", + ) + if "target" in merge_keys: + assert "target" in adata.uns["ccc_target"] + assert_is_subset( + adata.uns["ccc_target"]["target"].unique(), + adata.obs["label"].cat.categories, + "target cell", + "cell types", + ) + + if "receptor" in merge_keys: + # verify target receptors are in resource + assert "receptor" in adata.uns["ccc_target"] + assert_is_subset( + adata.uns["ccc_target"]["receptor"].unique(), + np.unique(adata.uns["ligand_receptor_resource"]["receptor_genesymbol"]), + "target receptor names", + "resource receptor names", + ) + if "ligand" in merge_keys: + # verify target ligands are in resource + assert "ligand" in adata.uns["ccc_target"] + assert_is_subset( + adata.uns["ccc_target"]["ligand"].unique(), + np.unique(adata.uns["ligand_receptor_resource"]["ligand_genesymbol"]), + "target ligand names", + "resource ligand names", + ) + + return True + + +def check_method(adata, merge_keys): + """Check that method output fits expected API.""" + assert "ccc_pred" in adata.uns + + # check response columns + assert "response" not in adata.uns["ccc_pred"] + assert "score" in adata.uns["ccc_pred"] + assert np.all(np.isreal(adata.uns["ccc_pred"]["score"])) + + # check merge keys + if "ligand" in merge_keys: + assert "ligand" in adata.uns["ccc_pred"] + assert_is_subset( + adata.uns["ccc_pred"]["ligand"].unique(), + np.unique(adata.uns["ligand_receptor_resource"]["ligand_genesymbol"]), + "predicted ligand names", + "resource ligand names", + ) + if "receptor" in merge_keys: + assert "receptor" in adata.uns["ccc_pred"] + assert_is_subset( + adata.uns["ccc_pred"]["receptor"].unique(), + np.unique(adata.uns["ligand_receptor_resource"]["receptor_genesymbol"]), + "predicted receptor names", + "resource receptor names", + ) + if "source" in merge_keys: + assert "source" in adata.uns["ccc_pred"] + assert_is_subset( + adata.uns["ccc_pred"]["source"].unique(), + adata.obs["label"].cat.categories, + "source cell", + "cell types", + ) + if "target" in merge_keys: + assert "target" in adata.uns["ccc_pred"] + assert_is_subset( + adata.uns["ccc_pred"]["target"].unique(), + adata.obs["label"].cat.categories, + "target cell", + "cell types", + ) + + return True + + +def sample_dataset(merge_keys): + """Create a simple dataset to use for testing methods in this task.""" + adata = load_sample_data() + + # keep only the top 10 most variable + sc.pp.highly_variable_genes(adata, n_top_genes=len(SAMPLE_RECEPTOR_NAMES)) + adata = adata[:, adata.var["highly_variable"]] + # ensure we got the right number of genes + adata = adata[:, : len(SAMPLE_RECEPTOR_NAMES)].copy() + # hard-code var names to known interactions + adata.var.index = adata.uns["var_names_all"] = SAMPLE_RECEPTOR_NAMES + # transfer label + adata.obs["label"] = adata.obs.cell_name + + # generate target interactions + adata.uns["ccc_target"] = pd.DataFrame( + { + "response": np.random.binomial(1, 0.2, 50), + "ligand": np.random.choice(adata.var.index, 50), + "receptor": np.random.choice(adata.var.index, 50), + "source": np.random.choice(list(set(adata.obs.label)), 50), + "target": np.random.choice(list(set(adata.obs.label)), 50), + } + ) + # subset columns + adata.uns["ccc_target"] = adata.uns["ccc_target"][["response"] + merge_keys] + + # assign to human prior knowledge + adata.uns["target_organism"] = 9606 + n_complexes = 5 + n_genes = len(adata.var.index) + ligand_complexes = [ + "_".join(np.random.choice(adata.var.index, 2)) for _ in range(n_complexes) + ] + receptor_complexes = [ + "_".join(np.random.choice(adata.var.index, 2)) for _ in range(n_complexes) + ] + adata.uns["ligand_receptor_resource"] = pd.DataFrame( + { + "ligand_genesymbol": np.concatenate( + [ + ligand_complexes, + np.random.choice(adata.var.index, n_genes, replace=False), + ] + ), + "receptor_genesymbol": np.concatenate( + [ + receptor_complexes, + np.random.choice(adata.var.index, n_genes, replace=False), + ] + ), + } + ) + + return adata + + +def sample_method(adata, merge_keys): + """Create sample method output for testing metrics in this task.""" + row_num = 500 + np.random.seed(1234) + + ligand_msk = ~adata.uns["ligand_receptor_resource"]["ligand_genesymbol"].isin( + adata.var.index + ) + receptor_msk = ~adata.uns["ligand_receptor_resource"]["receptor_genesymbol"].isin( + adata.var.index + ) + msk = np.logical_not(ligand_msk | receptor_msk) + # keep only plausible interactions + resource = adata.uns["ligand_receptor_resource"][msk] + + df = pd.DataFrame(np.random.random((row_num, 1)), columns=["score"]) + df["source"] = np.random.choice(np.unique(adata.obs[["label"]]), row_num) + df["target"] = np.random.choice(np.unique(adata.obs[["label"]]), row_num) + df["ligand"] = np.random.choice( + np.unique(resource["ligand_genesymbol"].values), row_num + ) + df["receptor"] = np.random.choice( + np.unique(resource["receptor_genesymbol"].values), row_num + ) + # subset columns + df = df[["score"] + merge_keys] + + adata.uns["ccc_pred"] = df + + return adata diff --git a/openproblems/tasks/_cell_cell_communication/_common/methods/__init__.py b/openproblems/tasks/_cell_cell_communication/_common/methods/__init__.py new file mode 100644 index 0000000000..0db596d2be --- /dev/null +++ b/openproblems/tasks/_cell_cell_communication/_common/methods/__init__.py @@ -0,0 +1,6 @@ +from .liana import cellphonedb +from .liana import connectome +from .liana import liana +from .liana import logfc +from .liana import natmi +from .liana import sca diff --git a/openproblems/tasks/_cell_cell_communication/_common/methods/liana.R b/openproblems/tasks/_cell_cell_communication/_common/methods/liana.R new file mode 100644 index 0000000000..3d2a976907 --- /dev/null +++ b/openproblems/tasks/_cell_cell_communication/_common/methods/liana.R @@ -0,0 +1,46 @@ +# Standardized CCC method runs via LIANA +# @param sce SingleCellExperiment +# @param min_expression_prop minimum proportion of cells per cell type +# expressing the ligands and receptors for a given interaction +# @param idents_col cell identity column name +# @param test logical; if a test run +# @return a tibble with liana results + +# Libraries +library(SingleCellExperiment, quietly = TRUE) +library(tibble, quietly = TRUE) +library(dplyr, quietly = TRUE) +library(liana, quietly = TRUE) + +# Parameters +nperms <- if (test) 2 else 1000 + +# Convert data to an R-friendly sparse format +sce@assays@data$counts <- + as(as.matrix(sce@assays@data$counts), "sparseMatrix") +sce@assays@data$logcounts <- + as(as.matrix(sce@assays@data$logcounts), "sparseMatrix") + + + +# Run LIANA +liana_res <- liana_wrap(sce, + resource = "custom", + external_resource = op_resource, + expr_prop = min_expression_prop, + idents_col = idents_col, + base = 2.718282, # Relevant only for logfc + permutation.params = list(nperms = nperms), # Relevant only for CPDB + ... +) + +# Aggregate if a run /w multiple methods +if (!is.tibble(liana_res)) { + liana_res <- liana_res %>% + liana_aggregate() +} + +# Return (Keep Complexes [not subunits] for Consistency) +liana_res %>% + dplyr::select(-any_of(c("ligand", "receptor"))) %>% + dplyr::rename(ligand = ligand.complex, receptor = receptor.complex) diff --git a/openproblems/tasks/_cell_cell_communication/_common/methods/liana.py b/openproblems/tasks/_cell_cell_communication/_common/methods/liana.py new file mode 100644 index 0000000000..636e3ac2aa --- /dev/null +++ b/openproblems/tasks/_cell_cell_communication/_common/methods/liana.py @@ -0,0 +1,136 @@ +from .....tools.conversion import r_function +from .....tools.decorators import method +from .....tools.normalize import log_cpm +from .....tools.utils import check_r_version +from ..utils import ligand_receptor_resource + +import functools + + +# Helper function to filter according to permutation p-values +def _p_filt(x, y): + if x <= 0.05: + return y + else: + return 0 + + +_r_liana = r_function( + "liana.R", args="sce, op_resource, min_expression_prop, idents_col, test, ..." +) + +_liana_method = functools.partial( + method, + paper_name="Comparison of methods and resources for cell-cell " + "communication inference from single-cell RNA-Seq data", + paper_url="https://www.nature.com/articles/s41467-022-30755-0", + paper_year=2022, + code_url="https://github.com/saezlab/liana", + image="openproblems-r-extras", +) + + +@_liana_method( + method_name="LIANA", +) +def liana( + adata, + score_col="aggregate_rank", + ascending=True, + min_expression_prop=0.1, + test=False, + **kwargs, +): + # log-normalize + adata = log_cpm(adata) + adata.layers["logcounts"] = adata.layers["log_cpm"] + del adata.layers["log_cpm"] + + # Run LIANA + liana_res = _r_liana( + adata, + op_resource=ligand_receptor_resource(adata.uns["target_organism"]), + min_expression_prop=min_expression_prop, + idents_col="label", + test=test, + **kwargs, + ) + + # Format results + liana_res["score"] = liana_res[score_col] + liana_res.sort_values("score", ascending=ascending, inplace=True) + adata.uns["ccc_pred"] = liana_res + + adata.uns["method_code_version"] = check_r_version("liana") + + return adata + + +@_liana_method( + method_name="CellPhoneDB", + paper_name="CellPhoneDB: inferring cell–cell communication from " + "combined expression of multi-subunit ligand–receptor complexes", + paper_url="https://www.nature.com/articles/s41596-020-0292-x", + paper_year=2020, +) +def cellphonedb(adata, test=False): + adata = liana( + adata, + method="cellphonedb", + score_col="lr.mean", + ascending=False, + test=test, + complex_policy="min", + ) + # Filter & Re-order + adata.uns["ccc_pred"]["score"] = adata.uns["ccc_pred"].apply( + lambda x: _p_filt(x.pvalue, x["lr.mean"]), axis=1 + ) + adata.uns["ccc_pred"].sort_values("score", ascending=False, inplace=True) + + return adata + + +@_liana_method( + method_name="Connectome", + paper_name="Computation and visualization of cell–cell signaling " + "topologies in single-cell systems data using Connectome", + paper_url="https://www.nature.com/articles/s41598-022-07959-x", + paper_year=2022, +) +def connectome(adata, test=False): + return liana( + adata, method="connectome", score_col="weight_sc", ascending=False, test=test + ) + + +@_liana_method( + method_name="Mean log2FC", +) +def logfc(adata, test=False): + return liana( + adata, method="logfc", score_col="logfc_comb", ascending=False, test=test + ) + + +@_liana_method( + method_name="NATMI", + paper_name="Predicting cell-to-cell communication networks using NATMI", + paper_url="https://www.nature.com/articles/s41467-020-18873-z", + paper_year=2021, +) +def natmi(adata, test=False): + return liana( + adata, method="natmi", score_col="edge_specificity", ascending=False, test=test + ) + + +@_liana_method( + method_name="SingleCellSignalR", + paper_name="SingleCellSignalR: inference of intercellular networks " + "from single-cell transcriptomics", + paper_url="https://academic.oup.com/nar/article/48/10/e55/5810485", + paper_year=2021, +) +def sca(adata, test=False): + return liana(adata, method="sca", score_col="LRscore", ascending=False, test=test) diff --git a/openproblems/tasks/_cell_cell_communication/_common/metrics/__init__.py b/openproblems/tasks/_cell_cell_communication/_common/metrics/__init__.py new file mode 100644 index 0000000000..e7de268379 --- /dev/null +++ b/openproblems/tasks/_cell_cell_communication/_common/metrics/__init__.py @@ -0,0 +1 @@ +from .odds_ratio import odds_ratio diff --git a/openproblems/tasks/_cell_cell_communication/_common/metrics/odds_ratio.py b/openproblems/tasks/_cell_cell_communication/_common/metrics/odds_ratio.py new file mode 100644 index 0000000000..0beaab2335 --- /dev/null +++ b/openproblems/tasks/_cell_cell_communication/_common/metrics/odds_ratio.py @@ -0,0 +1,31 @@ +from .....tools.decorators import metric + +import numpy as np +import scipy.stats as stats + + +@metric(metric_name="Odds Ratio", maximize=True) +def odds_ratio(adata, merge_keys, top_n=100): + # Join benchmark (assumed truth) and ccc results + # Get /w ccc_target and a response [0, 1] column + gt = adata.uns["ccc_target"].merge( + adata.uns["ccc_pred"], on=merge_keys, how="right" + ) + gt = gt[gt["response"].notna()] + + # assign the top rank interactions to 1 + a = np.zeros(len(gt["score"])) + a[0:top_n] = 1 + gt.loc[:, ["top_n"]] = a + + # Shape to contingency table + table = np.array(gt.pivot_table(index=["top_n", "response"], aggfunc="size")) + + # if positive or negative class is not in top_n + if table.shape != (4,): + return 1 + + # Fisher ET + oddsratio, _ = stats.fisher_exact(table.reshape(2, 2)) + + return oddsratio diff --git a/openproblems/tasks/_cell_cell_communication/_common/utils.py b/openproblems/tasks/_cell_cell_communication/_common/utils.py new file mode 100644 index 0000000000..25c43187ec --- /dev/null +++ b/openproblems/tasks/_cell_cell_communication/_common/utils.py @@ -0,0 +1,107 @@ +from typing import Union + +import anndata +import collections +import numpy as np +import pandas as pd +import pathlib +import scipy.sparse +import scprep.run + +# Helper function to obtain and convert a Ligand-Receptor resource +ligand_receptor_resource = scprep.run.RFunction( + args="target_organism", + body=""" + HUMAN <- 9606 + if (target_organism != HUMAN) { + op_resource <- liana::generate_homologs( + op_resource = liana::select_resource('Consensus')[[1]], + target_organism = target_organism + ) + } else { + op_resource <- liana::select_resource('Consensus')[[1]] + } + + dplyr::mutate( + op_resource, + ligand_genesymbol = source_genesymbol, + receptor_genesymbol = target_genesymbol + ) + """, +) + + +def map_gene_symbols(adata, map_filename: Union[str, pathlib.Path]): + """Maps gene symbols from aliases to standard symbols + + Genes that map many-to-one are summed. + Genes that map one-to-many are duplicated. + + Parameters + ---------- + adata : anndata.AnnData + map_filename : PathLike + Path to csv containing gene symbol map with two columns, `gene` and `alias` + + Returns + ------- + adata : anndata.AnnData + """ + map_df = pd.read_csv(map_filename) + var = adata.var.rename_axis("alias", axis=0)[[]] + gene_match_idx = np.isin(var.index, map_df["gene"]) + var_gene_match, var = var.loc[gene_match_idx].copy(), var.loc[~gene_match_idx] + alias_match_idx = np.isin(var.index, map_df["alias"]) + var_alias_match, var_no_map = ( + var.loc[alias_match_idx].copy(), + var.loc[~alias_match_idx].copy(), + ) + + # fill 'gene' column + var_alias_match = var_alias_match.reset_index().merge( + map_df, on="alias", how="left" + ) + var_gene_match["gene"] = var_gene_match.index + var_no_map["gene"] = var_no_map.index + + var_dealiased = pd.concat( + [var_gene_match.reset_index(), var_no_map.reset_index(), var_alias_match] + ) + duplicate_idx = var_dealiased["gene"].duplicated(keep=False) + var_dealiased_many_to_one, var_dealiased_one_to_any = ( + var_dealiased.loc[duplicate_idx], + var_dealiased.loc[~duplicate_idx], + ) + + adata_one_to_any = adata[:, var_dealiased_one_to_any["alias"]] + adata_one_to_any.var.index = var_dealiased_one_to_any["gene"] + + many_to_one_genes = var_dealiased_many_to_one["gene"].unique() + many_to_one_X = [] + many_to_one_layers = collections.defaultdict(list) + for gene in var_dealiased_many_to_one["gene"].unique(): + gene_aliases = var_dealiased_many_to_one.loc[ + var_dealiased_many_to_one["gene"] == gene, "alias" + ] + adata_gene = adata[:, gene_aliases] + many_to_one_X.append(scipy.sparse.coo_matrix(adata_gene.X.sum(axis=1))) + for layer_name, layer in adata_gene.layers.items(): + many_to_one_layers[layer_name].append( + scipy.sparse.coo_matrix(adata_gene.X.sum(axis=1)) + ) + + return anndata.AnnData( + X=scipy.sparse.hstack([adata_one_to_any.X] + many_to_one_X), + obs=adata.obs, + var=pd.DataFrame( + index=np.concatenate([adata_one_to_any.var.index, many_to_one_genes]) + ), + layers={ + layer_name: scipy.sparse.hstack( + [adata_one_to_any.layers[layer_name]] + many_to_one_layers[layer_name] + ) + for layer_name in adata.layers + }, + uns=adata.uns, + obsm=adata.obsm, + ) diff --git a/openproblems/tasks/_cell_cell_communication/cell_cell_communication_ligand_target/README.md b/openproblems/tasks/_cell_cell_communication/cell_cell_communication_ligand_target/README.md new file mode 100644 index 0000000000..501284657a --- /dev/null +++ b/openproblems/tasks/_cell_cell_communication/cell_cell_communication_ligand_target/README.md @@ -0,0 +1,115 @@ +# Cell-cell Communication + +## The task + +The growing availability of single-cell data has sparked an increased +interest in the inference of cell-cell communication (CCC), +with an ever-growing number of computational tools developed for this purpose. + +Different tools propose distinct preprocessing steps with diverse +scoring functions, that are challenging to compare and evaluate. +Furthermore, each tool typically comes with its own set of prior knowledge. +To harmonize these, [Dimitrov et +al, 2022](https://doi.org/10.1038/s41467-022-30755-0) recently developed the +[LIANA](https://github.com/saezlab/liana) framework, which was used +as a foundation for this task. + +The challenges in evaluating the tools are further exacerbated by the +lack of a gold standard to benchmark the performance of CCC methods. In an +attempt to address this, Dimitrov et al use alternative data modalities, including +the spatial proximity of cell types and inferred +downstream cytokine activities, to generate an inferred ground truth. However, +these modalities are only approximations of biological reality and come +with their own assumptions and limitations. In time, the inclusion of more +datasets with known ground truth interactions will become available, from +which the limitations and advantages of the different CCC methods will +be better understood. + +**This subtask evaluates the methods' ability to predict interactions, +the corresponding of cytokines of which, are inferred to be active in +the target cell types. This subtask focuses +on the prediction of interactions from steady-state, or single-context, +single-cell data.** + +## The metrics + +Metrics for cell-cell communication aim to characterize how good are +the different scoring methods at prioritizing assumed truth predictions. + +* **Odds ratio**: The odds ratio represents the ratio of true and false +positives within a set of prioritized interactions (top ranked hits) versus +the same ratio for the remainder of the interactions. Thus, in this +scenario odds ratios quantify the strength of association between the +ability of methods to prioritize interactions and those interactions +assigned to the positive class. + +## API + +### Datasets + +Datasets should include cell type annotations in `adata.obs["label"]`, along with some +assumed truth in `adata.uns["ccc_target"]`. The assumed truth could be derived from +various proxies; we refer the reader to [Dimitrov et +al](https://doi.org/10.1038/s41467-022-30755-0) for more details. + +`adata.uns["ccc_target"]` should be a Pandas DataFrame containing all the following +columns: + +* `response`: `int`, binary response variable indicating whether an interaction is + assumed to have occurred +* `ligand`: `str`, gene symbol of the ligand in an interaction +* `target`: `str`, name of target cell type in interaction + +The datasets should also include a +[NCBI taxonomy ID](https://www.ncbi.nlm.nih.gov/Taxonomy/Browser/wwwtax.cgi) +in `adata.uns["target_organism"]` - used to convert the (typically human) prior +knowledge of the CCC methods to the corresponding gene homologs. +`adata.X` should contain the raw counts matrix. + +### Methods + +Methods should predict interactions between cell types without using +`adata.uns["ccc_target"]`. Predicted interactions should be stored in +`adata.uns["ccc_pred"]` as a Pandas DataFrame containing all of the following columns: + +* `score`: `float`, score between `-inf` to `+inf` giving a predicted strength of the + inferred interaction +* `ligand`: `str`, gene symbol of the ligand in an interaction +* `target`: `str`, name of target cell type in interaction + +Methods should infer a score for each _intersecting interaction_ in the harmonized +prior-knowledge resource provided by LIANA. We define _intersecting interactions_ as +those for which the relevant genes are both present in the dataset and the resource. + +The prior-knowledge resource is available via the +`cell_cell_communication.utils.ligand_receptor_resource` function, which returns a +DataFrame containing the columns `ligand_genesymbol` and `receptor_genesymbol`, which +correspond to the ligand and receptor genes, respectively. These may contain complexes +with subunits separated with `_`. Hence, **methods should be able to deal with +complex-containing interactions**. + +## Prior-knowledge + +Each dataset should be supplemented with a prior knowledge resource of +ligand-receptor interactions, with matching feature IDs. +The resource used in the [Ligand-Target](./cell_cell_communication_ligand_target) +and [Source-Target](./cell_cell_communication_source_target) +tasks was generated as the consensus from multiple manually-curated human +ligand-receptor resources, and includes interactions from +[CellPhoneDB](https://www.nature.com/articles/s41596-020-0292-x), +[CellChatDB](https://www.nature.com/articles/s41467-021-21246-9#disqus_thread), +[ICELLNET](https://www.nature.com/articles/s41467-021-21244-x), +[connectomeDB2020](https://www.nature.com/articles/s41467-020-18873-z), +and [CellTalkDB](https://www.nature.com/articles/s41467-020-18873-z) resources. +All of these were queried via the +[OmniPath database](https://www.embopress.org/doi/full/10.15252/msb.20209923), +and are fixed for each version of LIANA. + +To ensure the consistency between the IDs in the dataset and those in the +resource we use a reference map, obtained via BioConductor-v3.15 `org.Hs.eg.db`, +and are provided in `tnbc_wu2021_gene_symbols.csv`. + +### Metrics + +Metrics should evaluate the concordance between `adata.uns["ccc_target"]` and +`adata.uns["ccc_pred"]` to evaluate the success of a method in predicting interactions. diff --git a/openproblems/tasks/_cell_cell_communication/cell_cell_communication_ligand_target/__init__.py b/openproblems/tasks/_cell_cell_communication/cell_cell_communication_ligand_target/__init__.py new file mode 100644 index 0000000000..8ba557ea16 --- /dev/null +++ b/openproblems/tasks/_cell_cell_communication/cell_cell_communication_ligand_target/__init__.py @@ -0,0 +1,14 @@ +from ....utils import get_callable_members +from . import api +from . import datasets +from . import methods +from . import metrics + +_task_name = "Cell-Cell Communication Inference (Ligand-Target)" +_task_summary = "Detect interactions between ligands and target cell types" + +DEFAULT_LAYER = "counts" + +DATASETS = get_callable_members(datasets) +METHODS = get_callable_members(methods) +METRICS = get_callable_members(metrics) diff --git a/openproblems/tasks/_cell_cell_communication/cell_cell_communication_ligand_target/api.py b/openproblems/tasks/_cell_cell_communication/cell_cell_communication_ligand_target/api.py new file mode 100644 index 0000000000..087d0f510c --- /dev/null +++ b/openproblems/tasks/_cell_cell_communication/cell_cell_communication_ligand_target/api.py @@ -0,0 +1,15 @@ +from ....tools.decorators import dataset +from .._common import api + +import functools + +MERGE_KEYS = ["ligand", "target"] + +check_dataset = functools.partial(api.check_dataset, merge_keys=MERGE_KEYS) +check_method = functools.partial(api.check_method, merge_keys=MERGE_KEYS) +sample_method = functools.partial(api.sample_method, merge_keys=MERGE_KEYS) + + +@dataset() +def sample_dataset(): + return api.sample_dataset(merge_keys=MERGE_KEYS) diff --git a/openproblems/tasks/_cell_cell_communication/cell_cell_communication_ligand_target/datasets/__init__.py b/openproblems/tasks/_cell_cell_communication/cell_cell_communication_ligand_target/datasets/__init__.py new file mode 100644 index 0000000000..5e4181d2d2 --- /dev/null +++ b/openproblems/tasks/_cell_cell_communication/cell_cell_communication_ligand_target/datasets/__init__.py @@ -0,0 +1 @@ +from .tnbc_wu2021 import tnbc_data diff --git a/openproblems/tasks/_cell_cell_communication/cell_cell_communication_ligand_target/datasets/tnbc_wu2021.py b/openproblems/tasks/_cell_cell_communication/cell_cell_communication_ligand_target/datasets/tnbc_wu2021.py new file mode 100644 index 0000000000..275a24edb2 --- /dev/null +++ b/openproblems/tasks/_cell_cell_communication/cell_cell_communication_ligand_target/datasets/tnbc_wu2021.py @@ -0,0 +1,27 @@ +from .....data.tnbc_wu2021 import load_tnbc_data +from .....tools.decorators import dataset +from ..._common.utils import ligand_receptor_resource +from ..._common.utils import map_gene_symbols + +import pathlib + + +@dataset( + "Triple negative breast cancer atlas", + data_url=load_tnbc_data.metadata["data_url"], + data_reference=load_tnbc_data.metadata["data_reference"], + dataset_summary="A single-cell atlas of human breast cancers with inferred " + "cytokine activities as assumed true cell-cell communication. Cytokine " + "activities were estimated by fitting a multivariate linear model with " + "cytokine-focused signatures (see Dimitrov et al., 2022).", + image="openproblems-r-extras", +) +def tnbc_data(test=False): + adata = load_tnbc_data(test=test) + adata = map_gene_symbols( + adata, pathlib.Path(__file__).parent.joinpath("tnbc_wu2021_gene_symbols.csv") + ) + adata.uns["ligand_receptor_resource"] = ligand_receptor_resource( + adata.uns["target_organism"] + ) + return adata diff --git a/openproblems/tasks/_cell_cell_communication/cell_cell_communication_ligand_target/datasets/tnbc_wu2021_gene_symbols.csv b/openproblems/tasks/_cell_cell_communication/cell_cell_communication_ligand_target/datasets/tnbc_wu2021_gene_symbols.csv new file mode 100644 index 0000000000..570a29aed8 --- /dev/null +++ b/openproblems/tasks/_cell_cell_communication/cell_cell_communication_ligand_target/datasets/tnbc_wu2021_gene_symbols.csv @@ -0,0 +1,71364 @@ +gene,alias +A1BG,A1B +A1BG,ABG +A1BG,GAB +A1BG,HYST2477 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+GATA3,HDR +GATA3,HDRS +GATA4,ASD2 +GATA4,TACHD +GATA4,TOF +GATA4,VSD1 +GATM,AGAT +GATM,AT +GATM,CCDS3 +GATM,FRTS1 +GBA,GBA1 +GBA,GCB +GBA,GLUC +GBAP1,Beta-GC +GBAP1,GBA +GBAP1,GBAP +GBAP1,GC +GBAP1,GLUC +NIPSNAP2,GBAS +GBE1,APBD +GBE1,GBE +GBE1,GSD4 +GBX1,Huh-17 +GC,DBP +GC,DBP-maf +GC,DBP/GC +GC,GRD3 +GC,Gc-MAF +GC,GcMAF +GC,HEL-S-51 +GC,VDB +GC,VDBG +GC,VDBP +GCDH,ACAD5 +GCDH,GCD +GCG,GLP-1 +GCG,GLP1 +GCG,GLP2 +GCG,GRPP +GCGR,GGR +GCGR,GL-R +GCGR,MVAH +GCH1,DYT14 +GCH1,DYT5 +GCH1,DYT5a +GCH1,GCH +GCH1,GTP-CH-1 +GCH1,GTPCH1 +GCH1,HPABH4B +GCHFR,GFRP +GCHFR,HsT16933 +GCHFR,P35 +GCK,FGQTL3 +GCK,GK +GCK,GLK +GCK,HHF3 +GCK,HK4 +GCK,HKIV +GCK,HXKP +GCK,LGLK +GCK,MODY2 +GCK,PNDM1 +GCKR,FGQTL5 +GCKR,GKRP +BLOC1S1,BLOS1 +BLOC1S1,BORCS1 +BLOC1S1,GCN5L1 +BLOC1S1,MICoA +BLOC1S1,RT14 +KAT2A,GCN5 +KAT2A,GCN5L2 +KAT2A,PCAF-b +KAT2A,hGCN5 +NR6A1,CT150 +NR6A1,GCNF +NR6A1,GCNF1 +NR6A1,NR61 +NR6A1,RTR +NR6A1,hGCNF +NR6A1,hRTR +GCNT1,C2GNT +GCNT1,C2GNT-L +GCNT1,C2GNT1 +GCNT1,G6NT +GCNT1,"NACGT2" +GCNT1,"NAGCT2" +GCNT2,CCAT +GCNT2,CTRCT13 +GCNT2,GCNT2C +GCNT2,GCNT5 +GCNT2,IGNT +GCNT2,II +GCNT2,"NACGT1" +GCNT2,"NAGCT1" +GCNT2,ULG3 +GCNT2,bA360O19.2 +GCNT2,bA421M1.1 +OPN1MW,CBBM +OPN1MW,CBD +OPN1MW,COD5 +OPN1MW,GCP +OPN1MW,GOP +OPN1MW,OPN1MW1 +GCSH,GCE +GCSH,NKH +GCY,STA +GCY,TS +GCY,TSY +GDF1,CERS1 +GDF1,CHTD6 +GDF1,DORV +GDF1,DTGA3 +GDF1,LAG1 +GDF1,LASS1 +GDF1,RAI +GDF1,UOG1 +GDF2,BMP-9 +GDF2,BMP9 +GDF2,HHT5 +MSTN,GDF8 +MSTN,MSLHP +GDF9,POF14 +GDF10,BIP +GDF10,BMP-3b +GDF10,BMP3B +GDI1,1A +GDI1,GDIL +GDI1,MRX41 +GDI1,MRX48 +GDI1,OPHN2 +GDI1,RABGD1A +GDI1,RABGDIA +GDI1,XAP-4 +GDI1,XLID41 +GDI2,HEL-S-46e +GDI2,RABGDIB +GDI2P1,GDI2P +GDNF,ATF +GDNF,ATF1 +GDNF,ATF2 +GDNF,HFB1-GDNF +GDNF,HSCR3 +GEM,KIR +GFAP,ALXDRD +GFER,ALR +GFER,ERV1 +GFER,HERV1 +GFER,HPO +GFER,HPO1 +GFER,HPO2 +GFER,HSS +GFER,MMCHD +GFER,MPMCD +GFI1,GFI-1 +GFI1,GFI1A +GFI1,SCN2 +GFI1,ZNF163 +GFPT1,CMS12 +GFPT1,CMSTA1 +GFPT1,GFA +GFPT1,GFAT +GFPT1,GFAT 1 +GFPT1,GFAT1 +GFPT1,GFAT1m +GFPT1,GFPT +GFPT1,GFPT1L +GFPT1,MSLG +GFRA1,GDNFR +GFRA1,GDNFRA +GFRA1,GFR-ALPHA-1 +GFRA1,GFRalpha-1 +GFRA1,RET1L +GFRA1,RETL1 +GFRA1,TRNR1 +GFRA2,GDNFRB +GFRA2,NRTNR-ALPHA +GFRA2,NTNRA +GFRA2,RETL2 +GFRA2,TRNR2 +GFRA3,GDNFR3 +GGCX,VKCFD1 +GGT1,CD224 +GGT1,D22S672 +GGT1,D22S732 +GGT1,GGT +GGT1,GGT 1 +GGT1,GGTD +GGT1,GTG +GGT3P,GGT3 +GGTA1,GGTA +GGTA1,GGTA1P +GGTA1,GLYT2 +GGTA1,a1/3GTP +B4GALT1,B4GAL-T1 +B4GALT1,CDG2D +B4GALT1,GGTB2 +B4GALT1,GT1 +B4GALT1,GTB +B4GALT1,beta4Gal-T1 +GGT7,D20S101 +GGT7,GGT4 +GGT7,GGTL3 +GGT7,GGTL5 +GGT5,GGL +GGT5,GGT 5 +GGT5,GGT-REL +GGT5,GGTLA1 +GH1,GH +GH1,GH-N +GH1,GHB5 +GH1,GHN +GH1,IGHD1A +GH1,IGHD1B +GH1,IGHD2 +GH1,hGH-N +GH2,GH-V +GH2,GHB2 +GH2,GHL +GH2,GHV +GH2,hGH-V +GHR,GHBP +GHR,GHIP +GHRH,GHRF +GHRH,GRF +GHRH,INN +GHRHR,GHRFR +GHRHR,GRFR +GHRHR,IGHD1B +GHRHR,IGHD4 +GHSR,GHDP +CBLIF,GIF +CBLIF,IF +CBLIF,IFMH +CBLIF,INF +CBLIF,TCN3 +GIPR,PGQTL2 +GJA1,AVSD3 +GJA1,CMDR +GJA1,CX43 +GJA1,EKVP +GJA1,EKVP3 +GJA1,GJAL +GJA1,HLHS1 +GJA1,HSS +GJA1,ODDD +GJA1,PPKCA +GJA3,CTRCT14 +GJA3,CX46 +GJA3,CZP3 +GJA4,CX37 +GJA5,ATFB11 +GJA5,CX40 +GJA8,CAE +GJA8,CAE1 +GJA8,CTRCT1 +GJA8,CX50 +GJA8,CZP1 +GJA8,MP70 +GJB1,CMTX +GJB1,CMTX1 +GJB1,CX32 +GJB2,BAPS +GJB2,CX26 +GJB2,DFNA3 +GJB2,DFNA3A +GJB2,DFNB1 +GJB2,DFNB1A +GJB2,HID +GJB2,KID +GJB2,NSRD1 +GJB2,PPK +GJB3,CX31 +GJB3,DFNA2 +GJB3,DFNA2B +GJB3,EKV +GJB3,EKVP1 +GJB5,CX31.1 +GK,GK1 +GK,GKD +GK2,GKP2 +GK2,GKTA +GK3P,GKP3 +GK3P,GKTB +GK4P,GKP6 +GLA,GALA +GPC3,DGSX +GPC3,GTR2-2 +GPC3,MXR7 +GPC3,OCI-5 +GPC3,SDYS +GPC3,SGB +GPC3,SGBS +GPC3,SGBS1 +GLB1,EBP +GLB1,ELNR1 +GLB1,MPS4B +GCLC,GCL +GCLC,GCS +GCLC,GLCL +GCLC,GLCLC +GCLM,GLCLR +GLDC,GCE +GLDC,GCSP +GLDC,HYGN1 +GLDCP1,GLDCP +GLE1,CAAHC +GLE1,CAAHD +GLE1,GLE1L +GLE1,LCCS +GLE1,LCCS1 +GLE1,hGLE1 +GLG1,CFR-1 +GLG1,ESL-1 +GLG1,MG-160 +GLG1,MG160 +GLI1,GLI +GLI1,PAPA8 +GLI1,PPD1 +GLI2,CJS +GLI2,HPE9 +GLI2,PHS2 +GLI2,THP1 +GLI2,THP2 +GLI3,ACLS +GLI3,GCPS +GLI3,GLI3-190 +GLI3,GLI3FL +GLI3,PAP-A +GLI3,PAPA +GLI3,PAPA1 +GLI3,PAPB +GLI3,PHS +GLI3,PPDIV +GLI4,HKR4 +GLI4,ZNF928 +GLO1,GLOD1 +GLO1,GLYI +GLO1,HEL-S-74 +GLP1R,GLP-1 +GLP1R,GLP-1-R +GLP1R,GLP-1R +GLRA1,HKPX1 +GLRA1,STHE +GLRA2,GLR +GLRB,HKPX2 +GLS,AAD20 +GLS,CASGID +GLS,DEE71 +GLS,EIEE71 +GLS,GAC +GLS,GAM +GLS,GDPAG +GLS,GLS1 +GLS,KGA +GLRX,GRX +GLRX,GRX1 +GLUD1,GDH +GLUD1,GDH1 +GLUD1,GLUD +GLUD2,GDH2 +GLUD2,GLUDP1 +GLUD1P3,C10orf102 +GLUD1P3,GLUDP3 +GLUD1P4,GLUDP4 +GLUD1P5,GLUDP5 +GLUL,GLNS +GLUL,GS +GLUL,PIG43 +GLUL,PIG59 +GLULP1,GLULL1 +GLULP2,GLULL2 +GM2A,GM2-AP +GM2A,SAP-3 +GM2AP1,GM2AP +GMDS,GMD +GMDS,SDR3E1 +GMFB,GMF +GML,LY6DL +GMPR,GMPR 1 +GMPR,GMPR1 +GNA11,FBH +GNA11,FBH2 +GNA11,FHH2 +GNA11,GNA-11 +GNA11,HHC2 +GNA11,HYPOC2 +GNA12,NNX3 +GNA12,RMP +GNA12,gep +GNA15,GNA16 +GNAI1,Gi +GNAI2,GIP +GNAI2,GNAI2B +GNAI2,H_LUCA15.1 +GNAI2,H_LUCA16.1 +GNAI2P1,GNAI2A +GNAI2P1,GNAI2L +GNAI3,87U6 +GNAI3,ARCND1 +GNAL,DYT25 +GNAO1,DEE17 +GNAO1,EIEE17 +GNAO1,G-ALPHA-o +GNAO1,GNAO +GNAO1,HLA-DQB1 +GNAO1,NEDIM +GNAQ,CMC1 +GNAQ,G-ALPHA-q +GNAQ,GAQ +GNAQ,SWS +GNAQP1,GNAQP +GNAS,AHO +GNAS,C20orf45 +GNAS,GNAS1 +GNAS,GPSA +GNAS,GSA +GNAS,GSP +GNAS,NESP +GNAS,PITA3 +GNAS,POH +GNAS,SCG6 +GNAS,SgVI +GNAT1,CSNB1G +GNAT1,CSNBAD3 +GNAT1,GBT1 +GNAT1,GNATR +GNAT2,ACHM4 +GNAT2,GNATC +GNAZ,gz-alpha +GNB1,MDS +GNB1,MRD42 +GNB2,SSS4 +GNB2,SSS4; NEDHYDF +GNB3,CSNB1H +GNG5P1,GNG5P +GNG11,GNGT11 +GNGT1,GNG1 +GNGT2,G-GAMMA-8 +GNGT2,G-GAMMA-C +GNGT2,GNG9 +GNGT2,GNGT8 +GNL1,HSR1 +GNRH1,GNRH +GNRH1,GRH +GNRH1,LHRH +GNRH1,LNRH +GNRH2,GnRH-II +GNRH2,LH-RHII +GNRHR,GNRHR1 +GNRHR,GRHR +GNRHR,HH7 +GNRHR,LHRHR +GNRHR,LRHR +GNS,G6S +GOLGA1,golgin-97 +GOLGA2,GM130 +GOLGA3,GCP170 +GOLGA3,MEA-2 +GOLGA4,CRPF46 +GOLGA4,GCP2 +GOLGA4,GOLG +GOLGA4,MU-RMS-40.18 +GOLGA4,p230 +GOLGB1,GCP +GOLGB1,GCP372 +GOLGB1,GOLIM1 +GOT1,AST1 +GOT1,ASTQTL1 +GOT1,GIG18 +GOT1,cAspAT +GOT1,cCAT +GOT2,DEE82 +GOT2,KAT4 +GOT2,KATIV +GOT2,KYAT4 +GOT2,mitAAT +SFN,YWHAS +GP1BA,BDPLT1 +GP1BA,BDPLT3 +GP1BA,BSS +GP1BA,CD42B +GP1BA,CD42b-alpha +GP1BA,DBPLT3 +GP1BA,GP1B +GP1BA,GPIbA +GP1BA,GPIbalpha +GP1BA,VWDP +GP1BB,BDPLT1 +GP1BB,BS +GP1BB,CD42C +GP1BB,GPIBB +GP1BB,GPIbbeta +GP2,ZAP75 +GP5,CD42d +GP5,GPV +GP9,CD42a +GP9,GPIX +GPC1,glypican +GPD1,GPD-C +GPD1,GPDH-C +GPD1,HTGTI +GPD2,GDH2 +GPD2,GPDM +GPD2,mGDH +GPD2,mGPDH +GPI,AMF +GPI,GNPI +GPI,NLK +GPI,PGI +GPI,PHI +GPI,SA-36 +GPI,SA36 +GPLD1,GPIPLD +GPLD1,GPIPLDM +GPLD1,PIGPLD +GPLD1,PIGPLD1 +GPLD1,PLD +GPM6A,GPM6 +GPM6A,M6A +GPM6B,M6B +CMKLR2,GPR1 +CCR10,GPR2 +GPR3,ACCA +GPR4,GPR6C.l +XCR1,CCXCR1 +XCR1,GPR5 +NPBWR1,GPR7 +NPBWR2,GPR8 +CXCR3,CD182 +CXCR3,CD183 +CXCR3,CKR-L2 +CXCR3,CMKAR3 +CXCR3,GPR9 +CXCR3,IP10-R +CXCR3,Mig-R +CXCR3,MigR +PRLHR,GPR10 +PRLHR,GR3 +PRLHR,PrRPR +GPR12,GPCR12 +GPR12,GPCR21 +GPR12,PPP1R84 +UTS2R,GPR14 +UTS2R,UR-2-R +UTS2R,UTR +UTS2R,UTR2 +GPR15,BOB +LPAR4,GPR23 +LPAR4,LPA4 +LPAR4,P2RY9 +LPAR4,P2Y5-LIKE +LPAR4,P2Y9 +MCHR1,GPR24 +MCHR1,MCH-1R +MCHR1,MCH1R +MCHR1,SLC-1 +MCHR1,SLC1 +GPR27,SREB1 +GPER1,CEPR +GPER1,CMKRL2 +GPER1,DRY12 +GPER1,FEG-1 +GPER1,GPCR-Br +GPER1,GPER +GPER1,GPR30 +GPER1,LERGU +GPER1,LERGU2 +GPER1,LyGPR +GPER1,mER +GPR31,12-HETER +GPR31,HETER +GPR31,HETER1 +GPR32,RVDR1 +GPR32P1,GPR32P +GPR32P1,hGPCR39 +GPR34,LYPSR1 +GPR37,EDNRBL +GPR37,PAELR +GPR37,hET(B)R-LP +MLNR,GPR38 +MLNR,MTLR1 +FFAR1,FFA1R +FFAR1,GPCR40 +FFAR1,GPR40 +FFAR3,FFA3R +FFAR3,GPR41 +FFAR3,GPR42 +GPR42,FFAR1L +GPR42,FFAR3L +GPR42,GPR41L +GPR42,GPR42P +FFAR2,FFA2R +FFAR2,GPR43 +GRK4,GPRK2L +GRK4,GPRK4 +GRK4,GRK4a +GRK4,IT11 +GRK5,FP2025 +GRK5,GPRK5 +GRK6,GPRK6 +GRK6P1,GPRK6L +GRK6P1,GPRK6P +GRK6P1,GRK6PS +MKNK2,GPRK7 +MKNK2,MNK2 +GPS1,COPS1 +GPS1,CSN1 +GPS1,SGN1 +GPS2,AMF-1 +GPT,AAT1 +GPT,ALT1 +GPT,GPT1 +GPX1,GPXD +GPX1,GSHPX1 +GPX2,GI-GPx +GPX2,GPRP +GPX2,GPRP-2 +GPX2,GPx-2 +GPX2,GPx-GI +GPX2,GSHPX-GI +GPX2,GSHPx-2 +GPX3,GPx-P +GPX3,GSHPx-3 +GPX3,GSHPx-P +GPX4,GPx-4 +GPX4,GSHPx-4 +GPX4,MCSP +GPX4,PHGPx +GPX4,SMDS +GPX4,snGPx +GPX4,snPHGPx +GPX5,EGLP +GPX5,GPx-5 +GPX5,GSHPx-5 +GPX5,HEL-S-75p +GPX7,CL683 +GPX7,GPX6 +GPX7,GPx-7 +GPX7,GSHPx-7 +GPX7,NPGPx +GPX1P2,GPXL1 +GPX1P2,GPXL2 +GPX1P2,GPXP2 +GPX1P2,GPXP2P +GRB2,ASH +GRB2,EGFRBP-GRB2 +GRB2,Grb3-3 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+GLB1L2,MST114 +GLB1L2,MSTP114 +KLC4,KNSL8 +KLC4,bA387M24.3 +SAPCD2,C9orf140 +SAPCD2,p42.3 +RSPRY1,SEMDFA +DPH6,ATPBD4 +MIDN,Stx +KIR3DX1,KIR3DL0 +KIR3DX1,LENG12 +UBE3D,C6orf157 +UBE3D,H10BH +UBE3D,UBE2CBP +UBE3D,YJR141W +FAM181A,C14orf152 +CCDC120,JM11 +ZNF30,KOX28 +PHLDB2,LL5b +PHLDB2,LL5beta +TMEM250,C9orf69 +TSR2,DBA14 +TSR2,DT1P1A10 +TSR2,WGG1 +KRT8P12,KRT8L2 +KCNH7,ERG3 +KCNH7,HERG3 +KCNH7,Kv11.3 +BTBD6,BDPL +TSPAN18,TSPAN +EFCAB11,C14orf143 +HS6ST2,MRXSPM +FRMD7,NYS +FRMD7,NYS1 +FRMD7,XIPAN +EMILIN3,C20orf130 +EMILIN3,EMILIN5 +EMILIN3,dJ620E11.4 +RPL7P6,RPL7_20_1216 +SYS1,C20orf169 +SYS1,dJ453C12.4 +SYS1,dJ453C12.4.1 +WFDC8,C20orf170 +WFDC8,HEL-S-292 +WFDC8,WAP8 +WFDC8,dJ461P17.1 +SNX21,C20orf161 +SNX21,PP3993 +SNX21,SNX-L +SNX21,SNXL +SNX21,dJ337O18.4 +ZSWIM1,C20orf162 +UCN2,SRP +UCN2,UCN-II +UCN2,UCNI +UCN2,UR +UCN2,URP +UNC5A,UNC5H1 +OTULIN,AIPDS +OTULIN,FAM105B +OTULIN,GUM +OLMALINC,C10orf75 +OLMALINC,HI-LNC80 +OLMALINC,LINC00263 +OLMALINC,NCRNA00263 +OLMALINC,OLMALINCAS 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+IQANK1,onco-lncRNA-3 +VDAC1P1,VDAC1P +MIR205HG,LINC00510 +MIR205HG,NPC-A-5 +EIF5AP4,EIF5AL3 +AKAIN1,C18orf42 +TRIM49C,TRIM49L2 +UBTFL1,C11orf27 +UBTFL1,HMGPI +RAD21L1,RAD21L +RAD21L1,dJ545L17.2 +RPSAP54,RPSA_26_1300 +FAM230E,LINC01662 +SCX,SCXA +SCX,SCXB +SCX,bHLHa48 +RPS11P7,RPS11_4_1771 +RPL21P69,RPL21_27_774 +RPL9P11,RPL9_1_9 +RPL3P7,RPL3_3_732 +NPIPA2,NPIP +NPIPA2,NPIPA1 +API5P1,API5L1 +ZNF487,KRBO1 +ZNF487,ZNF487P +BMS1P2,BMS1LP2 +BMS1P2,BMS1LP6 +BMS1P2,BMS1P6 +BMS1P2,bA144G6.1 +RPL6P10,RPL6_9_488 +MEP1AP1,MEP1AL1 +LINC00205,NCRNA00205 +SPATA42,AKNAD1-AS1 +SPATA42,SRG7 +RPS29P3,RPS29_7_451 +RPS29P3,bcm2758 +NIFKP8,MKI67IPP8 +CIBAR1-DT,LINC00535 +CERNA2,HOST2 +CERNA2,lncRNA-HOST2 +INSYN2A,C10orf141 +INSYN2A,FAM196A +INSYN2A,INSYN2 +FLVCR1-DT,FLVCR1-AS1 +FLVCR1-DT,LQK1 +FLVCR1-DT,NCRNA00292 +RBBP4P1,RBBP4P +FABP5P9,FABP5L9 +FAM163B,C9orf166 +GRIK1-AS1,C21orf9 +GRIK1-AS1,GRIK1-AS +GRIK1-AS1,GRIK1AS +GRIK1-AS1,NCRNA00110 +DUX4L17,DUXY3 +RPS26P28,RPS26_11_544 +RPL36AP8,RPL36A_20_1424 +SMIM5,C17orf109 +SMIM5,PP12104 +DUX4L19,DUXY4 +C11orf97,LINC01171 +RPL26P22,RPL26_5_860 +ZC3H11B,ZC3HDC11B +SMIM15,C5orf43 +ATF4P3,ATF4C +FAM25A,bA96C23.5 +CCT6P3,CCT6-3P +FADS2B,FADS2P1 +RPL36P8,RPL36_4_490 +RPL3P6,RPL3_2_568 +GRXCR2,DFNB101 +TMEM72,C10orf127 +TMEM72,KSP37 +MAP1LC3B2,ATG8G +CCT6P1,CCT6-5P +CCT6P1,CCT6AP1 +RPL21P106,RPL21_48_1266 +RPS29P18,RPS29_14_993 +HSPD1P1,HSPD1-2P +CT47B1,CT47.13 +CT47B1,CT47A13 +RNASE2CP,ECRP +RPL5P4,RPL5_1_31 +RPS27AP16,RPS27A_8_1488 +LINC00452,LINC00453 +TMEM253,C14orf176 +TMEM253,C14orf95 +TMEM253,NCRNA00220 +SPINK9,LEKTI2 +PURPL,LINC01021 +LIPK,LIPL2 +LIPK,bA186O14.2 +LIPN,ARCI8 +LIPN,LI4 +LIPN,LIPL4 +LIPN,bA186O14.3 +RBMS2P1,RBMS2P +FAM25E,FAM25D +RPL11P5,RPL11_3_1291 +TARDBPP1,TARDBPL +TARDBPP1,bA318P23.1 +RPL7P57,RPL7_22_1777 +RPL29P26,RPL29_14_1268 +KCTD9P2,KCTD9L +RPL12P33,RPL12_13_1292 +EEF1A1P15,EEF1AL13 +NR1H5P,Fxrb +NR1H5P,NR1H5 +RPSAP8,LAMR1P8 +RPSAP8,RPSA_26_1797 +RPS18P8,RPS18_5_650 +HSD17B1P1,EDH17B1 +HSD17B1P1,EDHB17 +HSD17B1P1,HSD17 +HSD17B1P1,HSD17BP1 +HSD17B1P1,SDR28C1P1 +RPL35P8,RPL35_6_1759 +SLC35G6,AMAC1L3 +SLC35G6,TMEM21B +CCDC168,C13orf40 +GOLGA8N,GOLGA8R +CASC16,LINC00918 +SCGB1B2P,SCGB4A1P +LYPLAL1-DT,LYPLAL1-AS1 +CASP4LP,CASP17P +HMGN2P4,HMGN2L4 +TRAF3IP2-AS1,BetaFAAR +TRAF3IP2-AS1,C6UAS +TRAF3IP2-AS1,C6orf3 +TRAF3IP2-AS1,NCRNA00248 +TRAF3IP2-AS1,TRAF3IP2-AS2 +RPLP0P5,RPLP0_1_167 +USP46-DT,USP46-AS1 +KRTAP24-1,KAP24.1 +ZFP62,ZET +ZFP62,ZNF755 +RPL31P32,RPL31_14_719 +SPDYE10,SPDYE10P +RPL17P34,RPL17_10_1081 +KRT126P,KRT +CBLN3,PRO1486 +HMGN2P6,HMGN2L6 +ARL2BPP2,ARL2BPL +ARL2BPP2,bcm1040 +RPL7P27,RPL7_11_720 +SPDYE9,SPDYE9P +CRNDE,CRNDEP +CRNDE,LINC00180 +CRNDE,NCRNA00180 +CRNDE,PNAS-108 +CRNDE,lincIRX5 +MORF4L2P1,MORF4LP4 +MORF4L2P1,MRG4 +RPS3AP20,RPS3A_6_581 +RPLP2P3,RPLP2_2_1170 +RPSAP43,RPSA_12_742 +ZNF733P,ZNF733 +RPS2P18,RPS2_7_293 +RPL7P29,RPL7_13_721 +RPL6P13,RPL6_10_498 +RPS3AP36,RPS3A_16_1083 +FNDC10,C1orf233 +RPS2P26,RPS2_8_601 +ARAFP2,ARAF2 +ARAFP2,ARAF2P +ARAFP2,ARAFPS +ARAFP2,PKS1 +RPS6P3,RPS6_1_251 +HSPD1P10,HSPD1-4P +ZNF734P,ZNF734 +CBWD6,CBWD7 +RPL32P23,RPL32_6_1020 +CTSLP3,CTSL1P3 +CTSLP3,CTSLL3 +RPL22P19,RPL22_8_1294 +RPL7AP62,RPL7A_28_1429 +C18orf63,DKFZP781G0119 +SLC25A5P6,ANTP6 +FAM25C,bA164N7.4 +PYY3,PYY-III +RPS14P3,RPS14_1_16 +BCRP1,BCR-1 +RPL18AP8,RPL18A_3_387 +MSH2-OT1,HCG2040054 +SDHAF1,LYRM8 +SDHAF1,MC2DN2 +RPL29P18,RPL29_8_938 +RPSAP50,RPSA_24_1172 +RPS14P9,RPS14_6_722 +RPL23AP53,RPL23A_20_869 +CCT8P1,CCT8-1P +GOT2P3,GOT2L3 +GOT2P3,GOT2L3P +RPS14P8,RPS14_5_603 +WIPF3,CR16 +DNM1P31,DNM1DN4-2 +DNM1P31,DNM1DN4.2 +DNM1P31,DNM1DN4@ +RPL10AP8,RPL10A_3_573 +BCRP3,BCR3 +BCRP3,BCRL3 +BCRP3,BCRL6 +RPS26P20,RPS26_7_290 +DRGX,DRG11 +DRGX,PRRXL1 +SYCE3,C22orf41 +SYCE3,THEG2 +RPS26P8,RPS26_20_1551 +RPLP2P2,RPLP2_1_633 +RPL12P27,RPL12_9_1087 +TRPC6P,TRPC6L +RPL9P4,RPL9_9_1348 +AURKBP1,AURKBPS1 +AURKBP1,AurBps1 +RPL7AP43,RPL7A_17_815 +MAPK8IP1P1,MAPK8IPP +MAPK8IP1P1,PRKM8IPP +FAM215B,ARL17A-IT1 +RPS20P19,RPS20_7_863 +MT1IP,MT1 +MT1IP,MT1I +MT1IP,MTE +RPS7P11,RPS7_6_1552 +ATP5PBP3,ATP5F1P3 +RPL37AP8,RPL37A_3_1174 +ZCCHC18,PNMA7B +ZCCHC18,SIZN2 +GCNT2P1,GCNT2P +GCNT2P1,GCNT6 +RPL23AP71,RPL23A_33_1349 +RPS2P47,RPS2_25_1553 +LRRC37A17P,LRRC37A5 +DEFB131A,DEFB-31 +DEFB131A,DEFB131 +TMEM30CP,CDC50C +TMEM30CP,TMEM30C +RPSAP61,RPSA_31_1776 +ICE2P1,"NARG2P1" +CTSLP4,CTSL1P4 +CTSLP4,CTSLL4 +CTSLP4,bA564C4.3 +HMGN2P7,HMGN2L7 +RPL39P21,RPL39_9_576 +RBM22P6,RBM22P10 +RPL13AP6,RPL13A_7_1096 +RPL13AP6,bA348N5.5 +RPL31P44,RPL31_14_1046 +NKX2-4,NKX2.4 +NKX2-4,NKX2D +SMIM10,CXorf69 +ELOCP3,TCEB1P3 +ELOCP3,dJ254P11.1 +PPIAL4G,COAS-2 +SMIM10L2B,LINC00086 +SMIM10L2B,LINC00087 +SMIM10L2B,NCRNA00086 +SMIM10L2B,NCRNA00087 +SMIM10L2B,SMIM10L2A +EEF1A1P12,EEF1AL10 +INTS4P2,INTS4L2 +RPL21P107,RPL21_46_1336 +NF1P6,NF1L4 +NF1P6,NF1L6 +RAD21-AS1,C8orf81 +RAD21-AS1,NCRNA00255 +CDRT15P2,CDRT15L1 +BMS1P13,BMS1P14 +IGKV1OR-3,IGKV1/OR-3 +IGKV1OR-3,IGKV1OR3 +IGKV1OR-3,IGKVPZ3 +WASF4P,WASF4 +HSPD1P4,HSPD1-5P +MEP1AP2,MEP1AL2 +RPS15AP11,RPS15A_3_41 +LINC02604,lncHERG +PAK6-AS1,C15orf56 +FAM83G,PAWS1 +EEF1B2P3,EEF1B4 +RPL36AP36,RPL36A_13_1022 +RPS28P3,RPS28_2_172 +MEIG1,SPATA39 +MEIG1,bA2K17.3 +RPL7P7,RPL7_1_7 +RPL32P18,RPL32_8_807 +RPL10P5,RPL10_1_257 +H3P16,H3.6 +H3P16,H3F3AP6 +H3P16,p21 +RPS26P15,RPS26_1_56 +RPS26P50,RPS26_18_1443 +KRT16P3,KERSMCR +RPS3AP26,RPS3A_11_824 +ABHD17AP6,FAM108A7P +SMIM23,C5orf50 +RPL32P22,RPL32_14_1010 +PIK3CD-AS1,C1orf200 +RPS3P6,RPS3_2_1244 +RPS2P20,RPS2_7_524 +GAPDHP59,GAPDHL3 +GAPDHP59,GAPDL3 +GAGE2B,CT4.2 +GAGE2B,GAGE-2 +GAGE2B,GAGE-2B +GAGE13,GAGE-12A +GAGE13,GAGE-13 +GAGE13,GAGE12A +RPL6P4,RPL6_3_194 +GAGE12G,AL4 +GAGE12G,CT4.7 +GAGE12G,GAGE-12G +COL25A1-DT,ZCCHC23 +CLRN2,DFNB117 +PFN1P6,PFN1P7 +ZBED1P1,ZBED1P +RPL21P61,RPL21_24_663 +RPL5P30,RPL5_16_1181 +RPL12P8,RPL12_10_1052 +RPL12P8,bA57G10.3 +RPS2P48,RPS2_26_1559 +RPL21P46,RPL21_14_463 +LOC645188,SPANX-L +LINGO3,LERN2 +LINGO3,LRRN6B +TMEM14DP,TMEM14D +TMEM14DP,bA524O24.3 +EIF3J-DT,EIF3J-AS1 +RPL29P17,RPL29_8_665 +MNX1-AS1,CCAT5 +MNX1-AS1,MAYA +GAPDHP66,GAPDHL11 +GAPDHP66,GAPDL11 +RPL7AP37,RPL7A_15_759 +HMGB1P6,HMG1L6 +HMGB1P6,HMGB1L6 +RPL17P39,RPL17_14_1450 +RPL17P39,hCG2004593 +RPS10P24,RPS10_9_1493 +SCDP1,SCDP +LINC00461,ECONEXIN +LINC00461,EyeLinc1 +LINC00461,NDIME +LINC00461,VISC +RPSAP11,LAMR +RPSAP11,LAMR1P11 +RPSAP11,LAMRL1 +RPSAP11,LAMRP1 +RPSAP11,RPSA_9_357 +CHCHD2P9,C9orf49 +CHCHD2P9,CHCHD9 +MIR3976HG,LINC01469 +PRAMEF26,PRAMEF25 +GGT8P,GGT1P1 +TMEM200C,TTMA +RPL21P76,RPL21_31_868 +PRAMEF33,PRAMEF33P +RPL6P27,RPL6_10_1582 +NIFKP2,MKI67IPP2 +RPS16P9,RPS16_5_1659 +SLC25A51P2,MCART4P +PRAMEF20,PRAMEF21 +ATP5PBP6,ATP5F1P6 +RPL17P6,RPL17_1_64 +ATP5F1AP3,ATP5A1P3 +ATP5F1AP3,ATP5AP3 +RPL21P68,RPL21_25_668 +CEP170P1,CEP170L +CEP170P1,FAM68B +CEP170P1,KIAA0470L +FAM138A,F379 +FAM138A,FAM138F +FAM238A,C10orf50 +FAM238A,LINC00264 +FAM238A,NCRNA00264 +FAM238A,bA128B16.2 +GOT2P1,GOT2L1 +HSPD1P6,HSPD1-6P +HSPD1P6,HSPDP6 +FAM90A22P,FAM90A22 +ATP5F1CP1,ATP5C1P1 +ATP5F1CP1,ATP5C2 +ATP5F1CP1,ATP5CL2 +FAM90A23P,FAM90A23 +RPS4XP19,RPS4P19 +RPS4XP19,RPS4X_9_1583 +LINC00614,C10orf52 +AK4P3,AK3L2 +AK4P3,AK3L2P +RPS13P8,RPS13_5_1432 +WFDC21P,LNCDC +WFDC21P,linc-DC +WFDC21P,lnc-DC +FAM32CP,FAM32C +FAM90A14P,FAM90A14 +MT1XP1,MTL1 +RPL31P48,RPL31_25_1257 +RPSAP56,RPSA_23_1496 +TUFMP1,TUFML +POU5F1P4,OCT3 +POU5F1P4,OCT4 +POU5F1P4,OTF-3 +POU5F1P4,OTF3 +POU5F1P4,Oct-3 +POU5F1P4,Oct-4 +POU5F1P4,Oct4-pg4 +POU5F1P4,POU5F1 +POU5F1P4,POU5FLC1 +RPL13AP3,RPL13A_11_1370 +LINC00520,C14orf34 +LINC00520,LASSIE +RPL12P38,RPL12_14_1564 +HTR5BP,5-HT5B +HTR5BP,GPR134 +HTR5BP,HTR5B +RPS10P23,RPS10_13_1498 +NOS2P1,NOS2C +TAF5LP1,HsT33855 +TAF5LP1,TAF5LP +MT1HL1,MT1P2 +RPL3P3,RPL3_2_1372 +RPL5P10,RPL5_4_365 +HSPD1P2,HSP60P2 +HSPD1P2,HSPD-9P +HSPD1P2,HSPDP2 +RPL23AP56,RPL23A_21_952 +CCDC154,C16orf29 +SEBOX,OG-9 +SEBOX,OG9 +SEBOX,OG9X +USP17L3,USP17B +USP17L3,USP17F +TMEM14EP,TMEM14E +RPL7AP58,RPL7A_20_1131 +RPS10P17,RPS10_5_1013 +MIX23P1,CCDC58P1 +FAM90A12P,FAM90A12 +RPS7P5,RPS7_2_183 +RPS28P4,RPS28_1_332 +RPS11P2,RPS11_1_184 +RPS13P7,RPS13_3_1434 +S100A7L2,S100a7b +MCFD2P1,C6orf44 +MCFD2P1,MCFD2L +MCFD2P1,NUCB2-L +MCFD2P1,dJ153G14.2 +FEN1P1,FENL1 +THAP12P8,PRKRIRP8 +HNRNPA1P1,HNRPA1P1 +HNRNPA1P1,b34I8.2 +HNRNPA1P1,hnRNPA1-Lp +SVIL2P,SVILP1 +RPS3P7,RPS3_3_1501 +RPS18P9,RPS18_6_767 +SPATA31C2,FAM75C2 +RPL23AP20,RPL23A_6_188 +RPS3AP25,RPS3A_9_820 +ATP5PBP2,ATP5F1P2 +PABPC1L2B,RBM32B +RPS26P31,RPS26_8_840 +CFL1P4,CFLL4 +CFL1P4,CFLP4 +RPL31P57,RPL31_21_1570 +"NAP1L6P","NAP1L6" +RPL10AP5,RPL10A_2_189 +SLC35G4,AMAC1L1 +SLC35G4,SLC35G4P +RPS17P3,RPS17B +RPS17P3,RPS17L2 +RPS17P3,RPS17_7_1537 +ADORA2A-AS1,C22orf45 +RPL29P23,RPL29_11_1134 +PA2G4P6,PA2G4L3 +WDR45BP1,WDR45LP1 +RPS8P7,RPS8_3_645 +RPL32P16,RPL32_7_768 +RPS7P14,RPS7_5_1787 +RPS8P4,RPS8_2_1103 +RPL26P21,RPL26_11_785 +LRRC77P,LINC01330 +RPL36AP48,RPL36A_23_1571 +SPATA31E2P,FAM75E2P +RPL18AP13,RPL18A_8_1622 +NOP56P1,NOL5B +NOP56P1,NOL5BP +NOP56P1,NOP56-L +NOP56P1,dJ207F6.1 +RPS28P7,RPS28_3_1160 +TLK2P1,TLK2ps1 +RPS27P10,RPS27_2_322 +RPL19P16,RPL19_5_1105 +SAR1AP1,SAR1P1 +SAR1AP1,SARAP +SAR1AP1,SARAP1 +SAR1AP1,dJ88J8.3 +SAR1AP1,hsSARA-p +RPS4XP8,RPS4P8 +RPS4XP8,RPS4X_5_772 +HSPD1P15,HSPD1-15P +RPS2P29,RPS2_9_695 +COL6A4P2,COL6A4 +"NAMPTP1","NAMPTL" +"NAMPTP1",PBEF2 +"NAMPTP1",bA92J19.4 +FTH1P13,FTHL13 +FTH1P13,FTHP3 +MAS1LP1,MAS1LP +IGHV1OR15-3,IGHV1OR153 +IGHV1OR15-4,IGHV1OR154 +DGAT2L7P,DGAT2L7 +RPL29P5,RPL29_2_63 +RPL32P19,RPL32_10_878 +CCT3P1,CCT3-1P +RPL27P2,RPL27_1_105 +ARIH2OS,C3orf71 +UBE2HP1,UBE2HP +RPL7AP39,RPL7A_12_828 +ADAM24P,ADAM24 +FABP9,PERF +FABP9,PERF15 +FABP9,T-FABP +FABP9,TLBP +RPL6P19,RPL6_14_853 +RPL21P70,RPL21_4_205 +SPATA31E3P,FAM75E3P +SUCLA2P1,Em:AB014080.4 +SUCLA2P1,SUCLA2P +RPS3AP44,RPS3A_18_1305 +RPL19P5,RPL19_2_325 +RPL7P10,RPL7_2_66 +RPLP1P12,RPLP1_6_1670 +RPS4XP18,RPS4P18 +RPS4XP18,RPS4X_10_1587 +ZNF652P1,ZNF652P +IGF2BP2-AS1,C3orf65 +ATP5MC1P3,ATP5G1P3 +RPL3P8,RPL3_4_835 +RPL27P4,RPL27_1_28 +URAD,PRHOXNB +SBK2,SGK069 +SYNDIG1L,CAPUCIN +SYNDIG1L,DSPC1 +SYNDIG1L,IFITMD4 +SYNDIG1L,TMEM90A 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+MIR376A2,mir-376a-2 +MIR487B,MIRN487B +MIR487B,mir-487b +MIR542,MIRN542 +MIR542,hsa-mir-542 +MIR542,mir-542 +HSP90AB4P,HSP90Bd +ZNF826P,ZNF826 +HNRNPA1P10,HNRPA1L-2 +HNRNPA1P27,HNRPA1P5 +HNRNPA1P26,HNRPA1P6 +SCARNA3,HBI-100 +SCARNA20,ACA66 +SCARNA21,ACA68 +SCARNA21,SCARNA21A +SCARNA18,SCARNA18A +SCARNA18,U109 +SCARNA2,HBII-382 +SCARNA2,mgU2-25/61 +SCARNA7,U90 +SCARNA13,U93 +SCARNA17,U91 +SCARNA22,ACA11 +SCARNA4,ACA26 +SCARNA6,U88 +SCARNA23,ACA12 +SCARNA1,ACA35 +SCARNA5,U87 +SCARNA8,U92 +SCARNA12,U89 +SCARNA15,ACA45 +LINC00488,C3orf66 +SCARNA11,ACA57 +SCARNA16,ACA47 +FAM138D,F379 +SNORA1,ACA1 +SNORA2A,ACA2A +SNORA2B,ACA2b +SNORA5B,ACA5b +SNORA5C,ACA5c +SNORA7B,ACA7B +SNORA9,ACA9 +SNORA9,SNORA9A +SNORA11,SNORA11A +SNORA11,U107 +SNORA12,U108 +SNORA14A,ACA14a +SNORA14B,ACA14b +SNORA15,ACA15 +SNORA15,SNORA15A +SNORA17A,ACA17 +SNORA17A,SNORA17 +SNORA18,ACA18 +SNORA20,ACA20 +SNORA22,ACA22 +SNORA22,SNORA22A +SNORA23,ACA23 +SNORA24,ACA24 +SNORA24,SNORA24A +SNORA26,HBI-6 +SNORA28,ACA28 +SNORA29,ACA29 +SNORA30,ACA30 +SNORA30,SNORA30A +SNORA31,ACA31 +SNORA31,IIAE10 +SNORA31,SNORA31A +SNORA2C,ACA34 +SNORA2C,MIR1291 +SNORA2C,SNORA34 +SNORA35,HBI-36 +SNORA35,SNORA35A +SNORA36A,ACA36 +SNORA36B,ACA36b +SNORA37,ACA37 +SNORA38,ACA38 +SNORA71E,ACA39 +SNORA71E,SNORA39 +SNORA40,ACA40 +SNORA40,SNORA40A +SNORA80E,ACA42 +SNORA80E,SNORA42 +SNORA17B,ACA43 +SNORA17B,SNORA43 +SNORA44,ACA44 +SNORA3B,ACA3-2 +SNORA3B,SNORA45 +SNORA3B,SNORA45B +SNORA46,ACA46 +SNORA47,HBI-115 +SNORA49,ACA49 +SNORA50A,ACA50 +SNORA50A,SNORA50 +SNORA50A,SNORA76A +SNORA51,ACA51 +SNORA53,ACA53 +SNORA54,ACA54 +SNORA55,ACA55 +SNORA56,ACA56 +SNORA58,ACA58 +SNORA58,SNORA58B +SNORA60,ACA60 +SNORA61,ACA61 +SNORA71C,U71c +SNORA71D,U71d +SNORA74B,U19-2 +SNORA50C,ACA62 +SNORA50C,SNORA76 +SNORA50C,SNORA76C +SNORA77,ACA63 +SNORA77,SNORA77A +SNORA78,ACA64 +SNORA79,ACA65 +SNORA79,SNORA79A +SNORA80A,ACA67 +SNORA80A,SNORA80 +SNORA81,HBI-61 +SNORD1A,R38A +SNORD1A,snR38A +SNORD1B,R38B +SNORD1B,snR38B +SNORD1C,R38C +SNORD1C,snR38C +SNORA59B,ACA59 +CHTF8P1,CHTF8P +CHTF8P1,CTF8P +SNORA59A,ACA59 +SNORA25,ACA25 +SNORA25,SNORA25A +SNORD9,mgU6-53B +SNORD12,HBII-99 +SNORD12,MIR1259 +SNORD12,MIRN1259 +SNORD11,HBII-95 +SNORA32,ACA32 +SNORD5,mgh28S-2410 +SNORA16A,ACA16 +SNORD6,mgh28S-2412 +SNORD7,Z30 +SNORD7,mgU6-47 +SNORD13,RNU13 +SNORD13,SNORD13A +SNORD13,U13 +SNORD45C,U45C +SNORD17,HBI-43 +SNORD49B,U49B +SNORD50B,U50' +SNORD50B,U50B +SNORD19,HBII-108 +SNORD59B,U59B +SNORD23,HBII-115 +SNORD32B,U32B +SNORD62B,U62 +SNORD62B,U62B +MSMP,PSMP +FAM86DP,FAM86D +SNORD65,HBII-135 +SNORD65,SNORD65A +SNORD66,HBII-142 +SNORD67,HBII-166 +SNORD69,HBII-210 +SNORD70,HBII-234 +SNORD70,SNORD70A +SNORD71,HBII-239 +SNORD71,MIRN768 +SNORD71,hsa-mir-768 +RPS26P4,RPS26_32_1733 +HMGB1P8,HMG1L8 +HMGB1P8,HMGB1L8 +SCARNA10,U85 +SCARNA14,U100 +SNORA16B,U98b +SNORA57,U99 +TAMALIN-AS1,GRASP-AS1 +TAMALIN-AS1,GRASPOS +SNORD75,U75 +SNORD76,U76 +SNORD77,SNORD77A +SNORD77,U77 +SNORD78,U78 +SNORD84,U84 +SNORD103C,HBII-251 +SNORD103C,SNORD85 +SNORD86,U86 +SNORD88A,HBII-180A +SNORD88B,HBII-180B +SNORD88C,HBII-180C +SNORD89,HBII-289 +SNORD90,HBII-295 +SNORD91A,HBII-296a +SNORD91B,HBII-296b +SNORD92,HBII-316 +SNORD93,HBII-336 +SNORD98,HBII-419 +SNORD99,HBII-420 +SNORD110,HBII-55 +SNORD111,HBII-82 +SNORD112,14q(0) +SNORD97,U97 +SNORD94,U94 +SNORD96B,U96b +SNORD104,U104 +SNORD105,U105 +SNORD117,U83 +SNORD103A,U103 +SNORD103B,U103B +SNORD116@,HBII-85 +SNORD116@,PET1 +SNORD116@,PWCR1 +PPAN-P2RY11,BXDC3 +PPAN-P2RY11,P2RY11 +PPAN-P2RY11,P2Y11 +PPAN-P2RY11,PPAN +PPAN-P2RY11,SSF1 +PPAN-P2RY11,Ssf-1 +MIR33B,MIRN33B +MIR33B,hsa-mir-33b +MIR33B,mir-33b +MIR411,MIRN411 +MIR411,hsa-mir-411 +MIR411,mir-411 +MIR421,MIRN421 +MIR421,hsa-mir-421 +MIR449B,MIRN449B +MIR449B,mir-449b +MIR532,MIRN532 +MIR532,hsa-mir-532 +MIR532,mir-532 +MIR548A1,MIRN548A1 +MIR548A2,MIRN548A2 +MIR548A3,MIRN548A3 +MIR548B,MIRN548B +MIR548C,MIRN548C +MIR548D1,MIRN548D1 +MIR548D2,MIRN548D2 +MIR549A,MIR549 +MIR549A,MIRN549 +MIR549A,hsa-mir-549 +MIR549A,hsa-mir-549a +MIR549A,mir-549a +MIR550A1,MIR550-1 +MIR550A1,MIRN550-1 +MIR550A1,hsa-mir-550a-1 +MIR550A1,mir-550a-1 +MIR550A2,MIR550-2 +MIR550A2,MIRN550-2 +MIR550A2,hsa-mir-550a-2 +MIR550A2,mir-550a-2 +MIR551A,MIRN551A +MIR551A,mir-551a +MIR551B,MIRN551B +MIR551B,mir-551b +MIR552,MIRN552 +MIR552,hsa-mir-552 +MIR553,MIRN553 +MIR553,hsa-mir-553 +MIR554,MIRN554 +MIR554,hsa-mir-554 +MIR555,MIRN555 +MIR555,hsa-mir-555 +MIR556,MIRN556 +MIR556,hsa-mir-556 +MIR556,mir-556 +MIR557,MIRN557 +MIR557,hsa-mir-557 +MIR558,MIRN558 +MIR558,hsa-mir-558 +MIR559,MIRN559 +MIR559,hsa-mir-559 +MIR561,MIRN561 +MIR561,hsa-mir-561 +MIR561,mir-561 +MIR562,MIRN562 +MIR562,hsa-mir-562 +MIR563,MIRN563 +MIR563,hsa-mir-563 +MIR564,MIRN564 +MIR564,hsa-mir-564 +MIR567,MIRN567 +MIR567,hsa-mir-567 +MIR568,MIRN568 +MIR568,hsa-mir-568 +MIR569,MIRN569 +MIR569,hsa-mir-569 +MIR570,MIRN570 +MIR570,hsa-mir-570 +MIR571,MIRN571 +MIR571,hsa-mir-571 +MIR572,MIRN572 +MIR572,hsa-mir-572 +MIR573,MIRN573 +MIR573,hsa-mir-573 +MIR573,mir-573 +MIR574,MIR574-3p +MIR574,MIRN574 +MIR574,hsa-mir-574 +MIR574,mir-574 +MIR575,MIRN575 +MIR575,hsa-mir-575 +MIR576,MIRN576 +MIR576,hsa-mir-576 +MIR577,MIRN577 +MIR577,hsa-mir-577 +MIR577,mir-577 +MIR578,MIRN578 +MIR578,hsa-mir-578 +MIR579,MIRN579 +MIR579,hsa-mir-579 +MIR580,MIRN580 +MIR580,hsa-mir-580 +MIR581,MIRN581 +MIR581,hsa-mir-581 +MIR582,MIRN582 +MIR582,hsa-mir-582 +MIR583,MIRN583 +MIR583,hsa-mir-583 +MIR584,MIRN584 +MIR584,hsa-mir-584 +MIR585,MIRN585 +MIR585,hsa-mir-585 +MIR585,mir-585 +MIR586,MIRN586 +MIR586,hsa-mir-586 +MIR587,MIRN587 +MIR587,hsa-mir-587 +MIR588,MIRN588 +MIR588,hsa-mir-588 +MIR589,MIRN589 +MIR589,hsa-mir-589 +MIR589,mir-589 +MIR590,MIRN590 +MIR590,hsa-mir-590 +MIR590,mir-590 +MIR591,MIRN591 +MIR591,hsa-mir-591 +MIR592,MIRN592 +MIR592,hsa-mir-592 +MIR592,mir-592 +MIR593,MIRN593 +MIR593,hsa-mir-593 +MIR595,MIRN595 +MIR595,hsa-mir-595 +MIR596,MIRN596 +MIR596,hsa-mir-596 +MIR597,MIRN597 +MIR597,hsa-mir-597 +MIR597,mir-597 +MIR598,MIRN598 +MIR598,hsa-mir-598 +MIR598,mir-598 +MIR599,MIRN599 +MIR599,hsa-mir-599 +MIR600,MIRN600 +MIR600,hsa-mir-600 +MIR601,MIRN601 +MIR601,hsa-mir-601 +MIR602,MIRN602 +MIR602,hsa-mir-602 +MIR603,MIRN603 +MIR603,hsa-mir-603 +MIR604,MIRN604 +MIR604,hsa-mir-604 +MIR605,MIRN605 +MIR605,hsa-mir-605 +MIR605,mir-605 +MIR606,MIRN606 +MIR606,hsa-mir-606 +MIR607,MIRN607 +MIR607,hsa-mir-607 +MIR608,MIRN608 +MIR608,hsa-mir-608 +MIR609,MIRN609 +MIR609,hsa-mir-609 +MIR610,MIRN610 +MIR610,hsa-mir-610 +MIR611,MIRN611 +MIR611,hsa-mir-611 +MIR612,MIRN612 +MIR612,hsa-mir-612 +MIR613,MIRN613 +MIR613,hsa-mir-613 +MIR614,MIRN614 +MIR614,hsa-mir-614 +MIR615,MIRN615 +MIR615,hsa-mir-615 +MIR615,mir-615 +MIR616,MIRN616 +MIR616,hsa-mir-616 +MIR617,MIRN617 +MIR617,hsa-mir-617 +MIR618,MIRN618 +MIR618,hsa-mir-618 +MIR618,mir-618 +MIR619,MIRN619 +MIR619,hsa-mir-619 +MIR620,MIRN620 +MIR620,hsa-mir-620 +MIR621,MIRN621 +MIR621,hsa-mir-621 +MIR622,MIRN622 +MIR622,hsa-mir-622 +MIR623,MIRN623 +MIR623,hsa-mir-623 +MIR624,MIRN624 +MIR624,hsa-mir-624 +MIR624,mir-624 +MIR625,MIRN625 +MIR625,hsa-mir-625 +MIR626,MIRN626 +MIR626,hsa-mir-626 +MIR627,MIRN627 +MIR627,hsa-mir-627 +MIR627,mir-627 +MIR628,MIRN628 +MIR628,hsa-mir-628 +MIR628,mir-628 +MIR629,MIRN629 +MIR629,hsa-mir-629 +MIR629,mir-629 +MIR630,MIRN630 +MIR630,hsa-mir-630 +MIR631,MIRN631 +MIR631,hsa-mir-631 +MIR632,MIRN632 +MIR632,hsa-mir-632 +MIR633,MIRN633 +MIR633,hsa-mir-633 +MIR634,MIRN634 +MIR634,hsa-mir-634 +MIR635,MIRN635 +MIR635,hsa-mir-635 +MIR636,MIRN636 +MIR636,hsa-mir-636 +MIR637,MIRN637 +MIR637,hsa-mir-637 +MIR638,MIRN638 +MIR638,hsa-mir-638 +MIR639,MIRN639 +MIR639,hsa-mir-639 +MIR640,MIRN640 +MIR640,hsa-mir-640 +MIR641,MIRN641 +MIR641,hsa-mir-641 +MIR641,mir-641 +MIR642A,MIR642 +MIR642A,MIRN642 +MIR642A,hsa-mir-642 +MIR642A,hsa-mir-642a +MIR642A,mir-642a +MIR643,MIRN643 +MIR643,hsa-mir-643 +MIR643,mir-643 +MIR644A,MIR644 +MIR644A,MIRN644 +MIR644A,hsa-mir-644 +MIR644A,hsa-mir-644a +MIR645,MIRN645 +MIR645,hsa-mir-645 +MIR646,MIRN646 +MIR646,hsa-mir-646 +MIR647,MIRN647 +MIR647,hsa-mir-647 +MIR648,MIRN648 +MIR648,hsa-mir-648 +MIR649,MIRN649 +MIR649,hsa-mir-649 +MIR92B,MIRN92B +MIR92B,hsa-mir-92b +MIR92B,mir-92b +MIR650,MIRN650 +MIR650,hsa-mir-650 +MIR651,MIRN651 +MIR651,hsa-mir-651 +MIR651,mir-651 +H2AC19,H2A/R +H2AC19,H2AC18 +H2AC19,HIST2H2AA4 +INS-IGF2,INSIGF +MIR652,MIRN652 +MIR652,hsa-mir-652 +MIR653,MIRN653 +MIR653,hsa-mir-653 +MIR653,mir-653 +MIR654,MIRN654 +MIR654,hsa-mir-654 +MIR654,mir-654 +MIR655,MIRN655 +MIR655,hsa-mir-655 +MIR655,mir-655 +MIR656,MIRN656 +MIR656,hsa-mir-656 +MIR656,mir-656 +MIR657,MIRN657 +MIR657,hsa-mir-657 +MIR658,MIRN658 +MIR658,hsa-mir-658 +MIR659,MIRN659 +MIR659,hsa-mir-659 +MIR659,mir-659 +MIR660,MIRN660 +MIR660,hsa-mir-660 +MIR660,mir-660 +MIR661,MIRN661 +MIR661,hsa-mir-661 +MIR662,MIRN662 +MIR662,hsa-mir-662 +MIR663A,MIR663 +MIR663A,MIRN663 +MIR663A,hsa-mir-663 +MIR663A,hsa-mir-663a +MIR663A,mir-663a +DEFA7P,DEFA7 +DEFA11P,DEFAP3 +LINC01545,CXorf31 +SNHG4,NCRNA00059 +SNHG4,U19H +SPG32,SPG29 +SNORD118,LCC +SNORD118,U8 +CASC8,CARLO1 +CASC8,CARLo-1 +CASC8,LINC00860 +TAB3-AS1,CXorf29 +TMEM51-AS2,C1orf195 +SNURFL,CXorf19 +LINC00163,C21orf134 +LINC00163,NCRNA00163 +LINC00163,NLC1-A +LINC00163,NLC1A +LINC00165,C21orf135 +LINC00165,NCRNA00165 +LINC00165,NLC1-B +LINC00165,NLC1B +IGFL1P1,IGFL-5P +IGFL1P2,IGFL-6P +SNORD116-19,HBII-85-19 +SH3GL1P3,CNSA-P3 +SH3GL1P3,SH3GLP3 +RPL22P12,RPL22_6_262 +ROCK1P1,ROCK1P +MAFIP,MIP +MAFIP,TEKT4P4 +MAFIP,pp5644 +RWDD1P3,RWDD1L1 +RPL31P31,RPL31_10_468 +HMGN2P5,HMGN2L5 +RPL21P17,RPL21_9_342 +RPL21P45,RPL21_15_469 +RPS11P5,RPS11_3_1298 +SPATA31A3,FAM75A3 +RPL13AP8,RPL13A_2_157 +IMPDH1P11,IMPDH1 +IMPDH1P11,IMPDHL1 +RPL9P16,RPL9_3_534 +SSX2B,CT5.2 +SSX2B,CT5.2b +SSX2B,HOM-MEL-40 +SSX2B,SSX +RGPD8,RANBP2L1 +RGPD8,RGP8 +RGPD8,RanBP2alpha +BHLHA9,BHLHF42 +BHLHA9,CCSPD +C4BPAP1,C4BPAL1 +RPL9P32,RPL9_18_1643 +FAM156B,TMEM29B +MUC5B,MG1 +MUC5B,MUC-5B +MUC5B,MUC5 +MUC5B,MUC9 +SPATA31A5,FAM75A5 +GXYLT2,GLT8D4 +RPS3AP16,RPS3A_3_456 +SDHAP2,SDHAL2 +SDHAP2,SDHALP2 +MROH1,HEATR7A +RPS3AP43,RPS3A_21_1203 +RPL17P22,RPL17_13_564 +RPS12P9,RPS12_3_484 +RPL15P17,RPL15_10_1204 +AGAP13P,CTGLF9P +AGAP13P,bA548K23.1 +RPL35P3,RPL35_2_728 +H2AZP3,H2AFZP3 +CT47A10,CT47 +CT47A10,CT47.10 +SSR4P1,C21orf122 +SSR4P1,PRED57 +SSR4P1,PRED90 +CT47A9,CT47.9 +PPBPP1,PPBPL1 +PPBPP1,TGB2 +CT47A8,CT47.8 +CT47A6,CT47.6 +CT47A5,CT47.5 +CT47A4,CT47.4 +LINC00290,NCRNA00290 +CT47A3,CT47.3 +RPL15P18,RPL15_5_1331 +CT47A2,CT47.2 +CT47A1,CT47.1 +RPL23AP50,RPL23A_20_731 +ANXA8L1,ANXA8 +ANXA8L1,ANXA8L2 +ANXA8L1,VAC-beta +ANXA8L1,bA145E20.2 +SINHCAFP2,FAM60CP +ZBTB8B,ZNF916B +NUTM2A,FAM22A +CSPG4P12,CSPG4P9 +RPSAP30,RPSA_7_452 +RPL7AP26,RPL7A_8_496 +NUTM2D,FAM22D +TSPY9P,TSPY9 +RPL23AP18,RPL23A_3_161 +TSPY3,CT78 +TSPY3,TSPY +TSPY3,TSPY1 +TSPY3,TSPY10 +RPL7AP70,RPL7A_32_1739 +DUX4L16,DUXY1 +RPL7AP36,RPL7A_9_655 +NUTM2A-AS1,FAM22A-AS1 +RPL36AP7,RPL36A_24_1574 +RPL23AP75,RPL23A_32_1546 +"NALF1",FAM155A +"NALF1",NLF-1 +FAM245A,LINC00864 +KRTAP4-8,KAP4.8 +KRTAP4-8,KRTAP4.8 +GGTLC3,GGT +HMGN2P16,HMGN2L +MAGED4,MAGE-D4 +MAGED4,MAGE-E1 +MAGED4,MAGE1 +MAGED4,MAGEE1 +RPS12P29,RPS12_15_1507 +KRTAP1-4,KAP1.4 +KRTAP1-4,KRTAP1.4 +FAM157A,GTF2IP18 +CARMN,CARMEN +CARMN,MIR143HG +KRTAP2-2,KAP2.1 +KRTAP2-2,KAP2.2 +KRTAP2-2,KRTAP2-1 +KRTAP2-2,KRTAP2.1A +KRTAP2-2,KRTAP2.1B +D2HGDH,D2HGD +GOLGA6L7,GOLGA6L7P +RPL26P24,RPL26_12_861 +RPL7AP31,RPL7A_7_480 +KRTAP9-1,KAP9.1 +KRTAP9-1,KRTAP9L3 +GTF2H2C,GTF2H2C_2 +NXF2B,TCP11X2 +NXF2B,bA353J17.1 +DEFA1B,HNP-1 +DEFA1B,HP-1 +DEFA1B,HP1 +OVOL3,HOVO3 +RPL6P16,RPL6_12_538 +USP17L24,USP17L25 +USP17L24,USP17L26 +USP17L24,USP17L27 +USP17L24,USP17L28 +USP17L24,USP17L29 +USP17L24,USP17L30 +RPL9P21,RPL9_8_1037 +POTEF,A26C1B +POTEF,ACTB +POTEF,POTE2alpha +POTEF,POTEACTIN +RPL7P26,RPL7_9_661 +DNM1P30,DNM1DN3-4 +DNM1P30,DNM1DN3.4 +DNM1P30,DNM1DN3@ +DNM1P30,DNM1DN4-3 +DNM1P30,DNM1DN4@ +TSPY4,TSPY10 +TSPY4,TSPY8 +LINC01548,C21orf54 +DUX4L2,DUX4 +POM121L7P,POM121L7 +RPS15P1,RPS15_1_87 +FAM90A20P,FAM90A20 +ZC3H12A-DT,LINC01137 +ICE2P2,"NARG2P2" +LINC02871,C20orf187 +GGT2,GGT +GGT2,GGT 2 +TEX28P1,pTEX +PPIEL,PPIEP1 +LINC00687,C20orf61 +LINC00687,dJ1012F16.1 +RPS28P6,RPS28_2_814 +OPN1MW2,GOP +METTL24,C6orf186 +RPL23AP21,RPL23A_1_1 +RPL23AP61,RPL23A_22_1044 +RPL7AP64,RPL7A_29_1509 +DNLZ,C9orf151 +DNLZ,HEP +DNLZ,HEP1 +DNLZ,TIMM15 +DNLZ,ZIM17 +DNLZ,bA413M3.2 +SERF1B,FAM2B +SERF1B,H4F5C +SERF1B,h4F5 +GOLGA8H,GOLGA6L11 +RPL21P75,RPL21_29_789 +WDR82P1,SW2 +WDR82P1,SWD2 +WDR82P1,WDR82 +WDR82P1,WDR82B +WDR82P1,hCG26824 +RPS19P7,RPS19_3_1038 +PNMA6B,PNMA6D +RPL23AP24,RPL23A_1_2 +"NAIPP2",BIRC +"NAIPP2","NAIP1B" +RPL9P17,RPL9_5_548 +SPDYE8,SPDYE8P +SPDYE8,SPDYE9 +C2orf92,LINC01125 +RPL23AP77,RPL23A_33_1589 +RPS8P8,RPS8_1_552 +MEMO1P1,C21orf19 +MEMO1P1,C2orf4P +MEMO1P1,MEMO1P +MEMO1P1,Memo1 +MEMO1P1,rcC2orf4 +RPSAP45,RPSA_11_739 +RPL23AP48,RPL23A_16_748 +C12orf73,BR +C12orf73,BRAWNIN +RPL32P11,RPL32_2_350 +RPL15P7,RPL15_2_475 +CCDC192,LINC01183 +RPS27AP11,RPS27A_4_740 +CCDC169,C13orf38 +CIAPIN1P,HsT2384 +PCAT18,LINC01092 +SDHAP3,SDHACL +SDHAP3,SDHAL +RPL7P22,RPL7_6_574 +CCDC30,PFD6L +CCDC30,PFDN6L +SKP1P2,SKP1B +SKP1P2,p19B +HMGN2P3,HMGN2L3 +DHRS4L1,SDR25C4 +FABP5P7,FABP5L7 +CDK11A,CDC2L2 +CDK11A,CDC2L3 +CDK11A,CDK11-p110 +CDK11A,CDK11-p46 +CDK11A,CDK11-p58 +CDK11A,PITSLRE +CDK11A,p58GTA +HULC,HCCAT1 +HULC,LINC00078 +HULC,NCRNA00078 +DUSP8P2,DUSP8P4 +RPL13AP5,RPL13A_8_1086 +RPL13AP5,bA196N24.2 +SLC35E2B,SLC35E2 +RPL21P105,RPL21_43_1282 +SPANXB1,B1 +SPANXB1,CT11.2 +SPANXB1,SPANX-B +SPANXB1,SPANXB +SPANXB1,SPANXB2 +SPANXB1,SPANXF1 +SPANXB1,SPANXF2 +RPS3AP19,RPS3A_8_458 +FAM177A1P1,FAM177A2 +SPANXA2,CT11.1 +SPANXA2,CT11.3 +SPANXA2,SPANX +SPANXA2,SPANX-A +SPANXA2,SPANX-C +SPANXA2,SPANXA +SPANXA2,SPANXC +ZBED3-AS1,lnc13728 +FABP5P8,FABP5L8 +NPIPB6,NPIPB +FAM90A17P,FAM90A17 +ANKRD20A4P,ANKRD20A4 +THAP12P1,PRKRIRP1 +FAM90A19P,FAM90A19 +DNM1P5,DNM1DN5 +DNM1P5,DNM1DN5@ +PABPC1P2,PABP2 +PABPC1P2,PABP4 +PABPC1P2,PABPCP2 +PABPC1P2,PABPCP4 +RPS15AP25,RPS15A_14_912 +RPL21P134,RPL21_61_1788 +ANKRD20A20P,CCDC29 +RPS10P4,RPS10_2_393 +RPS10P4,bcm1851 +FRMPD2B,FRMPD2 +FRMPD2B,FRMPD2L1 +FRMPD2B,FRMPD2L2 +FRMPD2B,FRMPD2P1 +FRMPD2B,FRMPD2P2 +FRMPD2B,FRMPDP2 +FRMPD2B,PDZD5A +FRMPD2B,PDZD5B +FRMPD2B,PDZD5C +FRMPD2B,PDZK4 +FRMPD2B,PDZK5A +FRMPD2B,PDZK5B +FRMPD2B,PDZK5C +FRMPD2B,bA556L1.2 +FRMPD2B,yX59F3.2 +NSFP1,NSFP +RPL29P15,RPL29_4_582 +RPS26P53,RPS26_19_1511 +FAM72D,GCUD2 +NBPF8,NBPF8P +RPL7P20,RPL7_8_636 +LINC00623,CH17-118O6.2 +NPIPB11,NPIP +CT45A2,CT45-2 +CT45A2,CT45.2 +CT45A2,CT45A8 +CT45A2,CT45A9 +ABHD17AP1,ABHD17AP2 +ABHD17AP1,C1orf47 +ABHD17AP1,FAM108A11P +ABHD17AP1,FAM108A2 +ABHD17AP1,FAM108A3 +ABHD17AP1,FAM108A3P +PDE4DIPP1,PDE4DIP1 +ELOA3BP,ELOA3 +ELOA3BP,ELOA3B +ELOA3BP,ELOA3L1 +ELOA3BP,EloA3C +ELOA3BP,Elongin-A3 +ELOA3BP,HsT828 +ELOA3BP,TCEB3C +ELOA3BP,TCEB3CL +ELOA3BP,TCEB3L2 +ELOA3BP,eloA3-like-1 +RPL23AP60,RPL23A_26_1111 +RPS26P25,RPS26_10_512 +SUB1P1,hCG17750 +RPS19P3,RPS19_4_1350 +ZNF705D,ZNF705C +RPL7P56,RPL7_33_1817 +RPS15AP10,RPS15A_4_42 +RPS27AP7,RPS27A_2_217 +UNC5B-AS1,UASR1 +RPL10AP9,RPL10A_3_593 +RPL7AP11,RPL7A_5_397 +RPL9P2,RPL9_10_1518 +FTH1P20,FTHL20 +SKA2P1,Em:AC006313.1 +SKA2P1,FAM33B +SKA2P1,SKA2L +NUTM2HP,FAM22HP +NUTM2HP,NUTMHP +FAAHP1,FAAH-OUT +FAAHP1,FAAHOUT +FAAHP1,LINC00505 +RPL29P3,RPL29_19_1356 +TEX46,C1orf234 +TEX46,TEX# +CIBAR1P1,FAM92A1P1 +CIBAR1P1,FAM92A2 +RPL36AP23,RPL36A_6_507 +RPSAP21,RPSA_2_180 +OIP5-AS1,cyrano +OIP5-AS1,linc-OIP5 +CCDC13-AS2,LINC02158 +GASK1A,C3orf41 +GASK1A,FAM198A +AGAP5,CTGLF2 +BMS1P4,BMS1LP4 +RPS4XP14,RPS4P14 +RPS4XP14,RPS4X_5_1190 +RPL7P21,RPL7_10_623 +RPLP1P2,RPLP1_2_185 +RPL9P28,RPL9_16_1554 +RPS3AP14,RPS3A_7_401 +FABP5P2,FABP5L2 +ANKRD20A8P,ANKRD20B +NBAT1,CASC14 +NBAT1,NBAT-1 +RPS26P58,RPS26_33_1769 +ANAPC10P1,ANAPC10P +SPACA5B,LYZL5B +RPL31P30,RPL31_2_226 +RPL21P14,HsT16795 +RPL21P14,RPL21_53_1436 +RPSAP26,RPSA_3_227 +KRT18P15,KRT18L5 +RPL13AP26,RPL13A_13_1745 +RPL32P29,RPL32_8_1362 +CCR2,CC-CKR-2 +CCR2,CCR-2 +CCR2,CCR2A +CCR2,CCR2B +CCR2,CD192 +CCR2,CKR2 +CCR2,CKR2A +CCR2,CKR2B +CCR2,CMKBR2 +CCR2,MCP-1-R +PRR20B,PRR20 +PRR20B,PRR20A +PRR20B,PRR20C +PRR20B,PRR20D +PRR20B,PRR20E +RPS13P2,RPS13_1_51 +SFTPA2,COLEC5 +SFTPA2,PSAP +SFTPA2,PSP-A +SFTPA2,PSPA +SFTPA2,SFTP1 +SFTPA2,SFTPA2B +SFTPA2,SP-2A +SFTPA2,SP-A +SFTPA2,SPA2 +SFTPA2,SPAII +PRR20C,PRR20 +PRR20C,PRR20A +PRR20C,PRR20B +PRR20C,PRR20D +PRR20C,PRR20E +RPL21P117,RPL21_53_1439 +PRR20D,PRR20 +PRR20D,PRR20A +PRR20D,PRR20B +PRR20D,PRR20C +PRR20D,PRR20E +KLHL2P1,MAVP1 +PRR20E,PRR20 +PRR20E,PRR20A +PRR20E,PRR20B +PRR20E,PRR20C +PRR20E,PRR20D +KRT16P1,KRT14P +KRT16P1,KRT16P +RPS24P4,RPS24_2_174 +RPS11P6,RPS11_2_1249 +RPL17P43,RPL17_24_1526 +NUTM2B,FAM22B +NUTM2B,bA119F19.1 +TP53TG3D,TP53TG3 +TP53TG3D,TP53TG3B +TP53TG3D,TP53TG3C +TP53TG3D,TP53TG3E +TP53TG3D,TP53TG3F +RPS2P53,RPS2_24_1626 +RPL23AP47,RPL23A_21_778 +RPL28P5,RPL28_2_1625 +ZNF286B,FOXO3B +ZNF286B,ZNF286C +ZNF286B,ZNF286L +ZNF286B,ZNF590 +RPS18P10,RPS18_2_763 +PMS2P8,PMS2L16 +RPL17P9,RPL17_1_25 +RPL31P45,RPL31_22_1025 +OC90,PLA2L +RPL27P8,RPL27_3_316 +RPL17P36,RPL17_11_1102 +AKR1B1P3,ALDRL3 +RPL17P16,RPL17_4_369 +TP53TG3B,TP53TG3E +TP53TG3B,TP53TG3F +PLIN4,KIAA1881 +PLIN4,S3-12 +RPL36AP37,RPL36A_14_1122 +RPS10P6,RPS10_1_98 +TRIM49D2,TRIM48L1 +TRIM49D2,TRIM49D1 +TRIM49D2,TRIM49D2P +TRIM49D2,TRIM49L +TRIM49D2,TRIM49L1 +RPS6P25,RPS6_13_1629 +COP1P1,RFWD2P +COP1P1,RFWD2P1 +POM121L6P,bA239L20.2 +GAGE12J,GAGE11 +RPL21P16,RPL21_38_1104 +GAPDHP61,GAPDHL6 +GAPDHP61,GAPDL6 +GAGE2D,CT4.8 +GAGE2D,GAGE-2D +GAGE2D,GAGE-8 +GAGE2D,GAGE8 +RPLP1P6,RPLP1_5_629 +LMO7DN,C13orf45 +GAGE12C,GAGE-12B +HNRNPA1P6,HNRNPA1L +HNRNPA1P6,HNRNPA1L1 +HNRNPA1P6,HNRPA1L +GAGE12B,GAGE-12B +GAGE12E,GAGE-12B +ATP6V1E1P3,ATP6EL1 +ATP6V1E1P3,ATP6V1EL1 +CASTOR2,GATSL1 +CASTOR2,GATSL2 +CCDC61,VFL3 +GAGE2A,CT4.2 +GAGE2A,GAGE-2 +GAGE2A,GAGE-2A +GAGE2A,GAGE2 +RPL7P50,RPL7_28_1618 +PMS2P6,PMS2L14 +PMS2P6,PMS2L2 +PMS2P6,PMS2P2 +PMS2P6,PMS4 +FAM230J,LINC01660 +RPL23AP5,RPL23AL +RPL23AP5,RPL23A_35_1465 +RPL23AP5,RPL2B +RPL23AP5,c367G8.3 +RPL23AP5,rjd10 +RPL21P18,RPL21_46_1252 +ABHD17AP4,FAM108A5 +ABHD17AP4,FAM108A5P +RPL12P14,RPL12_1_13 +TMEM242,BM033 +TMEM242,C6orf35 +AACSP1,AACSL +FAM72A,LMPIP +FAM72A,Ugene +FAM72A,p17 +RPL34P31,RPL34_16_1528 +RGPD6,RGP6 +RGPD6,RGPD7 +RGPD6,RanBP2L1 +RGPD6,RanBP2L2 +LINC01556,C6orf100 +LINC01556,dJ25J6.5 +RPF2P1,BXDC1P +RPF2P1,C20orf53 +RPF2P1,RPF2P +RPF2P1,bA353C18.4 +FAHD2P1,FAHD2P +RPL29P30,RPL29_20_1448 +RPL23AP74,RPL23A_37_1566 +RPL36AP46,RPL36A_22_1565 +RPL31P4,RPL31_28_1375 +ZNF812P,ZNF812 +S100A11P1,S100A11P +S100A11P1,S100A14 +RPL22P3,RPL22_1_15 +CCDC175,C14orf38 +CCDC175,c14_5395 +LDHAP5,LDHAL5 +RPL7P16,RPL7_5_405 +RPS2P51,RPS2_25_1632 +TPI1P1,RCTPI1 +RPL12P1,CICK0721Q.1 +RPL12P1,RPL12-L +RPL12P1,RPL12_5_682 +RPL12P1,dJ570F3.5 +PRSS44P,PRSS44 +PRSS44P,TESSP-4 +PRSS44P,TESSP4 +OR4F29,OR7-21 +RPL12P18,RPL12_3_246 +RPL23AP45,RPL23A_19_648 +RPS10P8,RPS10_1_104 +GOLGA8CP,GOLGA8C +RPS15P5,RPS15_3_372 +RPL23AP63,RPL23A_28_1140 +ZNF731P,ZNF731 +DNM1P34,DNM1DN8-1 +DNM1P34,DNM1DN8-5 +DNM1P34,DNM1DN8@ +FHIP1A,FAM160A1 +FHIP1A,FHIP-L +GGTLC4P,GGT +RPS2P50,RPS2_23_1541 +UMAD1,RPA3-AS1 +UMAD1,RPA3OS +RGPD2,NUP358 +RGPD2,RANBP2L2 +RGPD2,RGP2 +TBC1D3,PRC17 +TBC1D3,TBC1D3A +TBC1D3,TBC1D3C +TBC1D3,TBC1D3D +TBC1D3,TBC1D3F +TBC1D3,TBC1D3L +RPS16P10,RPS16_5_1636 +RPL36AP26,RPL36A_8_787 +CRPPA,ISPD +CRPPA,LGMDR20 +CRPPA,MDDGA7 +CRPPA,MDDGC7 +CRPPA,Nip +CRPPA,hISPD +RPS20P27,RPS20_12_1158 +ZNF75CP,ZNF75C +CDC26P1,CDC26P +RPL18AP5,RPL18A_1_27 +SHISA7,CKAMP59 +RPL21P71,RPL21_12_416 +MORN2,BLOCK27 +MORN2,MOPT +RFPL4AL1,RNF210B +BORCS8,MEF2BNB +SHISA9,CKAMP44 +RPSAP19,RPSA_2_81 +DNM1P49,DNM1DN8@ +RPL34P33,RPL34_18_1640 +RPL26P11,RPL26_5_125 +ZNF726,ZNF92P3 +MOSMO,ATTHOG +MOSMO,BC030336 +MOSMO,C16orf52 +GSTA7P,GSTAP2 +GSTA7P,GSTAP6 +NPIPB10P,NPIP +RPS26P16,RPS26_3_133 +RPS10P9,RPS10_3_235 +HSP90AA5P,HSP90Ae +RPL5P5,RPL5_1_139 +TUBA5P,TUBAP5 +BTG2-DT,LINC01136 +ACOD1,CAD +ACOD1,IRG1 +RPL17P8,RPL17_4_153 +PPIAL4E,COAS2 +PPIAL4E,PPIAL4F +ZNF735,ZNF735P +GTF2H2C_2,GTF2H2D +HINT1P1,HINTP1 +RPL14P3,RPL14_2_522 +KRTAP2-3,KAP2.3 +KRTAP2-3,KAP2.4 +KRTAP2-3,KRTAP2-4 +KRTAP2-3,KRTAP2.3 +KRTAP2-3,KRTAP2.4 +RPS3AP29,RPS3A_10_823 +RPS3AP39,RPS3A_18_1114 +DYNLL1P2,DNCL1P2 +RPL21P120,RPL21_57_1527 +RPL32P12,RPL32_4_459 +FAM238B,LINC00202-2 +RPL29P21,RPL29_9_1124 +TDRG1,LINC00532 +TDRG1,lincRNA-NR_024015 +MIX23P3,CCDC58P3 +SNHG9,NCRNA00062 +CSKMT,CS-KMT +CSKMT,METTL12 +CSKMT,U99HG +LINC00106,CXYorf8 +LINC00106,NCRNA00106 +CEMP1,CP-23 +CEMP1,CP23 +SYS1-DBNDD2,C20orf169-DBNDD2 +LUZP6,MPD6 +LUZP6,MTPNUT +SNORD113-1,14q(I-1) +SNORD113-2,14q(I-2) +SNORD113-3,14q(I-3) +SNORD113-4,14q(I-4) +SNORD113-5,14q(I-5) +SNORD113-6,14q(I-6) +SNORD113-7,14q(I-7) +SNORD113-8,14q(I-8) +SNORD113-9,14q(I-9) +SNORD114-1,14q(II-1) +SNORD114-2,14q(II-2) +SNORD114-3,14q(II-3) +SNORD114-4,14q(II-4) +SNORD114-5,14q(II-5) +SNORD114-6,14q(II-6) +SNORD114-7,14q(II-7) +SNORD114-8,14q(II-8) +SNORD114-9,14q(II-9) +SNORD114-10,14q(II-10) +SNORD114-11,14q(II-11) +SNORD114-12,14q(II-12) +SNORD114-13,14q(II-13) +SNORD114-14,14q(II-14) +SNORD114-15,14q(II-15) +SNORD114-16,14q(II-16) +SNORD114-17,14q(II-17) +SNORD114-18,14q(II-18) +SNORD114-19,14q(II-19) +SNORD114-20,14q(II-20) +SNORD114-21,14q(II-21) +SNORD114-22,14q(II-22) +SNORD114-23,14q(II-23) +SNORD114-24,14q(II-24) +SNORD114-25,14q(II-25) +SNORD114-26,14q(II-26) +SNORD114-27,14q(II-27) +SNORD114-28,14q(II-28) +SNORD114-29,14q(II-29) +SNORD114-30,14q(II-30) +SNORD114-31,14q(II-31) +H2BW4P,H2BFXP +PFN1P2,C1orf152 +PFN1P2,COAS3 +PFN1P4,PFN1P5 +HAR1A,HAR1F +HAR1A,LINC00064 +HAR1A,NCRNA00064 +HAR1B,HAR1R +HAR1B,LINC00065 +HAR1B,NCRNA00065 +PRCD,RP36 +MIR758,MIRN758 +MIR758,hsa-mir-758 +MIR758,mir-758 +MIR671,MIRN671 +MIR671,hsa-mir-671 +MIR671,mir-671 +MIR668,MIRN668 +MIR668,hsa-mir-668 +MIR767,MIRN767 +MIR767,hsa-mir-767 +MIR767,mir-767 +MIR454,MIRN454 +MIR454,hsa-mir-454 +MIR454,mir-454 +MIR769,MIRN769 +MIR769,hsa-mir-769 +MIR769,mir-769 +MIR766,MIRN766 +MIR766,hsa-mir-766 +MIR766,mir-766 +MIR802,MIRN802 +MIR802,hsa-mir-802 +MIR765,MIRN765 +MIR765,hsa-mir-765 +MIR765,mir-765 +MIR770,MIRN770 +MIR770,hsa-mir-770 +OPA6,ROA1 +TVP23A,FAM18A +TVP23A,YDR084C +SNORD3A,RNU3 +SNORD3A,U3 +SNORD3B-2,U3-2B +SNORD3B-2,U3b2 +SNORD3C,RNU3-3 +SNORD3C,U3-3 +SNORD3D,RNU3-4 +SNORD3D,U3-4 +HPP1,FPH1 +HPP1,HPP +HPP1,MUH +HHT3,ORW3 +DEL10Q26,C10q26DEL +DEL10Q26,TQDS +CHNG3,RTSH +GINGF3,GGF3 +GINGF3,HGF3 +TRS-AGA3-1,TRNAS4 +TRS-AGA3-1,TRS4 +ESRG,HESRG +UQCC3,C11orf83 +UQCC3,CCDS41658.1 +UQCC3,MC3DN9 +UQCC3,UNQ655 +TRK-CTT2-3,TRK-CTT2-4 +TRK-CTT2-3,TRK2 +TRK-CTT2-3,TRNAK2 +DEL17Q21.31,C17DELq21.31 +PWRN1,NCRNA00198 +PWRN2,NCRNA00199 +SHFL1,SHFLD +KTWS,KTS +TRN-GTT4-1,TRN2 +TRN-GTT4-1,TRNAN2 +TRN-GTT6-1,TRN3 +TRN-GTT6-1,TRNAN3 +TRV-AAC1-5,HtV1 +TRV-AAC1-5,TRNAV7 +TRV-AAC1-5,TRV7 +TRV-AAC3-1,HtV2 +TRV-AAC3-1,TRNAV8 +TRV-AAC3-1,TRV8 +TRV-CAC1-3,HtV3 +TRV-CAC1-3,TRNAV9 +TRV-CAC1-3,TRV9 +GAGE12F,AL4 +GAGE12F,CT4.7 +GAGE12F,GAGE-7 +GAGE12F,GAGE-7B +GAGE12F,GAGE-8 +GAGE12F,GAGE7 +GAGE12F,GAGE7B +RNA5-8SN5,RN5-8S1 +RNA5-8SN5,RNA5-8S5 +RNA18SN5,RN18S1 +RNA18SN5,RNA18S5 +RNA28SN5,RN28S1 +RNA28SN5,RNA28S5 +TRY-GTA7-1,TRNAY1 +TRY-GTA7-1,Tyr-tRNA +TRY-GTA5-4,TRNAY2 +TRY-GTA5-4,TRY-GTA5-2 +TRY-GTA5-4,Tyr-tRNA +TRA-AGC11-1,TRNAA2 +TRA-AGC8-1,TRNAA3 +TRF-GAA2-1,TRNAF1 +TRF-GAA1-4,TRF-GAA1-2 +TRF-GAA1-4,TRNAF2 +TRH-GTG1-6,TRH-GTG1-9 +TRH-GTG1-6,TRNAH5 +LINC02584,ANO1-AS2 +LINC02584,C11orf78 +LINC02584,NCRNA00224 +POU5F1P5,Oct4-pg5 +VN2R3P,VN2R4P +VN2R3P,VN2R5P +VN2R3P,VN2R8P +OTX2P1,OTX2P +SNORD116-1,HBII-85-1 +SNORD116-1,PWCR1 +SNORD116-2,HBII-85-2 +SNORD116-3,HBII-85-3 +SNORD116-4,HBII-85-4 +SNORD116-5,HBII-85-5 +SNORD116-6,HBII-85-6 +SNORD116-7,HBII-85-7 +SNORD116-8,HBII-85-8 +SNORD116-9,HBII-85-9 +SNORD116-10,HBII-85-10 +SNORD116-11,HBII-85-11 +SNORD116-12,HBII-85-12 +SNORD116-13,HBII-85-13 +SNORD116-14,HBII-85-14 +SNORD116-15,HBII-85-15 +SNORD116-16,HBII-85-16 +SNORD116-17,HBII-85-17 +SNORD116-18,HBII-85-18 +SNORD116-20,HBII-85-20 +SNORD116-21,HBII-85-21 +SNORD116-22,HBII-85-22 +SNORD116-23,HBII-85-23 +SNORD116-24,HBII-85-24 +SNORD116-25,HBII-85-25 +SNORD115-2,HBII-52-2 +SNORD116-26,HBII-85-26 +SNORD116-27,HBII-85-27 +SNORD115-3,HBII-52-3 +SNORD115-4,HBII-52-4 +SNORD115-5,HBII-52-5 +SNORD115-6,HBII-52-6 +SNORD115-7,HBII-52-7 +SNORD115-8,HBII-52-8 +SNORD115-9,HBII-52-9 +SNORD115-10,HBII-52-10 +SNORD115-11,HBII-52-11 +SNORD115-12,HBII-52-12 +SNORD115-13,HBII-52-13 +SNORD115-14,HBII-52-14 +SNORD115-15,HBII-52-15 +SNORD115-16,HBII-52-16 +SNORD115-17,HBII-52-17 +SNORD115-18,HBII-52-18 +SNORD115-19,HBII-52-19 +SNORD115-20,HBII-52-20 +SNORD115-21,HBII-52-21 +SNORD115-22,HBII-52-22 +SNORD115-23,HBII-52-23 +SNORD115-25,HBII-52-25 +SNORD115-26,HBII-52-26 +SNORD115-29,HBII-52-29 +SNORD115-30,HBII-52-30 +SNORD115-31,HBII-52-31 +SNORD115-32,HBII-52-32 +SNORD115-33,HBII-52-33 +SNORD115-34,HBII-52-34 +SNORD115-35,HBII-52-35 +SNORD115-36,HBII-52-36 +SNORD115-37,HBII-52-37 +SNORD115-38,HBII-52-38 +SNORD115-39,HBII-52-39 +SNORD115-40,HBII-52-40 +SNORD115-41,HBII-52-41 +SNORD115-42,HBII-52-42 +SNORD115-43,HBII-52-43 +SNORD115-44,HBII-52-44 +MIR675,MIRN675 +MIR675,hsa-mir-675 +SNORD116-28,HBII-85-28 +SNORD116-29,HBII-85-29 +SNORD115-48,HBII-52-48 +PDZK1P1,PDZK1P2 +SNORD115-24,HBII-52-24 +SNORD115-27,HBII-52-27 +SNORD115-28,HBII-52-28 +SNORD115-45,HBII-52-45 +SNORD115-45,SNORD115-48 +SNORD115-47,HBII-52-46 +SNORD115-47,HBII-52-47 +UBE2D3P1,RP4-680N4 +UBE2D3P1,UBE2D3L +UBE2D3P1,UBE2D3P +UBE2D3P1,UBE2D5P +DDTL,DDT +DDTL,KB-226F1.2 +TLX1NB,APT-B7 +TLX1NB,TD1 +TLX1NB,TDI +DDX39BP1,BAT1P1 +CDKN2B-AS1,ANRIL +CDKN2B-AS1,CDKN2B-AS +CDKN2B-AS1,CDKN2BAS +CDKN2B-AS1,NCRNA00089 +CDKN2B-AS1,PCAT12 +CDKN2B-AS1,p15AS +RPL35AP4,BPG294E21.7 +DDX39BP2,BAT1P2 +DDX39BP2,BPG309N1.15 +MCCD1P2,BPG309N1.16 +GUSBP17,GUSBP9 +SHFM3,C10DUPq24 +SHFM3,DUP10q24 +SHFM3,SHSF3 +TRK-TTT3-4,TRK-TTT3-3 +TRK-TTT3-4,TRNAK3 +TRV-CAC1-5,TRNAV6 +TRV-CAC1-5,TRV6 +MIMT1,LINC00067 +MIMT1,MIM1 +MIMT1,NCRNA00067 +SNHG8,LINC00060 +SNHG8,NCRNA00060 +GTF2IP4,GTF2I +TTTY1B,TTTY1 +TTTY8B,NCRNA00217 +TTTY8B,TTTY8 +TTTY7B,NCRNA00215 +TTTY23B,TTTY23 +POM121C,POM121-2 +SPDYE12,SPDYE12P +MRT9,MRT26 +MRT10,MRT20 +PMS2P7,PMS2L15 +PMS2P7,PMS2LP2 +PMS2P7,PMSR7 +ZSCAN30,ZNF-WYM +ZSCAN30,ZNF397OS +ZSCAN30,ZNF917 +MCTS2P,MCTS2 +MCTS2P,PSIMCT-1 +GAGE8,CT4.8 +GAGE8,GAGE-8 +ANKRD26P3,LINC00414 +UCKL1-AS1,UCKL1-AS +UCKL1-AS1,UCKL1AS +UCKL1-AS1,UCKL1OS +SNORD126,MIR1201 +SNORD126,MIRN1201 +SNORD111B,MIR3647 +LIN28B-AS1,C6orf220 +LIN28B-AS1,LINC00577 +LIN28B-AS1,dJ439I14.1 +LINC00583,C9orf146 +FAM138E,F379 +GINGF4,GGF4 +GINGF4,HGF4 +SNORD58C,U58 +SNORA70C,U70C +HOTAIR,HOXAS +HOTAIR,HOXC-AS4 +HOTAIR,HOXC11-AS1 +HOTAIR,NCRNA00072 +ZGLP1,GATAD3 +ZGLP1,GLP-1 +ZGLP1,GLP1 +FMR1-AS1,ASFMR1 +FMR1-AS1,FMR1-AS +FMR1-AS1,FMR1AS +FMR1-AS1,FMR4 +MIR298,MIRN298 +MIR298,hsa-mir-298 +MIR300,MIRN300 +MIR300,hsa-mir-300 +VTRNA2-1,CBL-3 +VTRNA2-1,CBL3 +VTRNA2-1,MIR886 +VTRNA2-1,MIRN886 +VTRNA2-1,VTRNA2 +VTRNA2-1,hsa-mir-886 +VTRNA2-1,hvg-5 +VTRNA2-1,nc886 +VTRNA2-1,pre-miR-886 +VTRNA2-1,svtRNA2-1a +MIR509-2,MIRN509-2 +MIR450B,MIRN450B +MIR450B,mir-450b +MIR890,MIRN890 +MIR890,hsa-mir-890 +MIR891B,MIRN891B +MIR888,MIRN888 +MIR888,hsa-mir-888 +MIR892B,MIRN892B +MIR541,MIRN541 +MIR541,hsa-mir-541 +MIR541,mir-541 +MIR875,MIRN875 +MIR875,hsa-mir-875 +MIR876,MIRN876 +MIR876,hsa-mir-876 +MIR876,mir-876 +MIR147B,MIRN147B +MIR147B,mir-147b +MIR744,MIRN744 +MIR744,hsa-mir-744 +MIR744,mir-744 +MIR877,MIRN877 +MIR877,hsa-mir-877 +MIR877,miRNA877 +MIR877,mir-877 +MIR665,MIRN665 +MIR665,hsa-mir-665 +MIR665,mir-665 +MIR873,MIRN873 +MIR873,hsa-mir-873 +MIR873,mir-873 +MIR374B,MIRN374B +MIR374B,mir-374b +MIR301B,MIRN301B +MIR301B,mir-301b +MIR216B,MIRN216B +MIR216B,mir-216b +MIR920,MIRN920 +MIR920,hsa-mir-920 +MIR922,MIRN922 +MIR922,hsa-mir-922 +MIR924,MIRN924 +MIR924,hsa-mir-924 +MIR934,MIRN934 +MIR934,hsa-mir-934 +MIR934,mir-934 +MIR935,MIRN935 +MIR935,hsa-mir-935 +MIR935,mir-935 +MIR936,MIRN936 +MIR936,hsa-mir-936 +MIR938,MIRN938 +MIR938,hsa-mir-938 +MIR940,MIRN940 +MIR940,hsa-mir-940 +MIR940,mir-940 +MIR941-1,MIRN941-1 +MIR941-1,mir-941-1 +MIR941-4,MIRN941-4 +MIR942,MIRN942 +MIR942,hsa-mir-942 +MIR942,mir-942 +MIR943,MIRN943 +MIR943,hsa-mir-943 +MIR708,MIRN708 +MIR708,hsa-mir-708 +MIR885,MIRN885 +MIR885,hsa-mir-885 +MIR885,mir-885 +MIR543,MIRN543 +MIR543,hsa-mir-543 +MIR543,mir-543 +MIR208B,MIRN208B +MIR208B,mir-208b +MIR509-3,MIRN509-3 +MIR509-3,mir-509-3 +MIR937,MIRN937 +MIR937,hsa-mir-937 +MIR937,mir-937 +MIR941-2,MIRN941-2 +MIR944,MIRN944 +MIR944,hsa-mir-944 +MIR944,mir-944 +MIR891A,MIRN891A +MIR892A,MIRN892A +MIR874,MIRN874 +MIR874,hsa-mir-874 +MIR874,mir-874 +MIR889,MIRN889 +MIR889,hsa-mir-889 +MIR889,mir-889 +MIR190B,MIRN190B +MIR190B,mir-190b +MIR887,MIRN887 +MIR887,hsa-mir-887 +MIR760,MIRN760 +MIR760,hsa-mir-760 +MIR760,mir-760 +MIR921,MIRN921 +MIR921,hsa-mir-921 +MIR933,MIRN933 +MIR933,hsa-mir-933 +MIR933,mir-933 +MIR939,MIRN939 +MIR939,hsa-mir-939 +MIR939,mir-939 +MIR941-3,MIRN941-3 +MIR297,MIRN297 +MIR297,hsa-mir-297 +MIR365A,MIR365-1 +MIR365A,MIRN365-1 +MIR365A,hsa-mir-365a +MIR365A,mir-365a +MIR365B,MIR365-2 +MIR365B,MIRN365-2 +MIR365B,hsa-mir-365b +MIR365B,mir-365b +GJE1,CX23 +MAFD4,BPAD +LINC00322,C21orf136 +LINC00322,NCRNA00322 +EGOT,EGO +EGOT,NCRNA00190 +GHRLOS,GHRL-AS1 +GHRLOS,GHRLAS +GHRLOS,NCRNA00068 +SNAR-A1,SNAR-A53113498 +SNAR-A2,SNAR-A53129250 +SNAR-A12,SNAR-A53102747 +HLA-Y,HLA-DEL +HLA-Y,HLA-GOR +MINAR2,KIAA1024L +RPL7P24,RPL7_8_586 +SMIM11P1,C6orf161 +SMIM11P1,FAM165A +SMIM11P1,bA33E24.4 +SLC66A2P2,PQLC1P2 +RPS15AP14,RPS15A_4_297 +RPL15P16,RPL15_9_1154 +ATF4P4,ATF4B +RPS3AP48,RPS3A_22_1469 +RPL15P5,RPL15_1_277 +RPL34P7,RPL34_1_168 +RPL18AP11,RPL18A_5_961 +RPL32P21,RPL32_13_977 +WHSC1L2P,HsT36289 +WHSC1L2P,NSD4P +WHSC1L2P,WHSC1L1P +RPL10P11,RPL10_4_817 +KLF7P1,KLF7P +SLC2A3P1,GLUT3P1 +SLC2A3P1,GLUT6 +SLC2A3P1,SLC2A3P +RPL36AP27,RPL36A_9_808 +RPS15AP40,RPS15A_26_1770 +ST8SIA6-AS1,APAL +BORCS8P1,MEF2BNBP1 +RPL26P35,RPL26_15_1614 +RPL17P15,RPL17_5_269 +SLC25A51P3,MCART5P +ADGRA1-AS1,GPR123-AS1 +TMCC1P1,TMCC1P +LINC01233,XLOC_013014 +RPS4XP17,RPS4P17 +RPS4XP17,RPS4X_7_1505 +CLCP2,LGALS10P1 +RPS6P7,RPS6_3_716 +RPS26P39,RPS26_21_1107 +SLC9B1P1,NHEDC1P1 +CD83P1,CD83P +CD83P1,HB15p +CD83P1,b34I8.3 +RPL31P55,RPL31_31_1463 +ZNF667-AS1,MORT +RPS18P3,RPS18_2_171 +WASIR1,NCRNA00286B +HTR5A-AS1,HTR5AOS +RPL35AP20,RPL35A_9_959 +DNM1P35,DNM1DN8-2 +DNM1P35,DNM1DN8.2 +DNM1P35,DNM1DN8@ +DNM1P35,FKSG88 +RPS13P1,RPS13_1_111 +RPL26P17,RPL26_8_492 +TRAPPC3L,BET3L +TRAPPC3L,bA259P20.2 +RPL15P15,RPL15_4_1179 +RPL21P52,RPL21_16_474 +RPS6P24,RPS6_12_1345 +RPS29P7,RPS29_2_60 +LINC00696,C3orf74 +FAM225B,C9orf110 +FAM225B,LINC00256B +FAM225B,NCRNA00256B +RPL35AP36,RPL35A_20_1756 +NIFKP5,MKI67IPP5 +RPL22P10,RPL22_3_253 +RPS24P14,RPS24_4_1115 +GEMIN8P1,FAM51A2P +GEMIN8P1,GEMIN8P +RPS6P8,RPS6_3_713 +CCNJP1,CCNJP +SCP2D1-AS1,C20orf78 +ATP5PBP5,ATP5F1P5 +RPS24P11,RPS24_6_457 +UQCRFS1P1,UQCRFSL1 +RPS18P14,RPS18_10_1747 +MIR29B2CHG,C1orf132 +ATP6V1G1P2,ATP6V1GP2 +CTAGE4,cTAGE-4 +MYHAS,linc-MYH +RPS15AP13,RPS15A_3_241 +UBA52P7,RPL40_2_1281 +CYB5AP2,CYB5P2 +RPL10AP7,RPL10A_2_460 +RPL22P20,RPL22_10_1337 +NIFKP4,MKI67IPP4 +RPL9P30,RPL9_11_1536 +PRELID3BP3,SLMO2P3 +RPLP1P8,RPLP1_2_758 +RPS7P9,RPS7_5_1072 +THAP12P6,PRKRIRP6 +SHLD2P2,FAM35A2 +SHLD2P2,FAM35AP +SHLD2P2,FAM35CP +LINC00476,C9orf130 +LINC00476,"NAG11" +LINC00476,"NAG12" +RPL35AP37,RPL35A_16_1766 +ZNF725P,ZNF725 +RPS15AP29,RPS15A_17_1092 +VPS9D1-AS1,MYU +RPSAP36,RPSA_14_525 +FAM66B,FAM66E +GATA6-AS1,BM742401 +GATA6-AS1,locus5689 +NPM1P4,NG4-5 +NPM1P4,NPMP4 +RPS21P4,RPS21_2_461 +RPS6P16,RPS6_8_1176 +ZBTB42,LCCS6 +ZBTB42,ZNF925 +RPL10AP6,RPL10A_1_376 +LINC01310,WI2-81516E3.1 +GAPDHP62,GAPDHL7 +GAPDHP62,GAPDL7 +RPL3P9,RPL3_6_918 +RPL35AP7,RPL35A_1_109 +RPL3P10,RPL3_5_896 +LSM6P1,HsT818 +LSM6P1,LSM6P +RPL24P5,RPL24_1_143 +RPLP1P4,RPLP1_3_298 +CFAP53P1,CCDC11P1 +KDM4F,JMJD2F +SEMA3F-AS1,RBM5-AS1 +RPL7P25,RPL7_12_684 +THAP12P5,PRKRIRP5 +KTN1-AS1,C14orf33 +KTN1-AS1,MYCLo-3 +TDGF1P7,CR-7 +TDGF1P7,CRIPTO-7 +TDGF1P7,TDGF7 +HIGD1AP1,HIG1 +HIGD1AP1,HIGD1D +HIGD1AP1,HIGD1DP +RPS5P4,RPS5_1_162 +BTNL10,BTN4 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+TRA-AGC12-2,TRNAA31 +TRL-AAG2-1,TRL-AAG2-3 +TRL-AAG2-1,TRNAL32 +TRQ-CTG5-1,TRNAQ27 +TRSUP-TTA2-1,TRNASUP2 +TRS-GCT4-3,TRNAS23 +TRS-GCT4-3,TRS-GCT4-2 +TRK-TTT7-1,TRNAK29 +TRE-TTC1-2,TRE-TTC1-1 +TRE-TTC1-2,TRNAE20 +TRH-GTG1-2,TRNAH10 +TRP-TGG3-3,TRNAP19 +TRP-TGG3-3,TRP-TGG3-2 +TRI-AAT5-2,TRI-AAT5-4 +TRI-AAT5-2,TRNAI20 +TRY-GTA8-1,TRNAY12 +TRN-GTT16-5,TRNAN28 +TRA-TGC4-1,TRNAA32 +TRT-AGT2-2,TRNAT15 +TRA-AGC18-1,TRNAA33 +TRV-TAC3-1,TRNAV26 +TRD-GTC2-9,TRD-GTC2-7 +TRD-GTC2-9,TRNAD15 +TRT-TGT1-1,TRNAT16 +TRX-CAT1-5,TRNAM18 +TRX-CAT1-5,TRX-CAT1-6 +TRA-AGC1-1,TRNAA34 +TRD-GTC2-10,TRD-GTC2-8 +TRD-GTC2-10,TRNAD16 +TRI-TAT3-1,TRNAI21 +TRL-AAG8-1,TRNAL33 +TRH-GTG2-1,TRNAH11 +TRQ-CTG18-1,TRNAQ28 +TRS-AGA1-1,TRNAS24 +TRA-AGC17-1,TRNAA35 +TRM-CAT4-2,TRNAM19 +TRC-GCA20-1,TRNAC22 +TRN-GTT12-1,TRNAN29 +TRC-GCA13-1,TRNAC23 +TRA-AGC14-1,TRNAA36 +TRC-GCA8-1,TRNAC24 +TRT-CGT3-1,TRNAT17 +TRP-AGG3-1,TRNAP20 +TRL-AAG1-1,TRNAL34 +TRV-CAC2-1,TRNAV27 +TRL-CAG1-4,TRNAL35 +TRR-CCG1-1,TRNAR24 +TRR-CCG1-1,TRR-CCG1-2 +TRG-GCC1-1,TRNAG30 +TRK-CTT10-1,TRNAK30 +TRV-CAC6-1,TRNAV28 +TRA-AGC12-1,TRNAA37 +TRL-CAA5-1,TRNAL36 +TRR-TCG3-1,TRNAR25 +TRI-AAT6-1,TRNAI22 +TRV-CAC1-1,TRNAV29 +TRT-AGT4-1,TRNAT18 +TRT-AGT4-1,TRT-AGT5-1 +TRC-GCA21-1,TRNAC25 +TRC-GCA9-3,TRNAC26 +NMTRL-TAA4-1,TRNAL37 +TRT-TGT2-1,TRNAT19 +TRC-GCA9-4,TRNAC27 +TRG-GCC2-1,TRNAG31 +TRN-GTT19-1,TRNAN30 +TRS-AGA2-1,TRNAS25 +TRS-AGA2-1,TRS-AGA2-2 +TRD-GTC2-11,TRD-GTC2-9 +TRD-GTC2-11,TRNAD17 +TRE-CTC1-1,TRNAE21 +TRE-CTC1-3,TRNAE22 +TRI-AAT5-5,TRI-AAT5-2 +TRI-AAT5-5,TRNAI23 +TRP-TGG3-4,TRNAP21 +TRP-TGG3-4,TRP-TGG3-3 +TRQ-CTG12-1,TRNAQ30 +TRK-TTT9-1,TRNAK31 +TRM-CAT4-1,TRNAM7 +TRK-TTT16-1,TRNAK32 +TRI-AAT4-1,TRNAI24 +TRL-CAG1-1,TRNAL38 +TRA-CGC5-1,TRNAA38 +TRR-ACG1-1,TRNAR26 +TRR-ACG1-1,TRR-ACG1-2 +NMTRQ-TTG5-1,TRNAQ31 +TRA-AGC8-2,TRNAA39 +TRK-CTT2-2,TRK-CTT2-3 +TRK-CTT2-2,TRNAK33 +TRA-CGC2-1,TRNAA40 +TRI-AAT7-1,TRNAI8 +TRN-GTT2-2,TRNAN32 +TRV-AAC7-1,TRNAV30 +TRY-GTA4-1,TRNAY13 +TRR-CCT4-1,TRNAR27 +TRE-TTC4-2,TRNAE23 +TRV-TAC4-1,TRNAV31 +TRE-CTC2-1,TRNAE24 +TRR-TCG2-1,TRNAR28 +TRA-AGC13-1,TRNAA41 +TRD-GTC2-1,TRNAD18 +TRC-GCA9-2,TRNAC28 +TRC-GCA2-4,TRC-GCA2-3 +TRC-GCA2-4,TRNAC29 +TRF-GAA1-3,TRF-GAA1-1 +TRF-GAA1-3,TRNAF11 +TRV-CAC3-1,TRNAV32 +TRE-CTC6-1,TRNAE25 +TRE-TTC1-1,TRE-TTC1-2 +TRE-TTC1-1,TRNAE26 +TRT-TGT4-1,TRNAT20 +TRS-AGA6-1,TRNAS26 +TRF-GAA7-1,TRNAF12 +TRQ-TTG4-1,TRNAQ32 +TRA-AGC9-2,TRNAA42 +TRY-GTA5-3,TRNAY14 +TRY-GTA5-3,TRY-GTA5-1 +TRK-TTT3-2,TRNAK34 +TRSUP-CTA1-1,TRNASUP3 +TRI-GAT1-3,TRNAI26 +TRR-ACG2-4,TRNAR29 +TRL-AAG6-1,TRNAL39 +TRS-TGA3-1,TRNAS27 +TRD-GTC8-1,TRNAD19 +TRS-AGA5-1,TRNAS28 +TRT-AGT3-1,TRNAT21 +TRA-AGC22-1,TRNAA43 +TRC-GCA14-1,TRNAC30 +TRW-CCA3-1,TRNAW9 +TRW-CCA3-1,TRW-CCA3-2 +TRY-GTA5-1,TRNAY15 +TRY-GTA5-1,TRY-GTA5-4 +TRT-AGT5-1,TRNAT22 +TRT-AGT5-1,TRT-AGT4-1 +TRK-CTT13-1,TRNAK35P +TRE-CTC12-1,TRNAE27P +TRL-TAG4-1,TRNAL40P +TRSUP-CTA2-1,TRNASUP4P +TRR-CCT7-1,TRNAR30P +TRQ-TTG7-1,TRNAQ33P +NMTRQ-TTG7-1,TRNAQ34P +TRF-GAA8-1,TRNAF13P +TRC-GCA25-1,TRNAC31P +NMTRQ-TTG8-1,TRNAQ35P +TRR-CCT6-2,TRNAR31P +TRE-CTC14-1,TRNAE28P +NMTRQ-TTG9-1,TRNAQ36P +TRQ-CTG11-1,TRNAQ37P +TRSUP-TTA3-1,TRNASUP5P +TRE-TTC10-1,TRNAE29P +TRP-GGG1-1,TRNAP22P +NMTRL-TAA6-1,TRNAL41P +TRE-TTC7-1,TRNAE30P +TRG-CCC7-1,TRNAG32P +TRE-CTC13-1,TRNAE31P +TRG-TCC5-1,TRNAG33P +NMTRQ-CTG1-1,TRNAQ38P +TRI-AAT10-1,TRNAI28P +NMTRP-TGG1-1,TRNAP23P +TRK-CTT12-1,TRNAK36P +TRE-TTC6-1,TRNAE32P +NMTRL-TAA5-1,TRNAL42P +TRK-CTT14-1,TRNAK37P +TRA-AGC23-1,TRNAA44P +TRA-TGC10-1,TRNAA45P +TRSUP-CTA3-1,TRNASUP6P +TRF-GAA12-1,TRNAF14P +TRF-GAA11-1,TRNAF15P +TRQ-TTG5-1,TRNAQ39P +TRI-AAT11-1,TRNAI29P +NMTRQ-TTG1-1,TRNAQ40P +TRP-AGG6-1,TRNAP24P +TRV-CAC14-1,TRNAV33P +NMTRQ-TTG12-1,TRNAQ41P +TRV-CAC11-2,TRNAV34P +TRK-TTT10-1,TRNAK38P +TRI-AAT12-1,TRNAI30P +TRP-AGG5-1,TRNAP25P +TRP-TGG4-1,TRNAP26P +TRE-TTC15-1,TRNAE33P +TRE-TTC14-1,TRNAE34P +TRV-CAC11-1,TRNAV39 +TRS-AGA7-1,TRNAS29P +TRE-CTC11-1,TRNAE35P +NMTRQ-TTG4-1,TRNAQ42P +TRN-GTT23-1,TRNAN34P +TRE-CTC7-1,TRNAE36P +TRE-CTC18-1,TRNAE37P +TRL-AAG7-1,TRNAL43P +TRE-CTC9-1,TRNAE38P +TRD-GTC10-1,TRNAD20P +TRY-GTA12-1,TRNAY16P +TRF-GAA10-1,TRNAF16P +LOC100189498,TRG-CCC6-1 +LOC100189498,TRNAG34P +TRL-CAA7-1,TRNAL44P +TRV-CAC13-1,TRNAV36P +NMTRQ-TTG11-1,TRNAQ43P +TRR-CCT8-1,TRNAR32P +TRQ-CTG16-1,TRNAQ44P +TRK-TTT15-1,TRNAK39P +TRQ-TTG8-1,TRNAQ45P +TRE-CTC4-1,TRNAE39P +TRY-GTA11-1,TRNAY17P +TRC-ACA1-1,TRNAC32P +TRQ-TTG9-1,TRNAQ46P +NMTRQ-TTG6-1,TRNAQ47P +TRG-TCC6-1,TRNAG35P +NMTRS-TGA3-1,TRNAS30P +NMTRV-TAC1-1,TRNAV37P +TRK-TTT14-1,TRNAK40P +TRP-TGG5-1,TRNAP27P +NMTRQ-TTG2-1,TRNAQ48P +TRE-TTC9-1,TRNAE40P +TRK-TTT17-1,TRNAK41P +NMTRL-TAA3-1,TRNAL45P +NMTRL-TAA2-1,TRNAL46P +TRQ-CTG14-1,TRNAQ55 +NMTRQ-TTG10-1,TRNAQ50P +NMTRS-TGA2-1,TRNAS31P +TRS-ACT1-1,TRNAS32P +TRE-CTC15-1,TRNAE41P +TRE-CTC16-1,TRNAE42P +TRA-TGC9-1,TRNAA46P +TRP-AGG4-1,TRNAP28P +TRQ-CTG9-1,TRNAQ51P +TRE-CTC10-1,TRNAE43P +TRF-GAA9-1,TRNAF17P +TRN-GTT22-1,TRNAN36P +NMTRQ-TTG13-1,TRNAQ52P +TRK-CTT16-1,TRNAK42P +TRR-CCT9-1,TRNAR34P +TRQ-CTG13-1,TRNAQ53P +TRK-TTT13-1,TRNAK43P +NMTRQ-TTG15-1,TRNAQ54P +TRV-CAC1-4,TRNAV4 +LDHAP2,LDHAL2 +LDHAP7,LDHAL7 +SUPT4H1P1,SUPT4H1P +NDUFB1P1,NDUFB1P +NDUFB1P1,bcm3095 +FAM32DP,FAM32D +FAM32EP,FAM32E +POROK5,DSAP3 +DEL2Q32Q33,C2DELq32q33 +DEL1Q43Q44,C1DELq42q44 +DEL1Q43Q44,C1DELq43q44 +DEL1Q43Q44,DEL1Q42Q44 +RPL36AP33,RPL36A_15_975 +C2CD4D,FAM148D +POROK2,PPPD1 +POROK6,DSAP4 +MANCR,LINC00704 +RPS24P8,RPS24_4_368 +RPS24P13,RPS24_7_1030 +RPS24P6,RPS24_2_250 +RPS24P16,RPS24_6_1447 +RPS24P17,RPS24_7_1485 +RPS24P7,RPS24_1_225 +DNM1P43,DNM1DN11-10 +DNM1P43,DNM1DN11@ +DNM1P24,DNM1DN15-1 +DNM1P24,DNM1DN15@ +DNM1P25,DNM1DN15-3 +DNM1P25,DNM1DN15@ +DNM1P26,DNM1DN16-1 +DNM1P26,DNM1DN16@ +DNM1P27,DNM1DN16-3 +DNM1P27,DNM1DN16@ +DNM1P36,DNM1DN11-3 +DNM1P36,DNM1DN11@ +DNM1P36,DNM1DN8-3 +DNM1P36,DNM1DN8@ +DNM1P36,DNM1P39 +DNM1P37,DNM1DN10-1 +DNM1P37,DNM1DN10@ +DNM1P38,DNM1DN10-2 +DNM1P38,DNM1DN10@ +DNM1P38,DNM1DN11-8 +DNM1P38,DNM1DN11@ +DNM1P38,DNM1P42 +DNM1P45,DNM1DN12-2 +DNM1P45,DNM1DN12@ +DNM1P47,DNM1DN14-3 +DNM1P47,DNM1DN14@ +DEL1Q21,C1DELq21 +DUP1Q21,C1DUPq21 +WAGRO,DEL11p14p12 +PLSA1,PLSA +GAPDHP44,GAPDH +DUP7Q11.23,C7DUPq11.23 +DEL15Q15.3,C15DELq15.3 +DEL11P15P14,C11DELp15p14 +DEL1P36,C1DELp36 +DUP18PDEL18Q,DUP18qDEL18p +DUP18PDEL18Q,DUP81qDEL18p +DEL2P16.1-P15,C2DELp161-p15 +DEL18P,C18DELp +DEL9P,C9DELp +NIFKP3,MKI67IPP3 +FABP5P12,FABP5L12 +CASC11,CARLO7 +CASC11,CARLo-7 +CASC11,LINC00990 +CASC11,MYMLR +CASC11,TCONS_00014535 +RPL21P112,RPL21_45_1329 +RPL7AP72,RPL7A_34_1809 +RPSAP63,RPSA_33_1813 +SHFLD3,C17DUPp13.3 +SHFLD3,DUP17p13.3 +DEL6PTER,C6DELpter +RPL7P11,RPL7_1_6 +RPL9P12,RPL9_1_50 +RPS2P13,RPS2_1_34 +RPS2P14,RPS2_2_88 +RPL37P9,RPL37_1_4 +RPL39P12,RPL39_1_3 +RPL5P9,RPL5_3_263 +RPL5P12,RPL5_4_502 +RPL5P13,RPL5_5_523 +RPL5P14,RPL5_5_550 +RPL5P15,RPL5_6_566 +RPL5P16,RPL5_7_588 +RPL5P21,RPL5_7_753 +RPL5P17,RPL5_8_591 +RPL5P23,RPL5_8_899 +RPL5P24,RPL5_9_935 +RPL6P15,RPL6_3_613 +RPL6P5,RPL6_4_280 +RPL6P6,RPL6_5_321 +RPL6P21,RPL6_5_786 +RPL6P9,RPL6_6_412 +RPL6P7,RPL6_7_423 +RPL7P15,RPL7_4_398 +RPL7P17,RPL7_6_493 +RPL7P18,RPL7_7_596 +RPL7P19,RPL7_9_617 +RPL8P3,RPL8_1_413 +RPL9P10,RPL9_2_176 +RPL9P13,RPL9_2_278 +RPL9P15,RPL9_4_422 +RPS2P15,RPS2_3_202 +RPS2P19,RPS2_4_428 +RPS2P16,RPS2_8_310 +RPS2P22,RPS2_9_556 +RPS3P4,RPS3_1_652 +RPS6P2,RPS6_1_301 +RPS6P10,RPS6_4_967 +RPS6P11,RPS6_5_956 +RPS6P12,RPS6_5_991 +RPS7P2,RPS7_1_102 +RPS7P6,RPS7_3_462 +RPS7P7,RPS7_3_472 +RPS8P6,RPS8_1_339 +RPSAP16,RPSA_1_135 +RPSAP17,RPSA_3_103 +RPSAP22,RPSA_5_248 +RPSAP23,RPSA_5_268 +RPSAP34,RPSA_8_506 +RPL21P21,RPL21_1_11 +RPL21P22,RPL21_2_29 +RPL21P23,RPL21_2_54 +RPL21P24,RPL21_4_45 +RPL21P25,RPL21_5_46 +RPL21P26,RPL21_6_74 +RPL26P7,RPL26_1_10 +RPL26P8,RPL26_1_22 +RPL26P9,RPL26_2_76 +RPL26P10,RPL26_3_90 +RPL31P12,RPL31_1_62 +RPL32P6,RPL32_1_26 +RPL34P4,RPL34_1_24 +RPL37P7,RPL37_1_52 +RPL39P6,RPL39_1_12 +RPL39P7,RPL39_2_23 +RPL39P8,RPL39_2_85 +RPL39P9,RPL39_4_77 +RPL7AP15,RPL7A_1_95 +RPL7AP16,RPL7A_2_43 +RPL7AP17,RPL7A_3_78 +RPS11P3,RPS11_1_99 +RPS20P5,RPS20_1_55 +RPS20P6,RPS20_1_80 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+SCGB2B1P,Abpbg1p +GCASPC,lnc-SOD2-1 +SCYGR1,KRTAP28-1 +SCYGR3,KRTAP28-3 +SCYGR4,KRTAP28-4 +SCYGR6,KRTAP28-6 +SCYGR8,KRTAP28-8 +SHLD3,CTC-534A2.2 +SHLD3,RINN1 +SCYGR2,KRTAP28-2 +SCYGR10,KRTAP28-10 +SCYGR10,KRTAP28p2 +SCYGR5,KRTAP28-5 +SCYGR7,KRTAP28-7 +SCGB1B1P,Abpa1p +C1DELP35,DEL1p35 +MAPK6-DT,lncMAPK6 +SIRLNT,lncRNA-PRLB +KHDRBS2-OT1,KHDRBS2-OT +SREBF2-AS1,821D11.7 +SREBF2-AS1,RP5 +LOC112694756,ALDA +LOC112694756,ALDOA +LINC02605,AS +LINC02605,IL-7 +SCAT2,CBX5-AS1 +SCAT8,EYS-AS1 +TIMM23B-AGAP6,LINC00843 +ZEBTR,BX111 +DERPC,CHTF8 +LOC113523647,DHS+21.5kb +LOC113604967,DHS+83.7kb +LOC113633874,DHS18 +LOC113633875,DHS18(a) +LOC113633875,DHS18a +LOC113633876,DHS18(b) +LOC113633876,DHS18b +LOC113633877,DHS19 +HEAT2,lnc-ADRA2A-1:1 +LOC113664106,DHS2 +LOC113664107,DHS3 +LOC114255744,sZRS +MIR10395,hsa-mir-10395 +MIR10401,hsa-mir-10401 +RCC2-AS1,LCPAT1 +SLCO1B3-SLCO1B7,LST-3TM12 +SLCO1B3-SLCO1B7,OATP1B3-1B7 +NR4A1AS,NR4A1-AS1 +H3P2,p03 +H3P3,p04 +H3P7,p60 +H3P9,p15 +H3P13,p19 +H2AL3,H2A.L.3 +H2AL3,H2AL1RP +H2AL1MP,H2AL1SP +H2BL1P,H2BP4 +H2BL1P,SubH2Bv +H3P8,p14 +H3P17,p23 +H3P23,p27 +H3P24,p28 +H3P27,p59 +H3P28,p40 +H3P30,p42 +H3P42,p52 +H3P45,p56 +H3P47,p57 +H3P39,p49 +H3P11,p17 +H3P19,p24 +H3P31,p43 +H3P1,p02 +H3P5,p12 +H3P10,p16 +H2AL1Q,H2A.L.1 +H2AL1Q,H2AL1QP +H3P22,p26 +H3P29,p41 +H3P33,p44 +H3P40,p50 +MIR223HG,linc-223 +EGILA,Lnc-EGFR +TRN-GTT3-2,TRNAN15 +LNCDAT,YYLNCT +SCYGR9,KRTAP28-9 +SCYGR9,KRTAP28p1 +PSMA7P1,PSMA7P +C1DUPP36.33,DUP1p36.33 +DUP17Q11.2,C17DUPq11.2 +ELOCP26,ELOC26 +ELOCP26,TCEB1P26 +LOC118142757,GUCA1A +LOC118142757,GUCA1ANB-GUCA1A +DEL13Q33Q34,C13DELq33q34 +GEFSP7,FEB3B +TOMT,COMT2 +HRURF,U2HR +SMIM44,CTF5 +SMIM44,NFI1_HUMAN +SMIM44,NFI2_HUMAN +SMIM44,NFIC +XNDC1N-ZNF705EP-ALG1L9P,LOC100133315-ZNF705P-ALG1L9P diff --git a/openproblems/tasks/_cell_cell_communication/cell_cell_communication_ligand_target/methods/__init__.py b/openproblems/tasks/_cell_cell_communication/cell_cell_communication_ligand_target/methods/__init__.py new file mode 100644 index 0000000000..2475b922e7 --- /dev/null +++ b/openproblems/tasks/_cell_cell_communication/cell_cell_communication_ligand_target/methods/__init__.py @@ -0,0 +1,6 @@ +from ..._common.methods import cellphonedb +from ..._common.methods import connectome +from ..._common.methods import liana +from ..._common.methods import logfc +from ..._common.methods import natmi +from ..._common.methods import sca diff --git a/openproblems/tasks/_cell_cell_communication/cell_cell_communication_ligand_target/metrics/__init__.py b/openproblems/tasks/_cell_cell_communication/cell_cell_communication_ligand_target/metrics/__init__.py new file mode 100644 index 0000000000..e7de268379 --- /dev/null +++ b/openproblems/tasks/_cell_cell_communication/cell_cell_communication_ligand_target/metrics/__init__.py @@ -0,0 +1 @@ +from .odds_ratio import odds_ratio diff --git a/openproblems/tasks/_cell_cell_communication/cell_cell_communication_ligand_target/metrics/odds_ratio.py b/openproblems/tasks/_cell_cell_communication/cell_cell_communication_ligand_target/metrics/odds_ratio.py new file mode 100644 index 0000000000..e5c113c52d --- /dev/null +++ b/openproblems/tasks/_cell_cell_communication/cell_cell_communication_ligand_target/metrics/odds_ratio.py @@ -0,0 +1,8 @@ +from .....tools.decorators import metric +from ..._common.metrics import odds_ratio as _odds_ratio +from ..api import MERGE_KEYS + + +@metric(**_odds_ratio.metadata) +def odds_ratio(adata): + return _odds_ratio(adata, merge_keys=MERGE_KEYS) diff --git a/openproblems/tasks/_cell_cell_communication/cell_cell_communication_source_target/README.md b/openproblems/tasks/_cell_cell_communication/cell_cell_communication_source_target/README.md new file mode 100644 index 0000000000..3d35dde314 --- /dev/null +++ b/openproblems/tasks/_cell_cell_communication/cell_cell_communication_source_target/README.md @@ -0,0 +1,99 @@ +# Cell-cell Communication + +## The task + +The growing availability of single-cell data has sparked an increased +interest in the inference of cell-cell communication (CCC), +with an ever-growing number of computational tools developed for this purpose. + +Different tools propose distinct preprocessing steps with diverse +scoring functions, that are challenging to compare and evaluate. +Furthermore, each tool typically comes with its own set of prior knowledge. +To harmonize these, [Dimitrov et +al, 2022](https://doi.org/10.1038/s41467-022-30755-0) recently developed the +[LIANA](https://github.com/saezlab/liana) framework, which was used +as a foundation for this task. + +The challenges in evaluating the tools are further exacerbated by the +lack of a gold standard to benchmark the performance of CCC methods. In an +attempt to address this, Dimitrov et al use alternative data modalities, including +the spatial proximity of cell types and inferred +downstream cytokine activities, to generate an inferred ground truth. However, +these modalities are only approximations of biological reality and come +with their own assumptions and limitations. In time, the inclusion of more +datasets with known ground truth interactions will become available, from +which the limitations and advantages of the different CCC methods will +be better understood. + +**This subtask evaluates methods in their ability to predict interactions between +spatially-adjacent source cell types and target cell types. This subtask focuses +on the prediction of interactions from steady-state, or single-context, +single-cell data.** + +## The metrics + +Metrics for cell-cell communication aim to characterize how good are +the different scoring methods at prioritizing assumed truth predictions. + +* **Odds ratio**: The odds ratio represents the ratio of true and false +positives within a set of prioritized interactions (top ranked hits) versus +the same ratio for the remainder of the interactions. Thus, in this +scenario odds ratios quantify the strength of association between the +ability of methods to prioritize interactions and those interactions +assigned to the positive class. + +## API + +### Datasets + +Datasets should include cell type annotations in `adata.obs["label"]`, along with some +assumed truth in `adata.uns["ccc_target"]`. The assumed truth could be derived from +various proxies; we refer the reader to [Dimitrov et +al](https://doi.org/10.1038/s41467-022-30755-0) for more details. + +`adata.uns["ccc_target"]` should be a Pandas DataFrame containing all of the +following columns: + +* `response`: `int`, binary response variable indicating whether an interaction is + assumed to have occurred +* `source`: `str`, name of source cell type in interaction +* `target`: `str`, name of target cell type in interaction + +The datasets should also include a +[NCBI taxonomy ID](https://www.ncbi.nlm.nih.gov/Taxonomy/Browser/wwwtax.cgi) +in `adata.uns["target_organism"]` - used to convert the (typically human) prior +knowledge of the CCC methods to the corresponding gene homologs. +`adata.X` should contain the raw counts matrix. + +### Methods + +Methods should predict interactions between cell types without using +`adata.uns["ccc_target"]`. Predicted interactions should be stored in +`adata.uns["ccc_pred"]` as a Pandas DataFrame containing all of the following columns: + +* `score`: `float`, score between `-inf` to `+inf` giving a predicted strength of the + inferred interaction +* `source`: `str`, name of source cell type in interaction +* `target`: `str`, name of target cell type in interaction + +### Prior-knowledge Resource + +Each dataset should be supplemented with a prior knowledge resource of +ligand-receptor interactions, with matching feature IDs. +The resource used in the [Ligand-Target](./cell_cell_communication_ligand_target) +and [Source-Target](./cell_cell_communication_source_target) +tasks was generated as the consensus from multiple manually-curated human +ligand-receptor resources, and includes interactions from +[CellPhoneDB](https://www.nature.com/articles/s41596-020-0292-x), +[CellChatDB](https://www.nature.com/articles/s41467-021-21246-9#disqus_thread), +[ICELLNET](https://www.nature.com/articles/s41467-021-21244-x), +[connectomeDB2020](https://www.nature.com/articles/s41467-020-18873-z), +and [CellTalkDB](https://www.nature.com/articles/s41467-020-18873-z) resources. +All of these were queried via the +[OmniPath database](https://www.embopress.org/doi/full/10.15252/msb.20209923), +and are fixed for each version of LIANA. + +### Metrics + +Metrics should evaluate the concordance between `adata.uns["ccc_target"]` and +`adata.uns["ccc_pred"]` to evaluate the success of a method in predicting interactions. diff --git a/openproblems/tasks/_cell_cell_communication/cell_cell_communication_source_target/__init__.py b/openproblems/tasks/_cell_cell_communication/cell_cell_communication_source_target/__init__.py new file mode 100644 index 0000000000..7da746c648 --- /dev/null +++ b/openproblems/tasks/_cell_cell_communication/cell_cell_communication_source_target/__init__.py @@ -0,0 +1,14 @@ +from ....utils import get_callable_members +from . import api +from . import datasets +from . import methods +from . import metrics + +_task_name = "Cell-Cell Communication Inference (Source-Target)" +_task_summary = "Detect interactions between source and target cell types" + +DEFAULT_LAYER = "counts" + +DATASETS = get_callable_members(datasets) +METHODS = get_callable_members(methods) +METRICS = get_callable_members(metrics) diff --git a/openproblems/tasks/_cell_cell_communication/cell_cell_communication_source_target/api.py b/openproblems/tasks/_cell_cell_communication/cell_cell_communication_source_target/api.py new file mode 100644 index 0000000000..c5ac158adb --- /dev/null +++ b/openproblems/tasks/_cell_cell_communication/cell_cell_communication_source_target/api.py @@ -0,0 +1,15 @@ +from ....tools.decorators import dataset +from .._common import api + +import functools + +MERGE_KEYS = ["source", "target"] + +check_dataset = functools.partial(api.check_dataset, merge_keys=MERGE_KEYS) +check_method = functools.partial(api.check_method, merge_keys=MERGE_KEYS) +sample_method = functools.partial(api.sample_method, merge_keys=MERGE_KEYS) + + +@dataset() +def sample_dataset(): + return api.sample_dataset(merge_keys=MERGE_KEYS) diff --git a/openproblems/tasks/_cell_cell_communication/cell_cell_communication_source_target/datasets/__init__.py b/openproblems/tasks/_cell_cell_communication/cell_cell_communication_source_target/datasets/__init__.py new file mode 100644 index 0000000000..d502bf8fcc --- /dev/null +++ b/openproblems/tasks/_cell_cell_communication/cell_cell_communication_source_target/datasets/__init__.py @@ -0,0 +1 @@ +from .allen_brain_atlas import mouse_brain_atlas diff --git a/openproblems/tasks/_cell_cell_communication/cell_cell_communication_source_target/datasets/allen_brain_atlas.py b/openproblems/tasks/_cell_cell_communication/cell_cell_communication_source_target/datasets/allen_brain_atlas.py new file mode 100644 index 0000000000..f46e17c1f6 --- /dev/null +++ b/openproblems/tasks/_cell_cell_communication/cell_cell_communication_source_target/datasets/allen_brain_atlas.py @@ -0,0 +1,21 @@ +from .....data.allen_brain_atlas import load_mouse_brain_atlas +from .....tools.decorators import dataset +from ..._common.utils import ligand_receptor_resource + + +@dataset( + "Mouse brain atlas", + data_url=load_mouse_brain_atlas.metadata["data_url"], + data_reference=load_mouse_brain_atlas.metadata["data_reference"], + dataset_summary="A murine brain atlas with inferred spatially-adjacent " + "cell types as assumed benchmark truth. Adjacent cell types are inferred " + "from z-transformed deconvolution proportion correlations. Generated from " + "murine brain 10x Visium slides (see Dimitrov et al., 2022).", + image="openproblems-r-extras", +) +def mouse_brain_atlas(test=False): + adata = load_mouse_brain_atlas(test=test) + adata.uns["ligand_receptor_resource"] = ligand_receptor_resource( + adata.uns["target_organism"] + ) + return adata diff --git a/openproblems/tasks/_cell_cell_communication/cell_cell_communication_source_target/methods/__init__.py b/openproblems/tasks/_cell_cell_communication/cell_cell_communication_source_target/methods/__init__.py new file mode 100644 index 0000000000..2475b922e7 --- /dev/null +++ b/openproblems/tasks/_cell_cell_communication/cell_cell_communication_source_target/methods/__init__.py @@ -0,0 +1,6 @@ +from ..._common.methods import cellphonedb +from ..._common.methods import connectome +from ..._common.methods import liana +from ..._common.methods import logfc +from ..._common.methods import natmi +from ..._common.methods import sca diff --git a/openproblems/tasks/_cell_cell_communication/cell_cell_communication_source_target/metrics/__init__.py b/openproblems/tasks/_cell_cell_communication/cell_cell_communication_source_target/metrics/__init__.py new file mode 100644 index 0000000000..e7de268379 --- /dev/null +++ b/openproblems/tasks/_cell_cell_communication/cell_cell_communication_source_target/metrics/__init__.py @@ -0,0 +1 @@ +from .odds_ratio import odds_ratio diff --git a/openproblems/tasks/_cell_cell_communication/cell_cell_communication_source_target/metrics/odds_ratio.py b/openproblems/tasks/_cell_cell_communication/cell_cell_communication_source_target/metrics/odds_ratio.py new file mode 100644 index 0000000000..e5c113c52d --- /dev/null +++ b/openproblems/tasks/_cell_cell_communication/cell_cell_communication_source_target/metrics/odds_ratio.py @@ -0,0 +1,8 @@ +from .....tools.decorators import metric +from ..._common.metrics import odds_ratio as _odds_ratio +from ..api import MERGE_KEYS + + +@metric(**_odds_ratio.metadata) +def odds_ratio(adata): + return _odds_ratio(adata, merge_keys=MERGE_KEYS) diff --git a/openproblems/tasks/denoising/README.md b/openproblems/tasks/denoising/README.md index 7584024ec5..9bfe698781 100644 --- a/openproblems/tasks/denoising/README.md +++ b/openproblems/tasks/denoising/README.md @@ -2,19 +2,54 @@ ## The task -Single-cell RNA-Seq protocols only detect a small fraction of the mRNA molecules present in each cell. As a result, the measurements (UMI counts) observed for each gene and each cell are associated with generally high levels of technical noise (Grün et al., 2014). Denoising describes the task of estimating the true expression level of each gene in each cell. In the single-cell literature, this task is also referred to as *imputation*, a term which is typically used for missing data problems in statistics. Similar to the use of the terms "dropout", "missing data", and "technical zeros", this terminology can create confusion about the underlying measurement process (Sarkar and Stephens, 2020). - -A key challenge in evaluating denoising methods is the general lack of a ground truth. A recent benchmark study (Hou et al., 2020) relied on flow-sorted datasets, mixture control experiments (Tian et al., 2019), and comparisons with bulk RNA-Seq data. Since each of these approaches suffers from specific limitations, it is difficult to combine these different approaches into a single quantitative measure of denoising accuracy. Here, we instead rely on an approach termed molecular cross-validation (MCV), which was specifically developed to quantify denoising accuracy in the absence of a ground truth (Batson et al., 2019). In MCV, the observed molecules in a given scRNA-Seq dataset are first partitioned between a *training* and a *test* dataset. Next, a denoising method is applied to the training dataset. Finally, denoising accuracy is measured by comparing the result to the test dataset. The authors show that both in theory and in practice, the measured denoising accuracy is representative of the accuracy that would be obtained on a ground truth dataset. - -# The metrics - -Metrics for data denoising aim to assess denoising accuracy by comparing the denoised *training* set to the randomly sampled *test* set. - -* **MSE**: The mean squared error between the denoised counts of the training dataset and the true counts of the test dataset after reweighting by the train/test ratio. -* **Poisson**: The Poisson log likelihood of observing the true counts of the test dataset given the distribution given in the denoised dataset. +Single-cell RNA-Seq protocols only detect a fraction of the mRNA molecules present +in each cell. As a result, the measurements (UMI counts) observed for each gene and each +cell are associated with generally high levels of technical noise ([Grün et al., +2014](https://www.nature.com/articles/nmeth.2930)). Denoising describes the task of +estimating the true expression level of each gene in each cell. In the single-cell +literature, this task is also referred to as *imputation*, a term which is typically +used for missing data problems in statistics. Similar to the use of the terms "dropout", +"missing data", and "technical zeros", this terminology can create confusion about the +underlying measurement process ([Sarkar and Stephens, +2020](https://www.biorxiv.org/content/10.1101/2020.04.07.030007v2)). + +A key challenge in evaluating denoising methods is the general lack of a ground truth. A +recent benchmark study ([Hou et al., +2020](https://genomebiology.biomedcentral.com/articles/10.1186/s13059-020-02132-x)) +relied on flow-sorted datasets, mixture control experiments ([Tian et al., +2019](https://www.nature.com/articles/s41592-019-0425-8)), and comparisons with bulk +RNA-Seq data. Since each of these approaches suffers from specific limitations, it is +difficult to combine these different approaches into a single quantitative measure of +denoising accuracy. Here, we instead rely on an approach termed molecular +cross-validation (MCV), which was specifically developed to quantify denoising accuracy +in the absence of a ground truth ([Batson et al., +2019](https://www.biorxiv.org/content/10.1101/786269v1)). In MCV, the observed molecules +in a given scRNA-Seq dataset are first partitioned between a *training* and a *test* +dataset. Next, a denoising method is applied to the training dataset. Finally, denoising +accuracy is measured by comparing the result to the test dataset. The authors show that +both in theory and in practice, the measured denoising accuracy is representative of the +accuracy that would be obtained on a ground truth dataset. + +## The metrics + +Metrics for data denoising aim to assess denoising accuracy by comparing the denoised +*training* set to the randomly sampled *test* set. + +* **MSE**: The mean squared error between the denoised counts of the training dataset + and the true counts of the test dataset after reweighting by the train/test ratio. +* **Poisson**: The Poisson log likelihood of observing the true counts of the test + dataset given the distribution given in the denoised dataset. ## API -Datasets should contain the raw UMI counts in `adata.X`, subsampled to training (`adata.obsm["train"]`) and testing (`adata.obsm["test"]`) datasets using `openproblems.tasks.denoising.datasets.utils.split_data`. - -The task-specific data loader functions should split the provided raw UMI counts into a training and a test dataset, as described by Batson et al., 2019. The training dataset should be stored in `adata.obsm['train']`, and the test dataset should be stored in `adata.obsm['test']`. Methods should store the denoising result in `adata.obsm['denoised']`. Methods should not edit `adata.obsm["train"]` or `adata.obsm["test"]`. +Datasets should contain the raw UMI counts in `adata.X`, subsampled to training +(`adata.obsm["train"]`) and testing (`adata.obsm["test"]`) datasets using +`openproblems.tasks.denoising.datasets.utils.split_data`. + +The task-specific data loader functions should split the provided raw UMI counts into a +training and a test dataset, as described by [Batson et al., +2019](https://www.biorxiv.org/content/10.1101/786269v1). The training dataset should be +stored in `adata.obsm['train']`, and the test dataset should be stored in +`adata.obsm['test']`. Methods should store the denoising result in +`adata.obsm['denoised']`. Methods should not edit `adata.obsm["train"]` or +`adata.obsm["test"]`. diff --git a/openproblems/tasks/denoising/__init__.py b/openproblems/tasks/denoising/__init__.py index 783ca91516..02393d5618 100644 --- a/openproblems/tasks/denoising/__init__.py +++ b/openproblems/tasks/denoising/__init__.py @@ -5,7 +5,9 @@ from . import metrics _task_name = "Denoising" -_task_summary = "TODO" +_task_summary = "Removing noise in sparse single-cell RNA-sequencing count data" + +DEFAULT_LAYER = "counts" DATASETS = utils.get_callable_members(datasets) METHODS = utils.get_callable_members(methods) diff --git a/openproblems/tasks/denoising/datasets/pbmc.py b/openproblems/tasks/denoising/datasets/pbmc.py index 86388a6a87..fcf0fc782b 100644 --- a/openproblems/tasks/denoising/datasets/pbmc.py +++ b/openproblems/tasks/denoising/datasets/pbmc.py @@ -1,15 +1,19 @@ -from ....data.pbmc import load_pbmc +from ....data.tenx import load_tenx_1k_pbmc from ....tools.decorators import dataset from . import utils @dataset( - "1k PBMCs from a Healthy Donor (10x/v3)", - data_url=load_pbmc.metadata["data_url"], - dataset_summary="TODO", + "1k Peripheral blood mononuclear cells", + data_url=load_tenx_1k_pbmc.metadata["data_url"], + data_reference=load_tenx_1k_pbmc.metadata["data_reference"], + dataset_summary=( + "1k Peripheral Blood Mononuclear Cells (PBMCs) from a healthy donor. " + "Sequenced on 10X v3 chemistry in November 2018 by 10X Genomics." + ), image="openproblems-python-extras", ) def pbmc(test=False): - adata = load_pbmc(test=test) + adata = load_tenx_1k_pbmc(test=test) adata = utils.split_data(adata) return adata diff --git a/openproblems/tasks/denoising/methods/__init__.py b/openproblems/tasks/denoising/methods/__init__.py index a2596a2a70..611ee9f2dc 100644 --- a/openproblems/tasks/denoising/methods/__init__.py +++ b/openproblems/tasks/denoising/methods/__init__.py @@ -1,5 +1,7 @@ from .alra import alra from .dca import dca +from .knn_smoothing import knn_smoothing +from .magic import knn_naive from .magic import magic from .magic import magic_approx from .no_denoising import no_denoising diff --git a/openproblems/tasks/denoising/methods/alra.py b/openproblems/tasks/denoising/methods/alra.py index 25e52c72c2..a4d7058e3a 100644 --- a/openproblems/tasks/denoising/methods/alra.py +++ b/openproblems/tasks/denoising/methods/alra.py @@ -12,7 +12,7 @@ method_name="ALRA", paper_name="Zero-preserving imputation of scRNA-seq data using " "low-rank approximation", - paper_url="https://www.biorxiv.org/content/10.1101/397588v1", + paper_url="https://doi.org/10.1101/397588", paper_year=2018, code_url="https://github.com/KlugerLab/ALRA", image="openproblems-r-extras", diff --git a/openproblems/tasks/denoising/methods/knn_smoothing.py b/openproblems/tasks/denoising/methods/knn_smoothing.py new file mode 100644 index 0000000000..6d8aa86d4c --- /dev/null +++ b/openproblems/tasks/denoising/methods/knn_smoothing.py @@ -0,0 +1,22 @@ +from ....tools.decorators import method +from ....tools.utils import check_version + + +@method( + method_name="KNN smoothing", + paper_name="K-nearest neighbor smoothing for high-throughput " + "single-cell RNA-Seq data", + paper_url="https://doi.org/10.1101/217737", + paper_year=2018, + code_url="https://github.com/yanailab/knn-smoothing", + image="openproblems-python-extras", +) +def knn_smoothing(adata, test=False): + import knn_smooth + import numpy as np + + adata.uns["method_code_version"] = check_version("knn_smooth") + X = adata.obsm["train"].transpose().toarray() + X = X.astype(np.float64) + adata.obsm["denoised"] = (knn_smooth.knn_smoothing(X, k=10)).transpose() + return adata diff --git a/openproblems/tasks/denoising/methods/magic.py b/openproblems/tasks/denoising/methods/magic.py index e8decabe93..fa1feae071 100644 --- a/openproblems/tasks/denoising/methods/magic.py +++ b/openproblems/tasks/denoising/methods/magic.py @@ -16,15 +16,28 @@ ) -def _magic(adata, solver): +def _magic(adata, solver, normtype="sqrt", **kwargs): from magic import MAGIC + if normtype == "sqrt": + norm_fn = np.sqrt + denorm_fn = np.square + elif normtype == "log": + norm_fn = np.log1p + denorm_fn = np.expm1 + else: + raise NotImplementedError + X, libsize = scprep.normalize.library_size_normalize( adata.obsm["train"], rescale=1, return_library_size=True ) - X = scprep.transform.sqrt(X) - Y = MAGIC(solver=solver, verbose=False).fit_transform(X, genes="all_genes") - Y = scprep.utils.matrix_transform(Y, np.square) + + X = scprep.utils.matrix_transform(X, norm_fn) + Y = MAGIC(solver=solver, **kwargs, verbose=False).fit_transform( + X, genes="all_genes" + ) + + Y = scprep.utils.matrix_transform(Y, denorm_fn) Y = scprep.utils.matrix_vector_elementwise_multiply(Y, libsize, axis=0) adata.obsm["denoised"] = Y @@ -36,11 +49,23 @@ def _magic(adata, solver): method_name="MAGIC", ) def magic(adata, test=False): - return _magic(adata, solver="exact") + return _magic(adata, solver="exact", normtype="sqrt") @_magic_method( method_name="MAGIC (approximate)", ) def magic_approx(adata, test=False): - return _magic(adata, solver="approximate") + return _magic(adata, solver="approximate", normtype="sqrt") + + +@method( + method_name="KNN Smoothing", + paper_name="KNN Smoothing (baseline)", + paper_url="https://openproblems.bio", + paper_year=2022, + code_url="https://github.com/openproblems-bio/openproblems", + image="openproblems-python-extras", +) +def knn_naive(adata, test=False): + return _magic(adata, solver="exact", normtype="log", decay=None, t=1) diff --git a/openproblems/tasks/denoising/methods/no_denoising.py b/openproblems/tasks/denoising/methods/no_denoising.py index 59ec11590a..40a369af49 100644 --- a/openproblems/tasks/denoising/methods/no_denoising.py +++ b/openproblems/tasks/denoising/methods/no_denoising.py @@ -5,7 +5,7 @@ @method( method_name="No denoising", paper_name="Molecular Cross-Validation for Single-Cell RNA-seq", - paper_url="https://www.biorxiv.org/content/10.1101/786269v1", + paper_url="https://doi.org/10.1101/786269", paper_year=2019, code_url="https://github.com/czbiohub/molecular-cross-validation", ) diff --git a/openproblems/tasks/dimensionality_reduction/README.md b/openproblems/tasks/dimensionality_reduction/README.md index 4f7648f157..96376edad1 100644 --- a/openproblems/tasks/dimensionality_reduction/README.md +++ b/openproblems/tasks/dimensionality_reduction/README.md @@ -1,17 +1,66 @@ -# Dimensionality reduction +# Dimensionality reduction for visualisation ## The task -Dimensionality reduction is one of the key challenges in single-cell data representation. -Routine single-cell RNA sequencing (scRNA-seq) experiments measure cells in roughly 20,000-30,000 dimensions (i.e., features - mostly gene transcripts but also other functional elements encoded in mRNA such as lncRNAs). -Since its inception, scRNA-seq experiments have been growing in terms of the number of cells measured. -Originally, cutting-edge SmartSeq experiments would yield a few hundred cells, at best. -Now, it is not uncommon to see experiments that yield over 100,000 cells or even > 1 million cells. +Dimensionality reduction is one of the key challenges in single-cell data +representation. Routine single-cell RNA sequencing (scRNA-seq) experiments measure cells +in roughly 20,000-30,000 dimensions (i.e., features - mostly gene transcripts but also +other functional elements encoded in mRNA such as lncRNAs). Since its inception, +scRNA-seq experiments have been growing in terms of the number of cells measured. +Originally, cutting-edge SmartSeq experiments would yield a few hundred cells, at best. +Now, it is not uncommon to see experiments that yield over [100,000 +cells]() or even [> 1 million +cells.](https://doi.org/10.1126/science.aba7721) + +Each *feature* in a dataset functions as a single dimension. While each of the ~30,000 +dimensions measured in each cell contribute to an underlying data structure, the overall +structure of the data is challenging to display in few dimensions due to data sparsity +and the [*"curse of +dimensionality"*](https://en.wikipedia.org/wiki/Curse_of_dimensionality) (distances in +high dimensional data don’t distinguish data points well). Thus, we need to find a way +to [dimensionally reduce](https://en.wikipedia.org/wiki/Dimensionality_reduction) the +data for visualization and interpretation. -Each *feature* in a dataset functions as a single dimension. -While each of the ~30,000 dimensions measured in each cell (not accounting for roughly 75-90% data dropout per cell, another issue entirely), likely contribute to some sort of data structure, the overall structure of the data is diluted due to the *"curse of dimensionality"*. -In short, it's difficult to visualize the contribution of each individual gene in a way that makes sense to the human eye, i.e., two or three dimensions (at most). -Thus, we need to find a way to dimensionally reduce the data for visualization and interpretation. +## The metrics + +* **Root mean square error**: the square root of the mean squared difference between + Euclidean distances in the high-dimensional data and Euclidean distances in the + dimension-reduced data. +* **Trustworthiness**: a measurement of similarity between the rank of each point's + nearest neighbors in the high-dimensional data and the reduced data ([Venna & Kaski, + 2001](http://dx.doi.org/10.1007/3-540-44668-0_68)). +* **Density preservation**: similarity between local densities in the high-dimensional + data and the reduced data ([Narayan, Berger & Cho, + 2020](https://doi.org/10.1038/s41587-020-00801-7)) +* **NN Ranking**: a set of metrics from + [pyDRMetrics](https://doi.org/10.17632/jbjd5fmggh.2) relating to the preservation + of nearest neighbors in the high-dimensional data and the reduced data. + +## API + +**Datasets** should provide un-normalized raw counts in `adata.X`. + +**Methods** should assign dimensionally-reduced 2D embedding coordinates to +`adata.obsm['X_emb']`. + +**Metrics** should calculate the quality or "goodness of fit" of a dimensional reduction +**method**. If the un-normalized input counts matrix is required by the matrix it can be +accessed in `adata.layers["counts"]`. + +## Pre-processing + +Different methods can require different pre-processing of the data. Standard +pre-processing functions are available as part of the `tools` module. Where possible +each **method** should first call one of these functions and use the processed `adata.X` +slot as the input to the method. Raw counts are also stored in `adata.layers["counts"]` +by the standard pre-processing functions, if a method performs its own pre-processing it +should also do this for use by metrics. For most methods a standard pre-processing with +the `log_cpm_hvg()` function is used which normalizes the expression matrix to counts +per million (CPM), performs a log transformation and subsets the data to highly-variable +genes (HVGs) as selected by scanpy's `high_variable_genes(adata, n_top_genes=n_genes, +flavor="cell_ranger")` (1000 genes by default). Variants of methods can be created by +applying different pre-processing prior to the method itself (see `phate.py` for an +example). ## The methods @@ -19,50 +68,68 @@ Thus, we need to find a way to Kruskel's stress uses the RMSE, more or less in the now commonly-used MDS (multi-dimensional scaling). -We can calculate and plot Kruskel's stress to get an idea where the majority of distortion of the ***topography*** of the data in high-dimensional space. +[Kruskel's +stress](http://cda.psych.uiuc.edu/psychometrika_highly_cited_articles/kruskal_1964a.pdf) +uses the RMSE, more or less in the now commonly-used MDS (multi-dimensional scaling). We +can calculate and plot Kruskel's stress to get an idea where the majority of distortion +of the ***topography*** of the data in high-dimensional space. ### Trustworthiness -_Adapted from the [sklearn documentation.](https://scikit-learn.org/stable/modules/generated/sklearn.manifold.trustworthiness.html)_ +*Adapted from the [sklearn +documentation.](https://scikit-learn.org/stable/modules/generated/sklearn.manifold.trustworthiness.html)* -Trustworthiness expresses to what extent the local structure in an embedding is retained. The trustworthiness is within [0, 1]. It is defined as +Trustworthiness expresses to what extent the local structure in an embedding is +retained. The trustworthiness is within [0, 1]. It is defined as $$ T(k) = 1 - \frac{2}{nk (2n - 3k - 1)} \sum^n_{i=1} \sum_{j \in \mathcal{N}_{i}^{k}} \max(0, (r(i, j) - k)) $$ -where for each sample i, $\mathcal{N}_{i}^{k}$ are its k nearest neighbors in the output space, and every sample j is its $r(i, j)$-th nearest neighbor in the input space. -In other words, any unexpected nearest neighbors in the output space are penalised in proportion to their rank in the input space. +where for each sample i, $\mathcal{N}_{i}^{k}$ are its k nearest neighbors in the output +space, and every sample j is its $r(i, j)$-th nearest neighbor in the input space. In +other words, any unexpected nearest neighbors in the output space are penalised in +proportion to their rank in the input space. References: - * "Neighborhood Preservation in Nonlinear Projection Methods: An Experimental Study" J. Venna, S. Kaski - * "Learning a Parametric Embedding by Preserving Local Structure" L.J.P. van der Maaten + +* "Neighborhood Preservation in Nonlinear Projection Methods: An Experimental Study" J. + Venna, S. Kaski +* "Learning a Parametric Embedding by Preserving Local Structure" L.J.P. van der Maaten ### Density preservation [Reference](https://www.nature.com/articles/s41587-020-00801-7) The local density preservation metric that is part of the cost function for densMAP. -Some parts of this are re-implemented as the are not exposed my the [umap-learn package](https://umap-learn.readthedocs.io). +Some parts of this are re-implemented as the are not exposed my the [umap-learn +package.](https://umap-learn.readthedocs.io) ### NN Ranking [Reference](https://doi.org/10.1016/j.heliyon.2021.e06199) A set of metrics from the [pyDRMetrics package](https://doi.org/10.17632/jbjd5fmggh.2). -The implementation uses a slightly modified version of the original source code rather than the PyPI package that is now available. +The implementation uses a slightly modified version of the original source code rather +than the PyPI package that is now available. * Continuity - Measures error of hard extrusions -* co-KNN size - Counts how many points are in both k-nearest neighbors before and after the DR +* co-KNN size - Counts how many points are in both k-nearest neighbors before and after + the DR * co-KNN AUC - The area under the co-KNN curve -* Local continuity meta criterion - co-KNN size with baseline removal which favors locality more +* Local continuity meta criterion - co-KNN size with baseline removal which favors + locality more * Local property metric - Summary of the local co-KNN * Global property metric - Summary of the global co-KNN @@ -162,38 +249,29 @@ The implementation uses a slightly modified version of the original source code ![Stress-plot RMASE CITE-seq](https://i.imgur.com/ulyAF9j.png) -Above is a "complex heatmap", which aims to show the regions that contribute the most stress. -You can see that while a majority of the stress comes from the left side of the plot (as shown by the top of the complex heat map), the center of that left set of clusters does not contribute much to the stress, leading us to believe that by the measure of RMSE, the topology is relatively well-preserved. -The stress mostly comes from the clusters at the top and bottom of that group of clusters spread across the second PC. +Above is a "complex heatmap", which aims to show the regions that contribute the most +stress. You can see that while a majority of the stress comes from the left side of the +plot (as shown by the top of the complex heat map), the center of that left set of +clusters does not contribute much to the stress, leading us to believe that by the +measure of RMSE, the topology is relatively well-preserved. The stress mostly comes from +the clusters at the top and bottom of that group of clusters spread across the second +PC. ![RMSE PCA CITE-seq](https://i.imgur.com/H5rvIf6.png) We performed principle component analysis, obtaining the first 50 components. -We can then calculate the relative stress using the RMSE for each, in comparison to the original data, $y$. As one might suspect, the more components used, the lower the amount of distortion of the original data. +We can then calculate the relative stress using the RMSE for each, in comparison to the +original data, $y$. As one might suspect, the more components used, the lower the amount +of distortion of the original data. ![RMSE Comparison](https://i.imgur.com/EoI72TI.png) -We can make this comparison across multiple dimensionality reduction methods. -We can see that *t*-SNE seems to distort the data the least, in terms of pairwise euclidean distances. -This does not necessarily mean the data is best represented by *t*-SNE, however. -There are multiple means of measuring the "goodness" of a dimensionality reduction; RMSE is simply one of them. - -## API - -**Datasets** should provide un-normalized raw counts in `adata.X`. - -**Methods** should assign dimensionally-reduced 2D embedding coordinates to `adata.obsm['X_emb']`. - -**Metrics** should calculate the quality or "goodness of fit" of a dimensional reduction **method**. -If the un-normalized input counts matrix is required by the matrix it can be accessed in `adata.layers["counts"]`. - -## Pre-processing - -Different methods can require different pre-processing of the data. -Standard pre-processing functions are available as part of the `tools` module. -Where possible each **method** should first call one of these functions and use the processed `adata.X` slot as the input to the method. -Raw counts are also stored in `adata.layers["counts"]` by the standard pre-processing functions, if a method performs its own pre-processing it should also do this for use by metrics. -For most methods a standard pre-processing with the `log_cpm_hvg()` function is used which normalizes the expression matrix to counts per million (CPM), performs a log transformation and subsets the data to highly-variable genes (HVGs) as selected by scanpy's `high_variable_genes(adata, n_top_genes=n_genes, flavor="cell_ranger")` (1000 genes by default). -Variants of methods can be created by applying different pre-processing prior to the method itself (see `phate.py` for an example). +We can make this comparison across multiple dimensionality reduction methods. We can see +that *t*-SNE seems to distort the data the least, in terms of pairwise euclidean +distances. This does not necessarily mean the data is best represented by *t*-SNE, +however. There are multiple means of measuring the "goodness" of a dimensionality +reduction; RMSE is simply one of them. -1. Raimundo, F., Vallot, C. & Vert, J. Tuning parameters of dimensionality reduction methods for single-cell RNA-seq analysis. Genome Biol 21, 212 (2020). https://doi.org/10.1186/s13059-020-02128-7 +1. Raimundo, F., Vallot, C. & Vert, J. Tuning parameters of dimensionality reduction + methods for single-cell RNA-seq analysis. Genome Biol 21, 212 (2020). + diff --git a/openproblems/tasks/dimensionality_reduction/__init__.py b/openproblems/tasks/dimensionality_reduction/__init__.py index 4b14879549..c32292b3b7 100644 --- a/openproblems/tasks/dimensionality_reduction/__init__.py +++ b/openproblems/tasks/dimensionality_reduction/__init__.py @@ -4,8 +4,12 @@ from . import methods from . import metrics -_task_name = "Dimensionality reduction manifold preservation" -_task_summary = "TODO" +_task_name = "Dimensionality reduction for visualisation" +_task_summary = ( + "Reduction of high-dimensional datasets to 2D for visualization & interpretation" +) + +DEFAULT_LAYER = "counts" DATASETS = utils.get_callable_members(datasets) METHODS = utils.get_callable_members(methods) diff --git a/openproblems/tasks/dimensionality_reduction/datasets/__init__.py b/openproblems/tasks/dimensionality_reduction/datasets/__init__.py index 1c8ba6c669..0c41aaa00b 100644 --- a/openproblems/tasks/dimensionality_reduction/datasets/__init__.py +++ b/openproblems/tasks/dimensionality_reduction/datasets/__init__.py @@ -1,3 +1,3 @@ -from .human_blood_nestorowa2016 import human_blood_nestorowa2016 from .mouse_blood_olssen_labelled import olsson_2016_mouse_blood +from .mouse_hspc_nestorowa2016 import mouse_hspc_nestorowa2016 from .tenx_5k_pbmc import tenx_5k_pbmc diff --git a/openproblems/tasks/dimensionality_reduction/datasets/human_blood_nestorowa2016.py b/openproblems/tasks/dimensionality_reduction/datasets/human_blood_nestorowa2016.py deleted file mode 100644 index 385e89ac2f..0000000000 --- a/openproblems/tasks/dimensionality_reduction/datasets/human_blood_nestorowa2016.py +++ /dev/null @@ -1,11 +0,0 @@ -from ....data.human_blood_nestorowa2016 import load_human_blood_nestorowa2016 -from ....tools.decorators import dataset - - -@dataset( - "Human blood (HSCs and differentiation thereof). Nestorowa, et al. Blood. 2016", - data_url=load_human_blood_nestorowa2016.metadata["data_url"], - dataset_summary="TODO", -) -def human_blood_nestorowa2016(test=False): - return load_human_blood_nestorowa2016(test=test) diff --git a/openproblems/tasks/dimensionality_reduction/datasets/mouse_blood_olssen_labelled.py b/openproblems/tasks/dimensionality_reduction/datasets/mouse_blood_olssen_labelled.py index eafd3b3244..bb3b3ccb94 100644 --- a/openproblems/tasks/dimensionality_reduction/datasets/mouse_blood_olssen_labelled.py +++ b/openproblems/tasks/dimensionality_reduction/datasets/mouse_blood_olssen_labelled.py @@ -3,9 +3,11 @@ @dataset( - "Mouse blood. Olsson, et al. Nature. 2016", + "Mouse myeloid lineage differentiation", data_url=load_olsson_2016_mouse_blood.metadata["data_url"], - dataset_summary="TODO", + data_reference=load_olsson_2016_mouse_blood.metadata["data_reference"], + dataset_summary="Myeloid lineage differentiation from mouse blood. " + "Sequenced by SMARTseq in 2016 by Olsson et al.", ) def olsson_2016_mouse_blood(test=False): return load_olsson_2016_mouse_blood(test=test) diff --git a/openproblems/tasks/dimensionality_reduction/datasets/mouse_hspc_nestorowa2016.py b/openproblems/tasks/dimensionality_reduction/datasets/mouse_hspc_nestorowa2016.py new file mode 100644 index 0000000000..b010474aa0 --- /dev/null +++ b/openproblems/tasks/dimensionality_reduction/datasets/mouse_hspc_nestorowa2016.py @@ -0,0 +1,13 @@ +from ....data.mouse_hspc_nestorowa2016 import load_mouse_hspc_nestorowa2016 +from ....tools.decorators import dataset + + +@dataset( + "Mouse hematopoietic stem cell differentiation", + data_url=load_mouse_hspc_nestorowa2016.metadata["data_url"], + data_reference=load_mouse_hspc_nestorowa2016.metadata["data_reference"], + dataset_summary="1.6k hematopoietic stem and progenitor cells from mouse bone " + "marrow. Sequenced by Smart-seq2.", +) +def mouse_hspc_nestorowa2016(test=False): + return load_mouse_hspc_nestorowa2016(test=test) diff --git a/openproblems/tasks/dimensionality_reduction/datasets/tenx_5k_pbmc.py b/openproblems/tasks/dimensionality_reduction/datasets/tenx_5k_pbmc.py index 932ed7b106..3b94989deb 100644 --- a/openproblems/tasks/dimensionality_reduction/datasets/tenx_5k_pbmc.py +++ b/openproblems/tasks/dimensionality_reduction/datasets/tenx_5k_pbmc.py @@ -1,12 +1,15 @@ -from ....data.tenx_5k_pbmc import load_tenx_5k_pbmc +from ....data.tenx import load_tenx_5k_pbmc from ....tools.decorators import dataset @dataset( - "5k Peripheral blood mononuclear cells (PBMCs) from a healthy donor. " - "10x Genomics; July 24, 2019.", + "5k Peripheral blood mononuclear cells", data_url=load_tenx_5k_pbmc.metadata["data_url"], - dataset_summary="TODO", + data_reference=load_tenx_5k_pbmc.metadata["data_reference"], + dataset_summary=( + "5k Peripheral Blood Mononuclear Cells (PBMCs) from a healthy donor. " + "Sequenced on 10X v3 chemistry in July 2019 by 10X Genomics." + ), ) def tenx_5k_pbmc(test=False): return load_tenx_5k_pbmc(test=test) diff --git a/openproblems/tasks/dimensionality_reduction/methods/pca.py b/openproblems/tasks/dimensionality_reduction/methods/pca.py index 5fdcb7d3f6..e81772d1f0 100644 --- a/openproblems/tasks/dimensionality_reduction/methods/pca.py +++ b/openproblems/tasks/dimensionality_reduction/methods/pca.py @@ -8,7 +8,7 @@ @method( method_name="Principle Component Analysis (PCA) (logCPM, 1kHVG)", paper_name="On lines and planes of closest fit to systems of points in space", - paper_url="https://www.tandfonline.com/doi/abs/10.1080/14786440109462720", + paper_url="https://doi.org/10.1080/14786440109462720", paper_year=1901, code_url="https://scikit-learn.org/stable/modules/generated/" "sklearn.decomposition.PCA.html", diff --git a/openproblems/tasks/dimensionality_reduction/methods/tsne.py b/openproblems/tasks/dimensionality_reduction/methods/tsne.py index 2752c5f57d..6b987c6457 100644 --- a/openproblems/tasks/dimensionality_reduction/methods/tsne.py +++ b/openproblems/tasks/dimensionality_reduction/methods/tsne.py @@ -8,7 +8,7 @@ @method( method_name="“t-Distributed Stochastic Neighbor Embedding (t-SNE) (logCPM, 1kHVG)", paper_name="Visualizing Data using t-SNE", - paper_url="https://lvdmaaten.github.io/publications/papers/JMLR_2008.pdf", + paper_url="https://www.jmlr.org/papers/v9/vandermaaten08a.html", paper_year=2008, code_url="https://scikit-learn.org/stable/modules/generated/" "sklearn.manifold.TSNE.html#sklearn.manifold.TSNE", diff --git a/openproblems/tasks/label_projection/README.md b/openproblems/tasks/label_projection/README.md index ba155ed13b..5a9dbc4951 100644 --- a/openproblems/tasks/label_projection/README.md +++ b/openproblems/tasks/label_projection/README.md @@ -2,18 +2,41 @@ ## The task -A major challenge for integrating single cell datasets is creating matching cell type annotations for each cell. One of the most common strategies for annotating cell types is referred to as ["cluster-then-annotate"](https://www.nature.com/articles/s41576-018-0088-9) whereby cells are aggregated into clusters based on feature similarity and then manually characterized based on differential gene expression or previously identified marker genes. Recently, methods have emerged to build on this strategy and annotate cells using [known marker genes](https://www.nature.com/articles/s41592-019-0535-3). However, these strategies pose a difficulty for integrating atlas-scale datasets as the particular annotations may not match. - -To ensure that the cell type labels in newly generated datasets match existing reference datasets, some methods align cells to a previously annotated [reference dataset](https://academic.oup.com/bioinformatics/article/35/22/4688/54802990) and then _project_ labels from the reference to the new dataset. - -Here, we compare methods for annotation based on a reference dataset. The datasets consist of two or more samples of single cell profiles that have been manually annotated with matching labels. These datasets are then split into training and test batches, and the task of each method is to train a cell type classifer on the training set and project those labels onto the test set. +A major challenge for integrating single cell datasets is creating matching cell type +annotations for each cell. One of the most common strategies for annotating cell types +is referred to as +["cluster-then-annotate"](https://www.nature.com/articles/s41576-018-0088-9) whereby +cells are aggregated into clusters based on feature similarity and then manually +characterized based on differential gene expression or previously identified marker +genes. Recently, methods have emerged to build on this strategy and annotate cells +using [known marker genes](https://www.nature.com/articles/s41592-019-0535-3). However, +these strategies pose a difficulty for integrating atlas-scale datasets as the +particular annotations may not match. + +To ensure that the cell type labels in newly generated datasets match existing reference +datasets, some methods align cells to a previously annotated [reference +dataset](https://academic.oup.com/bioinformatics/article/35/22/4688/54802990) and then +_project_ labels from the reference to the new dataset. + +Here, we compare methods for annotation based on a reference dataset. The datasets +consist of two or more samples of single cell profiles that have been manually annotated +with matching labels. These datasets are then split into training and test batches, and +the task of each method is to train a cell type classifer on the training set and +project those labels onto the test set. ## The metrics -Metrics for label projection aim to characterize how well each classifer correctly assigns cell type labels to cells in the test set. + +Metrics for label projection aim to characterize how well each classifer correctly +assigns cell type labels to cells in the test set. * **Accuracy**: Average number of correctly applied labels. -* **F1 score**: The [F1 score](https://scikit-learn.org/stable/modules/generated/sklearn.metrics.f1_score.html) is a weighted average of the precision and recall over all class labels, where an F1 score reaches its best value at 1 and worst score at 0, where each class contributes to the score relative to its frequency in the dataset. -* **Macro F1 score**: The macro F1 score is an unweighted F1 score, where each class contributes equally, regardless of its frequency. +* **F1 score**: The [F1 + score](https://scikit-learn.org/stable/modules/generated/sklearn.metrics.f1_score.html) + is a weighted average of the precision and recall over all class labels, where an F1 + score reaches its best value at 1 and worst score at 0, where each class contributes + to the score relative to its frequency in the dataset. +* **Macro F1 score**: The macro F1 score is an unweighted F1 score, where each class + contributes equally, regardless of its frequency. ## API @@ -23,10 +46,13 @@ Datasets should contain the following attributes: * `adata.obs["batch"]` with information of batches in the data, and * `adata.obs["is_train"]` with a train vs. test split -It should be noted that datasets may only contain a single batch, or not contain discriminative batch information. +It should be noted that datasets may only contain a single batch, or not contain +discriminative batch information. -Methods should assign output celltype labels to `adata.obs['labels_pred']` using only the labels from the training data. +Methods should assign output celltype labels to `adata.obs['labels_pred']` using only +the labels from the training data. Note that the true labels are contained in `adata['labels']`. -Metrics can compare `adata['labels']` to `adata.obs['labels_pred']` using only the labels from the test data. +Metrics can compare `adata['labels']` to `adata.obs['labels_pred']` using only the +labels from the test data. diff --git a/openproblems/tasks/label_projection/__init__.py b/openproblems/tasks/label_projection/__init__.py index 8817ee30c5..ee905a24c4 100644 --- a/openproblems/tasks/label_projection/__init__.py +++ b/openproblems/tasks/label_projection/__init__.py @@ -5,7 +5,9 @@ from . import metrics _task_name = "Label Projection" -_task_summary = "TODO" +_task_summary = "Automated cell type annotation from rich, labeled reference data" + +DEFAULT_LAYER = "counts" DATASETS = utils.get_callable_members(datasets) METHODS = utils.get_callable_members(methods) diff --git a/openproblems/tasks/label_projection/api.py b/openproblems/tasks/label_projection/api.py index c83894bda4..6e9cc6517d 100644 --- a/openproblems/tasks/label_projection/api.py +++ b/openproblems/tasks/label_projection/api.py @@ -21,6 +21,7 @@ def check_dataset(adata): def check_method(adata): """Check that method output fits expected API.""" assert "labels_pred" in adata.obs + assert np.issubdtype(adata.obs["is_train"].dtype, bool) return True diff --git a/openproblems/tasks/label_projection/datasets/cengen.py b/openproblems/tasks/label_projection/datasets/cengen.py index b40d7cc7ce..6089b2fad6 100644 --- a/openproblems/tasks/label_projection/datasets/cengen.py +++ b/openproblems/tasks/label_projection/datasets/cengen.py @@ -7,7 +7,9 @@ @dataset( "CeNGEN (by batch)", data_url=load_cengen.metadata["data_url"], - dataset_summary="TODO", + data_reference=load_cengen.metadata["data_reference"], + dataset_summary="100k FACS-isolated C. elegans neurons from 17 experiments " + "sequenced on 10x Genomics. Split into train/test by experimental batch.", ) def cengen_batch(test=False): adata = load_cengen(test=test) @@ -27,7 +29,9 @@ def cengen_batch(test=False): @dataset( "CeNGEN (random split)", data_url=load_cengen.metadata["data_url"], - dataset_summary="TODO", + data_reference=load_cengen.metadata["data_reference"], + dataset_summary="100k FACS-isolated C. elegans neurons from 17 experiments " + "sequenced on 10x Genomics. Split into train/test randomly.", ) def cengen_random(test=False): adata = load_cengen(test=test) diff --git a/openproblems/tasks/label_projection/datasets/pancreas.py b/openproblems/tasks/label_projection/datasets/pancreas.py index 043c730f6b..0981ed8ed0 100644 --- a/openproblems/tasks/label_projection/datasets/pancreas.py +++ b/openproblems/tasks/label_projection/datasets/pancreas.py @@ -8,7 +8,10 @@ @dataset( "Pancreas (by batch)", data_url=load_pancreas.metadata["data_url"], - dataset_summary="TODO", + data_reference=load_pancreas.metadata["data_reference"], + dataset_summary="Human pancreatic islet scRNA-seq data from 6 datasets " + "across technologies (CEL-seq, CEL-seq2, Smart-seq2, inDrop, Fluidigm C1, " + "and SMARTER-seq). Split into train/test by experimental batch.", ) def pancreas_batch(test=False): adata = load_pancreas(test=test) @@ -28,7 +31,10 @@ def pancreas_batch(test=False): @dataset( "Pancreas (random split)", data_url=load_pancreas.metadata["data_url"], - dataset_summary="TODO", + data_reference=load_pancreas.metadata["data_reference"], + dataset_summary="Human pancreatic islet scRNA-seq data from 6 datasets " + "across technologies (CEL-seq, CEL-seq2, Smart-seq2, inDrop, Fluidigm C1, " + "and SMARTER-seq). Split into train/test randomly.", ) def pancreas_random(test=False): adata = load_pancreas(test=test) @@ -44,9 +50,12 @@ def pancreas_random(test=False): @dataset( - r"Pancreas (random split with 20% label noise", + "Pancreas (random split with label noise)", data_url=load_pancreas.metadata["data_url"], - dataset_summary="TODO", + data_reference=load_pancreas.metadata["data_reference"], + dataset_summary="Human pancreatic islet scRNA-seq data from 6 datasets " + "across technologies (CEL-seq, CEL-seq2, Smart-seq2, inDrop, Fluidigm C1, " + "and SMARTER-seq). Split into train/test randomly with 20% label noise.", ) def pancreas_random_label_noise(test=False): adata = load_pancreas(test=test) diff --git a/openproblems/tasks/label_projection/datasets/tabula_muris_senis.py b/openproblems/tasks/label_projection/datasets/tabula_muris_senis.py index cd24af3425..60780fafa1 100644 --- a/openproblems/tasks/label_projection/datasets/tabula_muris_senis.py +++ b/openproblems/tasks/label_projection/datasets/tabula_muris_senis.py @@ -7,10 +7,14 @@ @dataset( "Tabula Muris Senis Lung (random split)", data_url=load_tabula_muris_senis.metadata["data_url"], - dataset_summary="TODO", + data_reference=load_tabula_muris_senis.metadata["data_reference"], + dataset_summary="All lung cells from Tabula Muris Senis, a 500k cell-atlas from 18 " + "organs and tissues across the mouse lifespan. Split into train/test randomly.", ) def tabula_muris_senis_lung_random(test=False): - adata = load_tabula_muris_senis(test=test, organ_list=["lung"]) + adata = load_tabula_muris_senis( + test=test, organ_list=["lung"], method_list=["droplet"] + ) adata.obs["labels"] = adata.obs["free_annotation"] adata.obs["batch"] = adata.obs["mouse.id"] adata.obs["is_train"] = np.random.choice( diff --git a/openproblems/tasks/label_projection/datasets/zebrafish.py b/openproblems/tasks/label_projection/datasets/zebrafish.py index 21e6be957a..30999ed261 100644 --- a/openproblems/tasks/label_projection/datasets/zebrafish.py +++ b/openproblems/tasks/label_projection/datasets/zebrafish.py @@ -7,7 +7,10 @@ @dataset( "Zebrafish (by labels)", data_url=load_zebrafish.metadata["data_url"], - dataset_summary="TODO", + data_reference=load_zebrafish.metadata["data_reference"], + dataset_summary="90k cells from zebrafish embryos throughout the first day of " + "development, with and without a knockout of chordin, an important developmental " + "gene. Split into train/test by laboratory.", ) def zebrafish_labels(test=False): adata = load_zebrafish(test=test) @@ -20,7 +23,10 @@ def zebrafish_labels(test=False): @dataset( "Zebrafish (random split)", data_url=load_zebrafish.metadata["data_url"], - dataset_summary="TODO", + data_reference=load_zebrafish.metadata["data_reference"], + dataset_summary="90k cells from zebrafish embryos throughout the first day of " + "development, with and without a knockout of chordin, an important developmental " + "gene. Split into train/test randomly.", ) def zebrafish_random(test=False): adata = load_zebrafish(test=test) diff --git a/openproblems/tasks/label_projection/methods/__init__.py b/openproblems/tasks/label_projection/methods/__init__.py index a5b099a5ae..7899498aea 100644 --- a/openproblems/tasks/label_projection/methods/__init__.py +++ b/openproblems/tasks/label_projection/methods/__init__.py @@ -10,5 +10,8 @@ from .scvi_tools import scanvi_hvg from .scvi_tools import scarches_scanvi_all_genes from .scvi_tools import scarches_scanvi_hvg +from .scvi_tools import scarches_scanvi_xgb_all_genes +from .scvi_tools import scarches_scanvi_xgb_hvg +from .seurat import seurat from .xgboost import xgboost_log_cpm from .xgboost import xgboost_scran diff --git a/openproblems/tasks/label_projection/methods/scvi_tools.py b/openproblems/tasks/label_projection/methods/scvi_tools.py index 05cd51825d..869dd85c78 100644 --- a/openproblems/tasks/label_projection/methods/scvi_tools.py +++ b/openproblems/tasks/label_projection/methods/scvi_tools.py @@ -1,5 +1,6 @@ from ....tools.decorators import method from ....tools.utils import check_version +from typing import Optional import functools @@ -16,7 +17,7 @@ _scanvi_scarches_method = functools.partial( method, paper_name="Query to reference single-cell integration with transfer learning", - paper_url="https://www.biorxiv.org/content/10.1101/2020.07.16.205997v1", + paper_url="https://doi.org/10.1101/2020.07.16.205997", paper_year=2021, code_url="https://github.com/YosefLab/scvi-tools", image="openproblems-python-scvi", @@ -86,7 +87,14 @@ def _scanvi(adata, test=False, n_hidden=None, n_latent=None, n_layers=None): return preds -def _scanvi_scarches(adata, test=False, n_hidden=None, n_latent=None, n_layers=None): +def _scanvi_scarches( + adata, + test=False, + n_hidden=None, + n_latent=None, + n_layers=None, + prediction_method="scanvi", +): import scvi if test: @@ -138,6 +146,15 @@ def _scanvi_scarches(adata, test=False, n_hidden=None, n_latent=None, n_layers=N train_kwargs["limit_val_batches"] = 10 query_model.train(plan_kwargs=dict(weight_decay=0.0), **train_kwargs) + if prediction_method == "scanvi": + preds = _pred_scanvi(adata, query_model) + elif prediction_method == "xgboost": + preds = _pred_xgb(adata, adata_train, adata_test, query_model, test=test) + + return preds + + +def _pred_scanvi(adata, query_model): # this is temporary and won't be used adata.obs["scanvi_labels"] = "Unknown" preds = query_model.predict(adata) @@ -146,6 +163,63 @@ def _scanvi_scarches(adata, test=False, n_hidden=None, n_latent=None, n_layers=N return preds +# note: could extend test option +def _pred_xgb( + adata, + adata_train, + adata_test, + query_model, + label_col="labels", + test=False, + num_round: Optional[int] = None, +): + import numpy as np + import xgboost as xgb + + df = _classif_df(adata_train, query_model, label_col) + + df["labels_int"] = df["labels"].cat.codes + categories = df["labels"].cat.categories + + # X_train = df.drop(columns="labels") + X_train = df.drop(columns=["labels", "labels_int"]) + # y_train = df["labels"].astype("category") + y_train = df["labels_int"].astype(int) + + X_test = query_model.get_latent_representation(adata_test) + + if test: + num_round = num_round or 2 + else: + num_round = num_round or 5 + + xgbc = xgb.XGBClassifier(tree_method="hist", objective="multi:softprob") + + xgbc.fit(X_train, y_train) + + # adata_test.obs["preds_test"] = xgbc.predict(X_test) + adata_test.obs["preds_test"] = categories[xgbc.predict(X_test)] + + preds = [ + adata_test.obs["preds_test"][idx] if idx in adata_test.obs_names else np.nan + for idx in adata.obs_names + ] + + return preds + + +def _classif_df(adata, trained_model, label_col): + import pandas as pd + + emb_data = trained_model.get_latent_representation(adata) + + df = pd.DataFrame(data=emb_data, index=adata.obs_names) + + df["labels"] = adata.obs[label_col] + + return df + + @_scanvi_method(method_name="scANVI (All genes)") def scanvi_all_genes(adata, test=False): adata.obs["labels_pred"] = _scanvi(adata, test=test) @@ -176,3 +250,25 @@ def scarches_scanvi_hvg(adata, test=False): adata.obs["labels_pred"] = _scanvi_scarches(bdata, test=test) adata.uns["method_code_version"] = check_version("scvi-tools") return adata + + +@_scanvi_scarches_method(method_name="scArches+scANVI+xgboost (All genes)") +def scarches_scanvi_xgb_all_genes(adata, test=False): + adata.obs["labels_pred"] = _scanvi_scarches( + adata, test=test, prediction_method="xgboost" + ) + + adata.uns["method_code_version"] = check_version("scvi-tools") + return adata + + +@_scanvi_scarches_method(method_name="scArches+scANVI+xgboost (Seurat v3 2000 HVG)") +def scarches_scanvi_xgb_hvg(adata, test=False): + hvg_df = _hvg(adata, test) + bdata = adata[:, hvg_df.highly_variable].copy() + adata.obs["labels_pred"] = _scanvi_scarches( + bdata, test=test, prediction_method="xgboost" + ) + + adata.uns["method_code_version"] = check_version("scvi-tools") + return adata diff --git a/openproblems/tasks/label_projection/methods/seurat.R b/openproblems/tasks/label_projection/methods/seurat.R new file mode 100644 index 0000000000..b7a3c7ebc8 --- /dev/null +++ b/openproblems/tasks/label_projection/methods/seurat.R @@ -0,0 +1,70 @@ +#' seurat.R +#' Runs Seurat TransferData with SCT +#' +#' @param sce SingleCellExperiment +#' @param n_pcs int Number of PCA components +#' @param k_filter int How many neighbors (k) to use when filtering anchors. +#' Set to NA to turn off filtering. +#' @param k_score int How many neighbors (k) to use when scoring anchors + +options(error = rlang::entrace) + +# Dependencies +library(SingleCellExperiment) +library(Seurat) +library(Matrix) +library(future) + +# 8GB max size up from default of 500MB +options(future.globals.maxSize = 8 * 1024^3) +plan(multicore, workers = availableCores()) + +args <- readRDS("/tmp/openproblems_seurat_args.rds") +sce <- args$sce +n_pcs <- args$n_pcs +k_filter <- args$k_filter +k_score <- args$k_score + +# Convert RsparseMatrix +if (is(assay(sce, "X"), "RsparseMatrix")) { + assay(sce, "X") <- as(assay(sce, "X"), "CsparseMatrix") # nolint: object_name_linter +} + +# Method body + +data <- as.Seurat(sce, counts = "X", data = NULL) +rm("sce") +gc() +data <- SCTransform(data, assay = "originalexp", verbose = FALSE) + +reference <- data[, data$is_train] +query <- data[, !data$is_train] +rm("data") +gc() + +reference <- RunPCA(reference, assay = "SCT", verbose = FALSE, n_pcs = n_pcs) +anchors <- FindTransferAnchors( + reference = reference, + query = query, + normalization.method = "SCT", + dims = 1:n_pcs, + k.score = k_score, + k.filter = k_filter, + verbose = FALSE +) +query <- TransferData( + anchorset = anchors, + reference = reference, + query = query, + k.weight = k_score, + refdata = list( + labels = "labels" + ), + verbose = FALSE +) +query$labels_pred <- query$predicted.labels +reference$labels_pred <- reference$labels +sce <- as.SingleCellExperiment(merge(reference, query)) + +# Return +saveRDS(sce, "/tmp/openproblems_seurat_sce_out.rds") diff --git a/openproblems/tasks/label_projection/methods/seurat.py b/openproblems/tasks/label_projection/methods/seurat.py new file mode 100644 index 0000000000..874f2c9b54 --- /dev/null +++ b/openproblems/tasks/label_projection/methods/seurat.py @@ -0,0 +1,45 @@ +from ....tools.conversion import r_function +from ....tools.decorators import method +from ....tools.utils import check_r_version +from typing import Optional + +import pathlib + +_seurat = r_function( + "seurat_wrapper.R", args="sce, n_pcs, k_score, k_filter, script_path" +) + + +@method( + method_name="Seurat reference mapping (SCTransform)", + paper_name="Integrated analysis of multimodal single-cell data", + paper_url="https://doi.org/10.1016/j.cell.2021.04.048", + paper_year=2021, + code_url="https://github.com/satijalab/seurat", + image="openproblems-r-extras", +) +def seurat( + adata, + n_pcs: Optional[int] = None, + k_score: Optional[int] = None, + k_filter: Optional[int] = None, + test: bool = False, +): + kwargs = {} + if test: + kwargs["n_pcs"] = n_pcs or 5 + kwargs["k_score"] = k_score or 5 + kwargs["k_filter"] = k_filter or 20 + else: # pragma: nocover + kwargs["n_pcs"] = n_pcs or 50 + kwargs["k_score"] = k_score or 30 + kwargs["k_filter"] = k_filter or 200 + adata = _seurat( + adata, + script_path=pathlib.Path(__file__).parent.joinpath("seurat.R").as_posix(), + **kwargs, + ) + # R conversion scrambles booleans + adata.obs["is_train"] = adata.obs["is_train"].astype(bool) + adata.uns["method_code_version"] = check_r_version("Seurat") + return adata diff --git a/openproblems/tasks/label_projection/methods/seurat_wrapper.R b/openproblems/tasks/label_projection/methods/seurat_wrapper.R new file mode 100644 index 0000000000..9755669c46 --- /dev/null +++ b/openproblems/tasks/label_projection/methods/seurat_wrapper.R @@ -0,0 +1,26 @@ +#' Seuratv3 TransferData wrapper +#' +#' rpy2 + seurat causes R to hang indefinitely. +#' This hack forks R to a separate process. +#' +#' @param sce SingleCellExperiment +#' @param n_pcs int Number of PCA components +#' @param k_filter int How many neighbors (k) to use when filtering anchors. +#' Set to NA to turn off filtering. +#' @param k_score int How many neighbors (k) to use when scoring anchors +#' @param script_path character Path to seuratv3.R + +saveRDS( + list(sce = sce, n_pcs = n_pcs, k_filter = k_filter, k_score = k_score), + "/tmp/openproblems_seurat_args.rds" +) +# clear memory +rm("sce") + +exitcode <- system2("Rscript", script_path) +if (exitcode != 0) { + stop(paste0("Rscript failed with error code ", as.character(exitcode))) +} + +sce_sp <- readRDS("/tmp/openproblems_seurat_sce_out.rds") +sce_sp diff --git a/openproblems/tasks/label_projection/metrics/accuracy.py b/openproblems/tasks/label_projection/metrics/accuracy.py index 865067b059..5e661fa097 100644 --- a/openproblems/tasks/label_projection/metrics/accuracy.py +++ b/openproblems/tasks/label_projection/metrics/accuracy.py @@ -9,7 +9,7 @@ def accuracy(adata): encoder = sklearn.preprocessing.LabelEncoder().fit(adata.obs["labels"]) test_data = adata[~adata.obs["is_train"]] - test_data.obs["labels"] = encoder.transform(test_data.obs["labels"]) - test_data.obs["labels_pred"] = encoder.transform(test_data.obs["labels_pred"]) + labels = encoder.transform(test_data.obs["labels"]) + labels_pred = encoder.transform(test_data.obs["labels_pred"]) - return np.mean(test_data.obs["labels"] == test_data.obs["labels_pred"]) + return np.mean(labels == labels_pred) diff --git a/openproblems/tasks/multimodal_data_integration/README.md b/openproblems/tasks/multimodal_data_integration/README.md index 8905f2901e..88884cdc74 100644 --- a/openproblems/tasks/multimodal_data_integration/README.md +++ b/openproblems/tasks/multimodal_data_integration/README.md @@ -1,21 +1,48 @@ -## The task - -Several recently described technologies allow for simultaneous measurement of different aspects of cell state. For example, [sci-CAR](https://doi.org/10.1126/science.aau0730) jointly profiles RNA expression and chromatin accessibility on the same cell and [CITE-seq](https://doi.org/10.1038/nmeth.4380) measures surface protein abundance and RNA expression from each cell. However, these joint profiling methods have several tradeoffs compared to unimodal measurements. +# Multimodal data integration -Joint methods can be more expensive or lower throughput or more noisy than measuring a single modality at a time. Therefore it is useful to develop methods that are capable of integrating measurements of the same biological system but obtained using different technologies. +## The task -Here the goal is to learn a latent space where observations from the same cell acquired using different modalities. A perfect result has each of the paired observations sharing the same coordinates in the latent space. +Cellular function is regulated by the complex interplay of different types of biological +molecules (DNA, RNA, proteins, etc.), which determine the state of a cell. Several +recently described technologies allow for simultaneous measurement of different aspects +of cellular state. For example, [sci-CAR](https://doi.org/10.1126/science.aau0730) +jointly profiles RNA expression and chromatin accessibility on the same cell and +[CITE-seq](https://doi.org/10.1038/nmeth.4380) measures surface protein abundance and +RNA expression from each cell. These technologies enable us to better understand +cellular function, however datasets are still rare and there are tradeoffs that these +measurements make for to profile multiple modalities. + +Joint methods can be more expensive or lower throughput or more noisy than measuring a +single modality at a time. Therefore it is useful to develop methods that are capable +of integrating measurements of the same biological system but obtained using different +technologies on different cells. + +Here the goal is to learn a latent space where cells profiled by different technologies in +different modalities are matched if they have the same state. We use jointly profiled +data as ground truth so that we can evaluate when the observations from the same cell +acquired using different modalities are similar. A perfect result has each of the paired +observations sharing the same coordinates in the latent space. ## The metrics -Metrics for multimodal data integration aim to characterize how well the aligned datasets correspond to the ground truth. -* **kNN AUC**: Let $f(i) ∈ F$ be the scRNA-seq measurement of cell $i$, and $g(i) ∈ G$ be the scATAC- seq measurement of cell $i$. kNN-AUC calculates the average percentage overlap of neighborhoods of $f(i)$ in $F$ with neighborhoods of $g(i)$ in $G$. Higher is better. -* **MSE**: Mean squared error (MSE) is the average distance between each pair of matched observations of the same cell in the learned latent space. Lower is better. +Metrics for multimodal data integration aim to characterize how well the aligned +datasets correspond to the ground truth. + +* **kNN AUC**: Let $f(i) ∈ F$ be the scRNA-seq measurement of cell $i$, and $g(i) ∈ G$ + be the scATAC- seq measurement of cell $i$. kNN-AUC calculates the average percentage + overlap of neighborhoods of $f(i)$ in $F$ with neighborhoods of $g(i)$ in $G$. Higher + is better. +* **MSE**: Mean squared error (MSE) is the average distance between each pair of matched + observations of the same cell in the learned latent space. Lower is better. ## API -Datasets should include matched measurements from two modalities, which are contained in `adata` and `adata.obsm["mode2"]`. The task is to align these two modalities as closely as possible, without using the known bijection between the datasets. +Datasets should include matched measurements from two modalities, which are contained in +`adata` and `adata.obsm["mode2"]`. The task is to align these two modalities as closely +as possible, without using the known bijection between the datasets. -Methods should create joint matrices `adata.obsm["aligned"]` and `adata.obsm["mode2_aligned"]` which reside in a joint space. +Methods should create joint matrices `adata.obsm["aligned"]` and +`adata.obsm["mode2_aligned"]` which reside in a joint space. -Metrics should evaluate how well the cells which are known to be equivalent are aligned in the joint space. +Metrics should evaluate how well the cells which are known to be equivalent are aligned +in the joint space. diff --git a/openproblems/tasks/multimodal_data_integration/__init__.py b/openproblems/tasks/multimodal_data_integration/__init__.py index 1df46258f2..a066ae939d 100644 --- a/openproblems/tasks/multimodal_data_integration/__init__.py +++ b/openproblems/tasks/multimodal_data_integration/__init__.py @@ -5,7 +5,9 @@ from . import metrics _task_name = "Multimodal Data Integration" -_task_summary = "TODO" +_task_summary = "Alignment of cellular profiles from two different modalities" + +DEFAULT_LAYER = "counts" DATASETS = utils.get_callable_members(datasets) METHODS = utils.get_callable_members(methods) diff --git a/openproblems/tasks/multimodal_data_integration/datasets/citeseq.py b/openproblems/tasks/multimodal_data_integration/datasets/citeseq.py index 188fc706c7..338423ef9e 100644 --- a/openproblems/tasks/multimodal_data_integration/datasets/citeseq.py +++ b/openproblems/tasks/multimodal_data_integration/datasets/citeseq.py @@ -5,7 +5,10 @@ @dataset( "CITE-seq Cord Blood Mononuclear Cells", data_url=load_citeseq_cbmc.metadata["data_url"], - dataset_summary="TODO", + data_reference=load_citeseq_cbmc.metadata["data_reference"], + dataset_summary="8k cord blood mononuclear cells sequenced by CITEseq, a multimodal" + " addition to the 10x scRNA-seq platform that allows simultaneous measurement of " + "RNA and protein.", ) def citeseq_cbmc(test=False): return load_citeseq_cbmc(test=test) diff --git a/openproblems/tasks/multimodal_data_integration/datasets/scicar.py b/openproblems/tasks/multimodal_data_integration/datasets/scicar.py index 14cae302e9..c3891fa12b 100644 --- a/openproblems/tasks/multimodal_data_integration/datasets/scicar.py +++ b/openproblems/tasks/multimodal_data_integration/datasets/scicar.py @@ -6,7 +6,10 @@ @dataset( "sciCAR Cell Lines", data_url=load_scicar_cell_lines.metadata["data_url"], - dataset_summary="TODO", + data_reference=load_scicar_cell_lines.metadata["data_reference"], + dataset_summary="5k cells from a time-series of dexamethasone treatment sequenced " + "by sci-CAR, a combinatorial indexing-based co-assay that jointly profiles " + "chromatin accessibility and mRNA.", ) def scicar_cell_lines(test=False): return load_scicar_cell_lines(test=test) @@ -15,7 +18,10 @@ def scicar_cell_lines(test=False): @dataset( "sciCAR Mouse Kidney", data_url=load_scicar_mouse_kidney.metadata["data_url"], - dataset_summary="TODO", + data_reference=load_scicar_cell_lines.metadata["data_reference"], + dataset_summary="11k cells from adult mouse kidney sequenced " + "by sci-CAR, a combinatorial indexing-based co-assay that jointly profiles " + "chromatin accessibility and mRNA.", ) def scicar_mouse_kidney(test=False): return load_scicar_mouse_kidney(test=test) diff --git a/openproblems/tasks/multimodal_data_integration/methods/harmonic_alignment.py b/openproblems/tasks/multimodal_data_integration/methods/harmonic_alignment.py index 7f20e05847..e17db7332e 100644 --- a/openproblems/tasks/multimodal_data_integration/methods/harmonic_alignment.py +++ b/openproblems/tasks/multimodal_data_integration/methods/harmonic_alignment.py @@ -10,7 +10,7 @@ _harmonic_alignment_method = functools.partial( method, paper_name="Harmonic Alignment", - paper_url="https://epubs.siam.org/doi/abs/10.1137/1.9781611976236.36", + paper_url="https://doi.org/10.1137/1.9781611976236.36", paper_year=2020, code_url="https://github.com/KrishnaswamyLab/harmonic-alignment", ) diff --git a/openproblems/tasks/multimodal_data_integration/methods/mnn.R b/openproblems/tasks/multimodal_data_integration/methods/mnn.R index c7b2c429a0..d8e65497e0 100644 --- a/openproblems/tasks/multimodal_data_integration/methods/mnn.R +++ b/openproblems/tasks/multimodal_data_integration/methods/mnn.R @@ -12,7 +12,7 @@ library(batchelor) n_svd <- 100 # Convert data to friendly sparse format -assay(sce, "X") <- as(assay(sce, "X"), "CsparseMatrix") +assay(sce, "X") <- as(assay(sce, "X"), "CsparseMatrix") # nolint: object_name_linter reducedDim(sce, "mode2") <- as(reducedDim(sce, "mode2"), "CsparseMatrix") # Check parameters diff --git a/openproblems/tasks/regulatory_effect_prediction/README.md b/openproblems/tasks/regulatory_effect_prediction/README.md index a7bb6a6929..1bd06f809c 100644 --- a/openproblems/tasks/regulatory_effect_prediction/README.md +++ b/openproblems/tasks/regulatory_effect_prediction/README.md @@ -1,6 +1,10 @@ # Chromatin accessibility prediction -Chromatin accessibility prediction refers to the gene expression prediction of a cell or cell type from ATAC-seq peaks. For a summary or all relevant models, see gene score method in [Jeffrey M. Granja et al.](https://www.biorxiv.org/content/10.1101/2020.04.28.066498v1), [Su Wang et al.](https://pubmed.ncbi.nlm.nih.gov/24263090/) et al. +Chromatin accessibility prediction refers to the gene expression prediction of a cell or +cell type from ATAC-seq peaks. For a summary or all relevant models, see gene score +method in [Jeffrey M. Granja et +al.](https://www.biorxiv.org/content/10.1101/2020.04.28.066498v1), [Su Wang et +al.](https://pubmed.ncbi.nlm.nih.gov/24263090/) et al. ## API @@ -12,8 +16,11 @@ Datasets should contain the following attributes: * `adata.uns['mode2_var_start']` (single cell atac-seq peak start position) * `adata.uns['mode2_var_end']` (single cell atac-seq peak end position) * `adata.obsm['mode2']` (cell by peak matrix of single cell atac-seq) -* `adata.X` (cell by gene matrix of single cell gene expression, which is the ground truth) +* `adata.X` (cell by gene matrix of single cell gene expression, which is the ground + truth) -Methods should assign gene regulation scores to `adata.obsm['gene_score']` using only single cell atac-seq peak counts. +Methods should assign gene regulation scores to `adata.obsm['gene_score']` using only +single cell atac-seq peak counts. -Metrics should compare `adata.obsm['gene_score']` with the true gene expression in `adata.X`. +Metrics should compare `adata.obsm['gene_score']` with the true gene expression in +`adata.X`. diff --git a/openproblems/tasks/regulatory_effect_prediction/__init__.py b/openproblems/tasks/regulatory_effect_prediction/__init__.py index 4465a4a55d..0a3b1a97bb 100644 --- a/openproblems/tasks/regulatory_effect_prediction/__init__.py +++ b/openproblems/tasks/regulatory_effect_prediction/__init__.py @@ -5,7 +5,9 @@ from . import metrics _task_name = "Regulatory effect prediction" -_task_summary = "TODO" +_task_summary = "Prediction of gene expression from chromatin accessibility" + +DEFAULT_LAYER = "counts" DATASETS = utils.get_callable_members(datasets) METHODS = utils.get_callable_members(methods) diff --git a/openproblems/tasks/regulatory_effect_prediction/datasets/scicar.py b/openproblems/tasks/regulatory_effect_prediction/datasets/scicar.py index 56e7c13ed4..e5dff69ea6 100644 --- a/openproblems/tasks/regulatory_effect_prediction/datasets/scicar.py +++ b/openproblems/tasks/regulatory_effect_prediction/datasets/scicar.py @@ -5,7 +5,10 @@ @dataset( "sciCAR Mouse Kidney with cell clusters", data_url=scicar.load_scicar_mouse_kidney.metadata["data_url"], - dataset_summary="TODO", + data_reference=scicar.load_scicar_mouse_kidney.metadata["data_reference"], + dataset_summary="11k cells from adult mouse kidney sequenced " + "by sci-CAR, a combinatorial indexing-based co-assay that jointly profiles " + "chromatin accessibility and mRNA.", ) def scicar_mouse_kidney(test=False): adata = scicar.load_scicar_mouse_kidney(test=test) diff --git a/openproblems/tasks/spatial_decomposition/README.md b/openproblems/tasks/spatial_decomposition/README.md new file mode 100644 index 0000000000..79b3db1d8e --- /dev/null +++ b/openproblems/tasks/spatial_decomposition/README.md @@ -0,0 +1,54 @@ +# Spatial Decomposition/Deconvolution + +## The task + +Spatial decomposition (also often referred to as Spatial deconvolution) is +applicable to spatial transcriptomics data where the transcription profile of +each capture location (spot, voxel, bead, etc.) do not share a bijective +relationship with the cells in the tissue, i.e., multiple cells may contribute +to the same capture location. The task of spatial decomposition then refers to +estimating the composition of cell types/states that are present at each capture +location. The cell type/states estimates are presented as proportion values, +representing the proportion of the cells at each capture location that belong to +a given cell type. + +We distinguish between _reference-based_ decomposition and _de novo_ +decomposition, where the former leverage external data (e.g., scRNA-seq or +scNuc-seq) to guide the inference process, while the latter only work with the +spatial data. We require that all datasets have an associated reference single +cell data set, but methods are free to ignore this information. + +## Metrics + +### R2 + +R2 pronounced as "R squared", also known as the "coefficient of determination". R2 +reports the fraction of the true proportion values' (`adata.obsm["proportions_true"]`) +variance that can be explained by the predicted proportion values +(`adata.obsm["proportion_pred"]`). The **best score**, and upper bound, is 1.0. There is +no fixed lower bound for the metric. The _uniform/non-weighted average_ across all cell +types/states is used to summarize performance. See the +[sklearn](https://scikit-learn.org/stable/modules/generated/sklearn.metrics.r2_score.html) +documentation for details on the implementation and the +[wikipedia](https://en.wikipedia.org/wiki/Coefficient_of_determination) site for more +general information regarding the metric. + +## API + +Datasets consists of 2 `anndata.AnnData` objects, concatenated by key +`adata.obs["modality"]` with values: + +* `sc` for the single cell reference. +* `sp` for the target spatial dataset. + +In the single cell reference, cell-types are stored in `adata_sc.obs["label"]`. No label +may have fewer than 25 cells. + +In the spatial target, ground-truth cell-type proportions are stored in +`adata_spatial.obsm["proportions_true"]`. + +Methods should return only the spatial data with inferred proportions stored in +`adata_spatial.obsm["proportions_pred"]`. Proportions must sum to one. + +Metrics shall compare `adata_spatial.obsm['proportions_pred']` to +`adata_spatial.obsm['proportions_true']`. diff --git a/openproblems/tasks/spatial_decomposition/__init__.py b/openproblems/tasks/spatial_decomposition/__init__.py new file mode 100644 index 0000000000..54718c7fe3 --- /dev/null +++ b/openproblems/tasks/spatial_decomposition/__init__.py @@ -0,0 +1,16 @@ +from ...utils import get_callable_members +from . import api +from . import datasets +from . import methods +from . import metrics + +_task_name = "Spatial Decomposition" +_task_summary = ( + "Calling cell-type compositions for spot-based spatial transcriptomics data" +) + +DEFAULT_LAYER = "counts" + +DATASETS = get_callable_members(datasets) +METHODS = get_callable_members(methods) +METRICS = get_callable_members(metrics) diff --git a/openproblems/tasks/spatial_decomposition/api.py b/openproblems/tasks/spatial_decomposition/api.py new file mode 100644 index 0000000000..3fa833ac03 --- /dev/null +++ b/openproblems/tasks/spatial_decomposition/api.py @@ -0,0 +1,82 @@ +from ...data.sample import load_sample_data +from ...tools.decorators import dataset +from .utils import merge_sc_and_sp +from .utils import split_sc_and_sp +from anndata import AnnData +from pandas.core.dtypes.common import is_categorical_dtype + +import numpy as np + +CELLTYPE_MIN_CELLS = 25 + + +def check_dataset(adata: AnnData): + """Check that dataset output fits expected API.""" + # test for spatial coordinates + assert "spatial" in adata.obsm + assert isinstance(adata.obsm["spatial"], np.ndarray) + # check that proportions are included + assert "proportions_true" in adata.obsm + assert isinstance(adata.obsm["proportions_true"], np.ndarray) + assert "label" in adata.obs + assert is_categorical_dtype(adata.obs["label"]) + assert "modality" in adata.obs + assert np.all(np.isin(adata.obs["modality"], ["sc", "sp"])) + adata_sc, adata_sp = split_sc_and_sp(adata) + # make sure proportions sum to one, some precision error allowed + proportions_sum = np.sum(adata_sp.obsm["proportions_true"], axis=1) + np.testing.assert_allclose(proportions_sum, 1) + # make sure no celltype labels are too rare + celltype_counts = adata_sc.obs["label"].value_counts() + assert np.all(celltype_counts >= CELLTYPE_MIN_CELLS) + return True + + +def check_method(adata: AnnData): + """Check that method output fits expected API.""" + assert np.all(adata.obs["modality"] == "sp") + assert "proportions_pred" in adata.obsm + assert isinstance(adata.obsm["proportions_pred"], np.ndarray) + assert "proportions_true" in adata.obsm + assert isinstance(adata.obsm["proportions_true"], np.ndarray) + assert np.all(np.isfinite(adata.obsm["proportions_true"])) + assert adata.obsm["proportions_pred"].shape == adata.obsm["proportions_true"].shape + proportions_sum = np.sum(adata.obsm["proportions_true"], axis=1) + np.testing.assert_allclose(proportions_sum, 1) + return True + + +@dataset() +def sample_dataset(): + """Create a simple dataset to use for testing methods in this task.""" + # set number of cell types + n_types = 3 + # load sample anndata + adata_spatial = load_sample_data() + # modify index + adata_spatial.obs.index = "spatial_" + adata_spatial.obs.index + # set spatial coordinates + adata_spatial.obsm["spatial"] = np.random.random((adata_spatial.shape[0], 2)) + # generate proportion values + props = np.random.dirichlet(alpha=np.ones(n_types), size=adata_spatial.shape[0]) + adata_spatial.obsm["proportions_true"] = props + # get anndata for single cell reference + adata_sc = load_sample_data() + # set labels for single cell data + adata_sc.obs["label"] = np.random.randint( + 0, n_types, size=adata_sc.shape[0] + ).astype(str) + # bind single cell reference to spatial anndata + adata_merged = merge_sc_and_sp(adata_sc, adata_spatial) + return adata_merged + + +def sample_method(adata): + """Create sample method output for testing metrics in this task.""" + _, adata = split_sc_and_sp(adata) + # get number of cell types + n_types = adata.obsm["proportions_true"].shape[1] + # generate predicted proportions + props = np.random.dirichlet(alpha=np.ones(n_types), size=adata.shape[0]) + adata.obsm["proportions_pred"] = props + return adata diff --git a/openproblems/tasks/spatial_decomposition/datasets/__init__.py b/openproblems/tasks/spatial_decomposition/datasets/__init__.py new file mode 100644 index 0000000000..7b405366a9 --- /dev/null +++ b/openproblems/tasks/spatial_decomposition/datasets/__init__.py @@ -0,0 +1,4 @@ +from .destvi.generate import destvi +from .pancreas import pancreas_alpha_0_5 +from .pancreas import pancreas_alpha_1 +from .pancreas import pancreas_alpha_5 diff --git a/openproblems/tasks/spatial_decomposition/datasets/destvi/__init__.py b/openproblems/tasks/spatial_decomposition/datasets/destvi/__init__.py new file mode 100644 index 0000000000..e69de29bb2 diff --git a/openproblems/tasks/spatial_decomposition/datasets/destvi/generate.py b/openproblems/tasks/spatial_decomposition/datasets/destvi/generate.py new file mode 100644 index 0000000000..84a408d876 --- /dev/null +++ b/openproblems/tasks/spatial_decomposition/datasets/destvi/generate.py @@ -0,0 +1,21 @@ +from openproblems.data.utils import filter_genes_cells +from openproblems.tools.decorators import dataset + + +@dataset( + "DestVI", + data_url="https://github.com/romain-lopez/DestVI-reproducibility/" + "blob/master/simulations/make_dataset.py", + data_reference="https://doi.org/10.1038/s41587-022-01272-8", + dataset_summary="scRNA-seq is generated based on learn NB parameters" + "from the destVI manuscripts leveraging sparsePCA. Number of cells and" + "cell types present in each spatial spot is computed via combination of" + "kernel-based parametrization of a categorical distribution and the NB model.", + image="openproblems-python-extras", +) +def destvi(test=False): + from .utils import generate_synthetic_dataset + + merged_adata = generate_synthetic_dataset(test=test) + filter_genes_cells(merged_adata) + return merged_adata diff --git a/openproblems/tasks/spatial_decomposition/datasets/destvi/ground_truth_pca.npz b/openproblems/tasks/spatial_decomposition/datasets/destvi/ground_truth_pca.npz new file mode 100644 index 0000000000..e27d832053 Binary files /dev/null and b/openproblems/tasks/spatial_decomposition/datasets/destvi/ground_truth_pca.npz differ diff --git a/openproblems/tasks/spatial_decomposition/datasets/destvi/inverse_dispersion.npy b/openproblems/tasks/spatial_decomposition/datasets/destvi/inverse_dispersion.npy new file mode 100644 index 0000000000..d5cc38417d Binary files /dev/null and b/openproblems/tasks/spatial_decomposition/datasets/destvi/inverse_dispersion.npy differ diff --git a/openproblems/tasks/spatial_decomposition/datasets/destvi/utils.py b/openproblems/tasks/spatial_decomposition/datasets/destvi/utils.py new file mode 100644 index 0000000000..89a1d36f2b --- /dev/null +++ b/openproblems/tasks/spatial_decomposition/datasets/destvi/utils.py @@ -0,0 +1,264 @@ +""" +From: +https://github.com/romain-lopez/DestVI-reproducibility/blob/master/simulations/ +""" +from ...utils import merge_sc_and_sp +from numba import jit +from openproblems.data.utils import loader +from pathlib import Path +from scipy.sparse import csr_matrix +from scipy.spatial.distance import pdist +from scipy.spatial.distance import squareform +from sklearn.cluster import AgglomerativeClustering +from sklearn.decomposition import PCA +from sklearn.neighbors import kneighbors_graph +from typing import Optional + +import anndata +import numpy as np +import pandas as pd +import scanpy as sc + + +def categorical(p, n_samples): + size = list(p.shape[:-1]) + size.insert(0, n_samples) + return (p.cumsum(-1) >= np.random.uniform(size=size)[..., None]).argmax(-1).T + + +@jit(nopython=True) +def get_mean_normal(cell_types, gamma, mean_, components_): # pragma: no cover + """Util for preparing the mean of the normal distribution. + + cell_types: (n_spots, n_cells) + gamma: (n_spots, n_cells, n_latent) + + return: samples: (n_spots, n_cells, n_genes) + """ + # extract shapes + n_spots = gamma.shape[0] + n_cells = gamma.shape[1] + n_genes = components_[0].shape[1] + + mean_normal = np.zeros((n_spots, n_cells, n_genes)) + for spot in range(n_spots): + for cell in range(n_cells): + mean_normal[spot, cell] = mean_[cell_types[spot, cell]] + c = components_[cell_types[spot, cell]] + g = np.expand_dims(gamma[spot, cell], 0) + mean_normal[spot, cell] += np.dot(g, c)[0] + return mean_normal + + +@loader( + data_url="https://github.com/romain-lopez/DestVI-reproducibility/", + data_reference="https://doi.org/10.1038/s41587-022-01272-8", +) +def generate_synthetic_dataset( + test: bool, + lam_ct: float = 0.1, + temp_ct: float = 1.0, + lam_gam: float = 0.5, + sf_gam: float = 15.0, + bin_sampling: float = 1.0, + ct_study: int = 0, + grid_size: Optional[int] = None, # size of spatial grid + K_sampled: Optional[int] = None, # cells sampled for each spot + seed: int = 0, +): + import torch + + np.random.seed(seed) + torch.manual_seed(seed) + + # parameters + K = 100 + if test: + K_sampled = K_sampled or 3 + grid_size = grid_size or 5 + n_cells = 200 + knn_cells = 3 + else: # pragma: nocover + K_sampled = K_sampled or 20 + grid_size = grid_size or 10 + n_cells = None + knn_cells = 30 + + script_dir = Path(__file__).resolve().parent + + grtruth_PCA = np.load(script_dir.joinpath("ground_truth_pca.npz").as_posix()) + mean_, components_ = grtruth_PCA["mean_"], grtruth_PCA["components_"] + + inv_dispersion = np.load(script_dir.joinpath("inverse_dispersion.npy").as_posix()) + + C = components_.shape[0] + D = components_.shape[1] + + locations, freq_sample, gamma = generate_spatial_information( + C=C, + D=D, + grid_size=grid_size, + lam_ct=lam_ct, + temp_ct=temp_ct, + lam_gam=lam_gam, + sf_gam=sf_gam, + ) + + cell_types_sc = categorical(freq_sample, K) + gamma_sc = gamma[:, None, :].repeat(K, axis=1) + location_sc = locations[:, None, :].repeat(K, axis=1) + # get means of the Gaussian using the sPCA model + mean_normal = get_mean_normal(cell_types_sc, gamma_sc, mean_, components_) + # convert back to count distribution and sample from Poisson + mean_normal[mean_normal <= 0] = np.min(mean_normal[mean_normal > 0]) * 0.01 + transformed_mean = np.expm1(mean_normal) + + # dispersion was learned on the single-cell data. + # this simulation might have different library sizes + # we must match them so that the dispersion estimates make sense + # (i.e., genes are as overpoissonian as in the experiments) + inv_dispersion *= 1e2 + + # Important remark: Gamma is parametrized by the rate = 1/scale! + gamma_s = torch.distributions.Gamma( + concentration=torch.tensor(inv_dispersion), + rate=torch.tensor(inv_dispersion) / torch.tensor(transformed_mean), + ).sample() + mean_poisson = torch.clamp(gamma_s, max=1e8).cpu().numpy() + transformed_mean = mean_poisson + + samples = np.random.poisson(lam=transformed_mean) + + X = samples[:, :K_sampled].reshape((-1, samples.shape[-1])) + obs_names = [f"sc_{i}" for i in range(X.shape[0])] + sc_anndata = anndata.AnnData( + X=csr_matrix(X), + obs=dict(obs_names=obs_names), + ) + sc_anndata.obs["cell_type"] = cell_types_sc[:, :K_sampled].reshape(-1, 1) + sc_anndata.obs["label"] = sc_anndata.obs["cell_type"].astype(str).astype("category") + sc_anndata.obs["n_counts"] = np.sum(sc_anndata.X, axis=1) + sc_anndata.obsm["gamma"] = gamma_sc[:, :K_sampled].reshape(-1, gamma.shape[-1]) + sc_anndata.obsm["spatial"] = location_sc[:, :K_sampled].reshape(-1, 2) + if n_cells is not None: + sc.pp.subsample(sc_anndata, n_obs=n_cells, random_state=seed, copy=False) + + # cluster the single-cell data using sklearn + target_list = [2, 4, 8, 16] + key_list = ["cell_type"] + hier_labels_sc = np.zeros((sc_anndata.n_obs, len(target_list))) + for ct in range(C): + slice_ind = np.where(sc_anndata.obs["cell_type"] == ct) + slice_counts = sc_anndata.X[slice_ind] + slice_normalized = slice_counts / slice_counts.sum(1) + slice_embedding = PCA(n_components=10).fit_transform( + np.log(1 + 1e4 * slice_normalized) + ) + knn_graph = kneighbors_graph(slice_embedding, knn_cells, include_self=False) + for i, target in enumerate(target_list): + labels = AgglomerativeClustering( + n_clusters=target, connectivity=knn_graph + ).fit_predict(slice_embedding) + hier_labels_sc[slice_ind, i] = labels + + # aggregate hierarchical labels and append to anndata + for i, target in enumerate(target_list): + base_cell_type = sc_anndata.obs["cell_type"] + sub_cell_type = hier_labels_sc[:, i] + nb_sub_ct = len(np.unique(sub_cell_type)) + all_cell_type = np.array( + [ + base_cell_type[j] * nb_sub_ct + sub_cell_type[j] + for j in range(sc_anndata.n_obs) + ] + ) + key = str(target) + "th_sub-cell_type" + sc_anndata.obs[key] = all_cell_type.astype(np.int) + key_list.append(key) + # dump keys as well + sc_anndata.uns["key_clustering"] = key_list + sc_anndata.uns["target_list"] = [1] + target_list + + transformed_mean_st_full = transformed_mean.mean(1) + # here we should create a mask to remove contributions from one cell type + transformed_mean_st_partial = np.sum( + (cell_types_sc != C - 1)[:, :, np.newaxis] * transformed_mean, axis=1 + ) + transformed_mean_st_partial /= np.sum(cell_types_sc != C - 1, 1)[:, np.newaxis] + + if ct_study == 1: + list_transformed = [transformed_mean_st_full, transformed_mean_st_partial] + elif ct_study == 0: + list_transformed = [transformed_mean_st_full] + else: + raise NotImplementedError + for i, transformed_mean_st in enumerate(list_transformed): + # Important remark: Gamma is parametrized by the rate = 1/scale! + gamma_st = torch.distributions.Gamma( + concentration=torch.tensor(inv_dispersion), + rate=torch.tensor(inv_dispersion) / torch.tensor(transformed_mean_st), + ).sample() + mean_poisson_st = torch.clamp(gamma_st, max=1e8).cpu().numpy() + mean_st = mean_poisson_st + + samples_st = np.random.poisson(lam=mean_st) + samples_st = np.random.binomial(samples_st, bin_sampling) + obs_names = [f"st_{i}" for i in range(samples_st.shape[0])] + st_anndata = anndata.AnnData( + X=samples_st, + obs=dict(obs_names=obs_names), + ) + if i == 0: + altered_freq = freq_sample + if i > 0: + altered_freq = freq_sample[:, :-1] + altered_freq /= altered_freq.sum(1)[:, np.newaxis] + columns = np.arange(altered_freq.shape[1]).astype(np.str) + freq_df = pd.DataFrame( + altered_freq, index=st_anndata.obs.index, columns=columns + ) + # st_anndata.obsm["cell_type"] = freq_df + st_anndata.obsm["proportions_true"] = freq_df.to_numpy() + st_anndata.obsm["gamma"] = gamma + st_anndata.obsm["spatial"] = locations + st_anndata.obs["n_counts"] = np.sum(st_anndata.X, axis=1) + st_anndata.uns["key_clustering"] = key_list + st_anndata.uns["target_list"] = [1] + target_list + + sc_anndata.layers["counts"] = sc_anndata.X.copy() + st_anndata.layers["counts"] = st_anndata.X.copy() + + merged_anndata = merge_sc_and_sp(sc_anndata, st_anndata, test=test) + + return merged_anndata + + +def generate_spatial_information( + grid_size, + C, + lam_ct, + temp_ct, + lam_gam, + D, + sf_gam, +): + locations = ( + np.mgrid[-grid_size:grid_size:0.5, -grid_size:grid_size:0.5].reshape(2, -1).T + ) + # get the kernel bandwidth for GP simulation + dist_table = pdist(locations) + bandwidth = np.median(dist_table) + # sample from the multivariate GP for cell type + K = np.exp(-squareform(dist_table) ** 2 / (lam_ct * bandwidth**2)) + N = K.shape[0] + sample = np.random.multivariate_normal(np.zeros(N), K, size=C).T + # get through softmax + e_sample = np.exp(sample / temp_ct) + freq_sample = e_sample / np.sum(e_sample, 1)[:, np.newaxis] + + # form the multivariate GP covariance for gamma + K = sf_gam * np.exp(-squareform(dist_table) ** 2 / (lam_gam * bandwidth**2)) + # get latent variable for each cell types + gamma = np.random.multivariate_normal(np.zeros(N), K, size=(D)).T + + return locations, freq_sample, gamma diff --git a/openproblems/tasks/spatial_decomposition/datasets/pancreas.py b/openproblems/tasks/spatial_decomposition/datasets/pancreas.py new file mode 100644 index 0000000000..7c27ebf758 --- /dev/null +++ b/openproblems/tasks/spatial_decomposition/datasets/pancreas.py @@ -0,0 +1,55 @@ +from ....data.pancreas import load_pancreas +from ....data.utils import filter_genes_cells +from ....tools.decorators import dataset +from .utils import generate_synthetic_dataset + +import scanpy as sc + + +@dataset( + "Pancreas (alpha=1)", + data_url=load_pancreas.metadata["data_url"], + data_reference=load_pancreas.metadata["data_reference"], + dataset_summary="Human pancreas cells aggregated from single-cell" + " (Dirichlet alpha=1)", +) +def pancreas_alpha_1(test=False, n_obs=100): + adata = load_pancreas(test=test, integer_only=True) + sc.pp.filter_genes(adata, min_counts=10) + adata.obs["label"] = adata.obs["celltype"] + + merged_adata = generate_synthetic_dataset(adata, n_obs=n_obs, alpha=1, test=test) + filter_genes_cells(merged_adata) + return merged_adata + + +@dataset( + "Pancreas (alpha=5)", + data_url=load_pancreas.metadata["data_url"], + data_reference=load_pancreas.metadata["data_reference"], + dataset_summary="Human pancreas cells aggregated from single-cell" + " (Dirichlet alpha=5)", +) +def pancreas_alpha_5(test=False, n_obs=100): + adata = load_pancreas(test=test, integer_only=True) + adata.obs["label"] = adata.obs["celltype"] + + merged_adata = generate_synthetic_dataset(adata, n_obs=n_obs, alpha=5) + filter_genes_cells(merged_adata) + return merged_adata + + +@dataset( + "Pancreas (alpha=0.5)", + data_url=load_pancreas.metadata["data_url"], + data_reference=load_pancreas.metadata["data_reference"], + dataset_summary="Human pancreas cells aggregated from single-cell" + " (Dirichlet alpha=0.5)", +) +def pancreas_alpha_0_5(test=False, n_obs=100): + adata = load_pancreas(test=test, integer_only=True) + adata.obs["label"] = adata.obs["celltype"] + + merged_adata = generate_synthetic_dataset(adata, n_obs=n_obs, alpha=0.5) + filter_genes_cells(merged_adata) + return merged_adata diff --git a/openproblems/tasks/spatial_decomposition/datasets/utils.py b/openproblems/tasks/spatial_decomposition/datasets/utils.py new file mode 100644 index 0000000000..07f8b604ca --- /dev/null +++ b/openproblems/tasks/spatial_decomposition/datasets/utils.py @@ -0,0 +1,151 @@ +from ..api import CELLTYPE_MIN_CELLS +from ..utils import merge_sc_and_sp +from typing import Sequence +from typing import Union + +import anndata as ad +import numpy as np + + +def generate_synthetic_dataset( + adata: ad.AnnData, + type_column: str = "label", + alpha: Union[float, Sequence] = 1.0, + n_obs: int = 1000, + cell_lb: int = 10, + cell_ub: int = 30, + umi_lb: int = 1000, + umi_ub: int = 5000, + seed: int = 42, + test: bool = False, +) -> ad.AnnData: + """Create cell-aggregate samples for ground-truth spatial decomposition task. + + Parameters + ---------- + adata: AnnData + Anndata object. + type_column: str + name of column in `adata.obs` where cell type labels are gives + alpha: Union[float,Sequence] + alpha value in dirichlet distribution. If single number then all alpha_i values + will be set to this value. Default value is 1. + n_obs: int + number of spatial observations to generate. Default value is 1000. + cell_lb: int + lower bound for number of cells at each spot. Default value is 10. + cell_ub: int + upper bound for number of cells at each spot. Default value is 30. + umi_lb: int + lower bound for number of UMIs at each spot. Default value is 10. + umi_ub: int + upper bound for number of UMIs at each spot. Default value is 30. + seed: int + Seed for rng. + + Returns + ------- + AnnData with: + - `adata_spatial.X`: simulated counts (aggregate of sc dataset). + - `adata_spatial.uns["sc_reference"]`: original sc adata for reference. + - `adata_spatial.obsm["proportions_true"]`: true proportion values. + - `adata_spatial.obsm["n_cells"]`: number of cells from each type at + every location + - `adata_spatial.obs["proportions_true"]`: + total number of cells at each location + + The cell type labels are stored in adata_sc.obs["label"]. + """ + # remove rare celltypes + adata = filter_celltypes(adata) + + # set random generator seed + rng = np.random.default_rng(seed) + + # get single cell expression data + X = adata.X + # get cell annotations/labels + labels = adata.obs[type_column].values + # get unique labels + uni_labs = np.unique(labels) + # count number of labels + n_labs = len(uni_labs) + # get number of genes + n_genes = adata.shape[1] + + # create dict with indices of each label + label_indices = dict() + for label in uni_labs: + label_indices[label] = np.where(labels == label)[0] + + # adjust alpha to vector if single scalar + if not hasattr(alpha, "__len__"): + alpha = np.ones(n_labs) * alpha + else: + assert len(alpha) == n_labs, "alpha must be same size as number of cell types" + + # generate probability of sampling label at each spot + sp_props = rng.dirichlet(alpha, size=n_obs) + # number of cells present at each spot + n_cells = rng.integers(cell_lb, cell_ub, size=n_obs) + + # initialize spatial expression matrix + sp_x = np.zeros((n_obs, n_genes)) + # initialize spatial proportion matrix + sp_p = np.zeros((n_obs, n_labs)) + # initialize spatial cell number matrix + sp_c = np.zeros(sp_p.shape) + + # generate expression vector for each spot (s) + for s in range(n_obs): + # number of cells from each label at s + raw_s = rng.multinomial(n_cells[s], pvals=sp_props[s, :]) + # store number of cells from each type at s + sp_c[s, :] = raw_s + # compute proportion of each type at s + prop_s = raw_s / n_cells[s] + # store proportion of each type at s + sp_p[s, :] = prop_s + + # initialize transcript pool at s + pool_s = np.zeros(n_genes) + + # add molecules to transcript pool + for lab, n in enumerate(raw_s): + # get indices of cells from which transcripts should be added + idx_sl = rng.choice(label_indices[uni_labs[lab]], size=n) + # add molecules to pool + pool_s += X[idx_sl, :].sum(axis=0).A.flatten() + + # number of UMIs at spot s + n_umis = rng.integers(umi_lb, umi_ub) + # compute probability of sampling UMI from gene + prob_pool_s = pool_s / pool_s.sum() + + # sample transcripts from pool + sp_x[s, :] = np.random.multinomial(n=n_umis, pvals=prob_pool_s) + + obs_names = ["spatial_{}".format(x) for x in range(n_obs)] + adata_spatial = ad.AnnData( + sp_x, + obs=dict(obs_names=obs_names), + var=dict(var_names=adata.var_names), + ) + + # fake coordinates + adata_spatial.obsm["spatial"] = rng.random((adata_spatial.shape[0], 2)) + adata_spatial.obsm["proportions_true"] = sp_p + adata_spatial.obs["n_cells"] = n_cells + adata_spatial.obsm["n_cells"] = sp_c + adata_merged = merge_sc_and_sp(adata, adata_spatial, test=test) + adata_merged.X[adata_merged.X == np.inf] = adata_merged.X.max() # remove inf + adata_merged.layers["counts"] = adata_merged.X.copy() + + return adata_merged + + +def filter_celltypes(adata, min_cells=CELLTYPE_MIN_CELLS): + """Filter rare celltypes from an AnnData""" + celltype_counts = adata.obs["label"].value_counts() >= min_cells + keep_cells = np.isin(adata.obs["label"], celltype_counts.index[celltype_counts]) + return adata[adata.obs.index[keep_cells]].copy() diff --git a/openproblems/tasks/spatial_decomposition/methods/__init__.py b/openproblems/tasks/spatial_decomposition/methods/__init__.py new file mode 100644 index 0000000000..bb1e6f57c8 --- /dev/null +++ b/openproblems/tasks/spatial_decomposition/methods/__init__.py @@ -0,0 +1,13 @@ +from .cell2location import cell2location_amortised_detection_alpha_20 +from .cell2location import cell2location_detection_alpha_20 +from .cell2location import cell2location_detection_alpha_20_nb +from .cell2location import cell2location_detection_alpha_200 +from .destvi import destvi +from .nmfreg import nmfreg +from .nnls import nnls_scipy +from .random import random_proportion_assignment +from .rctd import rctd +from .seuratv3 import seuratv3 +from .stereoscope import stereoscope +from .tangram import tangram +from .vanillanmf import nmf diff --git a/openproblems/tasks/spatial_decomposition/methods/cell2location.py b/openproblems/tasks/spatial_decomposition/methods/cell2location.py new file mode 100644 index 0000000000..3a599cb7e6 --- /dev/null +++ b/openproblems/tasks/spatial_decomposition/methods/cell2location.py @@ -0,0 +1,275 @@ +from ....tools.decorators import method +from ....tools.utils import check_version +from ..utils import split_sc_and_sp +from typing import Optional + +import functools +import numpy as np + +_cell2location_method = functools.partial( + method, + paper_name="Cell2location maps fine-grained cell types in spatial transcriptomics", + paper_url="https://doi.org/10.1038/s41587-021-01139-4", + paper_year=2022, + code_url="https://github.com/BayraktarLab/cell2location", + image="openproblems-python-extras", +) + + +def _cell2location( + adata, + detection_alpha, + n_cells_per_location=20, + hard_coded_reference=True, + amortised=False, + num_samples=1000, + sc_batch_size=2500, + st_batch_size=None, + test=False, + max_epochs_sc=None, + max_epochs_st=None, +): + + from cell2location.cluster_averages.cluster_averages import compute_cluster_averages + from cell2location.models import Cell2location + from cell2location.models import RegressionModel + from torch.nn import ELU + + if test: + max_epochs_sc = max_epochs_sc or 2 + max_epochs_st = max_epochs_st or 2 + num_samples = num_samples or 10 + else: # pragma: nocover + max_epochs_sc = max_epochs_sc or 250 + max_epochs_st = max_epochs_st or 30000 + num_samples = num_samples or 1000 + + adata_sc, adata = split_sc_and_sp(adata) + + if not hard_coded_reference: + # if "tech" in adata_sc.obs.columns: + # adata_sc.obs["batch_key"] = adata_sc.obs["tech"].copy() + # else: + adata_sc.obs["batch_key"] = "all" + # REFERENCE SIGNATURE ESTIMATION FROM SCRNA + # prepare anndata for the regression model + RegressionModel.setup_anndata( + adata=adata_sc, + # 10X reaction / sample / batch + batch_key="batch_key", + # cell type, covariate used for constructing signatures + labels_key="label", + ) + sc_model = RegressionModel(adata_sc) + sc_model.train(max_epochs=max_epochs_sc, batch_size=sc_batch_size) + # In this section, we export the estimated cell abundance + # (summary of the posterior distribution). + adata_sc = sc_model.export_posterior( + adata_sc, + sample_kwargs={"num_samples": num_samples, "batch_size": sc_batch_size}, + ) + # export estimated expression in each cluster + try: + means_per_cluster = adata_sc.varm["means_per_cluster_mu_fg"] + except KeyError: + # sometimes varm fails for unknown reason + means_per_cluster = adata_sc.var + means_per_cluster = means_per_cluster[ + [ + f"means_per_cluster_mu_fg_{i}" + for i in adata_sc.uns["mod"]["factor_names"] + ] + ].copy() + means_per_cluster.columns = adata_sc.uns["mod"]["factor_names"] + else: + means_per_cluster = compute_cluster_averages( + adata_sc, + labels="label", + layer=None, + use_raw=False, + ) + + # SPATIAL MAPPING + # find shared genes and subset both anndata and reference signatures + intersect = np.intersect1d(adata.var_names, means_per_cluster.index) + adata = adata[:, intersect].copy() + means_per_cluster = means_per_cluster.loc[intersect, :].copy() + + # prepare anndata for cell2location model + adata.obs["sample"] = "all" + Cell2location.setup_anndata(adata=adata, batch_key="sample") + cell2location_kwargs = dict( + cell_state_df=means_per_cluster, + # the expected average cell abundance: tissue-dependent + # hyper-prior which can be estimated from paired histology: + # here = average in the simulated dataset + N_cells_per_location=n_cells_per_location, + # hyperparameter controlling normalisation of + # within-experiment variation in RNA detection: + detection_alpha=detection_alpha, + ) + if amortised: + cell2location_kwargs["amortised"] = True + cell2location_kwargs["encoder_mode"] = "multiple" + cell2location_kwargs["encoder_kwargs"] = { + "dropout_rate": 0.1, + "n_hidden": { + "single": 256, + "n_s_cells_per_location": 10, + "b_s_groups_per_location": 10, + "z_sr_groups_factors": 64, + "w_sf": 256, + "detection_y_s": 20, + }, + "use_batch_norm": False, + "use_layer_norm": True, + "n_layers": 1, + "activation_fn": ELU, + } + # create and train the model + st_model = Cell2location(adata, **cell2location_kwargs) + st_model.train( + max_epochs=max_epochs_st, + # train using full data (batch_size=None) + batch_size=st_batch_size, + # use all data points in training because + # we need to estimate cell abundance at all locations + train_size=1, + ) + # In this section, we export the estimated cell abundance + # (summary of the posterior distribution). + adata = st_model.export_posterior( + adata, + sample_kwargs={ + "num_samples": num_samples, + "batch_size": st_batch_size, + }, + ) + + adata.obsm["proportions_pred"] = adata.obsm["q05_cell_abundance_w_sf"].values + adata.uns["method_code_version"] = check_version("cell2location") + return adata + + +@_cell2location_method( + method_name="Cell2location (detection_alpha=20, reference hard-coded)" +) +def cell2location_detection_alpha_20( + adata, + detection_alpha=20, + n_cells_per_location=20, + hard_coded_reference=True, + amortised=False, + num_samples=1000, + sc_batch_size=2500, + st_batch_size=None, + test: bool = False, + max_epochs_sc: Optional[int] = None, + max_epochs_st: Optional[int] = None, +): + return _cell2location( + adata, + detection_alpha=detection_alpha, + n_cells_per_location=n_cells_per_location, + hard_coded_reference=hard_coded_reference, + amortised=amortised, + num_samples=num_samples, + sc_batch_size=sc_batch_size, + st_batch_size=st_batch_size, + test=test, + max_epochs_sc=max_epochs_sc, + max_epochs_st=max_epochs_st, + ) + + +@_cell2location_method( + method_name="Cell2location (detection_alpha=20, reference NB without batch info)" +) +def cell2location_detection_alpha_20_nb( + adata, + detection_alpha=20, + n_cells_per_location=20, + hard_coded_reference=False, + amortised=False, + num_samples=1000, + sc_batch_size=2500, + st_batch_size=None, + test: bool = False, + max_epochs_sc: Optional[int] = None, + max_epochs_st: Optional[int] = None, +): + return _cell2location( + adata, + detection_alpha=detection_alpha, + n_cells_per_location=n_cells_per_location, + hard_coded_reference=hard_coded_reference, + amortised=amortised, + num_samples=num_samples, + sc_batch_size=sc_batch_size, + st_batch_size=st_batch_size, + test=test, + max_epochs_sc=max_epochs_sc, + max_epochs_st=max_epochs_st, + ) + + +@_cell2location_method( + method_name="Cell2location (detection_alpha=200, reference hard-coded)" +) +def cell2location_detection_alpha_200( + adata, + detection_alpha=200, + n_cells_per_location=20, + hard_coded_reference=True, + amortised=False, + num_samples=1000, + sc_batch_size=2500, + st_batch_size=None, + test: bool = False, + max_epochs_sc: Optional[int] = None, + max_epochs_st: Optional[int] = None, +): + return _cell2location( + adata, + detection_alpha=detection_alpha, + n_cells_per_location=n_cells_per_location, + hard_coded_reference=hard_coded_reference, + amortised=amortised, + num_samples=num_samples, + sc_batch_size=sc_batch_size, + st_batch_size=st_batch_size, + test=test, + max_epochs_sc=max_epochs_sc, + max_epochs_st=max_epochs_st, + ) + + +@_cell2location_method( + method_name="Cell2location, amortised (detection_alpha=20, reference hard-coded)" +) +def cell2location_amortised_detection_alpha_20( + adata, + detection_alpha=20, + n_cells_per_location=20, + hard_coded_reference=True, + amortised=True, + num_samples=1000, + sc_batch_size=2500, + st_batch_size=1024, + test: bool = False, + max_epochs_sc: Optional[int] = None, + max_epochs_st: Optional[int] = None, +): + return _cell2location( + adata, + detection_alpha=detection_alpha, + n_cells_per_location=n_cells_per_location, + hard_coded_reference=hard_coded_reference, + amortised=amortised, + num_samples=num_samples, + sc_batch_size=sc_batch_size, + st_batch_size=st_batch_size, + test=test, + max_epochs_sc=max_epochs_sc, + max_epochs_st=max_epochs_st, + ) diff --git a/openproblems/tasks/spatial_decomposition/methods/destvi.py b/openproblems/tasks/spatial_decomposition/methods/destvi.py new file mode 100644 index 0000000000..9330ba1d92 --- /dev/null +++ b/openproblems/tasks/spatial_decomposition/methods/destvi.py @@ -0,0 +1,51 @@ +from ....tools.decorators import method +from ....tools.utils import check_version +from ..utils import split_sc_and_sp +from typing import Optional + + +@method( + method_name="DestVI", + paper_name="DestVI identifies continuums of cell types in spatial " + "transcriptomics data", + paper_url="https://doi.org/10.1038/s41587-022-01272-8", + paper_year=2022, + code_url="https://github.com/YosefLab/scvi-tools", + image="openproblems-python-extras", +) +def destvi( + adata, + test: bool = False, + max_epochs_sc: Optional[int] = None, + max_epochs_sp: Optional[int] = None, +): + from scvi.model import CondSCVI + from scvi.model import DestVI + + if test: + max_epochs_sp = max_epochs_sp or 10 + max_epochs_sc = max_epochs_sc or 10 + else: # pragma: nocover + max_epochs_sc = max_epochs_sc or 300 + max_epochs_sp = max_epochs_sp or 2500 + + adata_sc, adata = split_sc_and_sp(adata) + + CondSCVI.setup_anndata(adata_sc, labels_key="label") + sc_model = CondSCVI(adata_sc, weight_obs=False) + sc_model.train( + max_epochs=max_epochs_sc, + early_stopping=True, + early_stopping_monitor="reconstruction_loss_train", + ) + DestVI.setup_anndata(adata) + + st_model = DestVI.from_rna_model(adata, sc_model) + st_model.train( + max_epochs=max_epochs_sp, + early_stopping=True, + early_stopping_monitor="reconstruction_loss_train", + ) + adata.obsm["proportions_pred"] = st_model.get_proportions().to_numpy() + adata.uns["method_code_version"] = check_version("scvi-tools") + return adata diff --git a/openproblems/tasks/spatial_decomposition/methods/nmfreg.py b/openproblems/tasks/spatial_decomposition/methods/nmfreg.py new file mode 100644 index 0000000000..0876cb37a1 --- /dev/null +++ b/openproblems/tasks/spatial_decomposition/methods/nmfreg.py @@ -0,0 +1,102 @@ +from ....tools.decorators import method +from ....tools.utils import check_version +from ..utils import split_sc_and_sp + +import numpy as np + + +@method( + method_name="NMF-reg", + paper_name="Slide-seq: A scalable technology for measuring genome-wide" + " expression at high spatial resolution", + paper_url="https://doi.org/10.1126/science.aaw1219", + paper_year=2019, + code_url="https://github.com/tudaga/NMFreg_tutorial", +) +def nmfreg(adata, test=False, factors=None): + """NMF-reg: NMF regression for array-based spatial transcriptomics data. + + Re-implementation from https://github.com/tudaga/NMFreg_tutorial. + Originally developed for Slide-seq data. + + Parameters + ---------- + adata : AnnData + Adata with true proportions and signature matrix. + + Returns + ------- + Adata with predicted proportions saved in `adata.obsm["proportions_pred"]`. + """ + from scipy.optimize import nnls + from scipy.sparse import issparse + from sklearn.decomposition import NMF + from sklearn.preprocessing import StandardScaler + + adata_sc, adata = split_sc_and_sp(adata) + + n_types = adata_sc.obs["label"].cat.categories.shape[0] + + factors = factors or 30 + + # Learn from reference + if issparse(adata_sc.X): + X = adata_sc.X.toarray() + else: + X = adata_sc.X + X_norm = X / X.sum(1)[:, np.newaxis] + X_scaled = StandardScaler(with_mean=False).fit_transform(X_norm) + + model = NMF( + n_components=factors, + init="random", + random_state=17, # TODO(handle random_state) + ) + Ha = model.fit_transform(X_scaled) + Wa = model.components_ + + cluster_df = adata_sc.obs[["label"]].copy() + cluster_df.loc[:, "factor"] = np.argmax(Ha, axis=1) + cluster_df.loc[:, "code"] = cluster_df.label.values.codes + factor_to_cluster_map = np.array( + [ + np.histogram( + cluster_df.loc[cluster_df.factor == k, "code"], + bins=n_types, + range=(0, n_types), + )[0] + for k in range(factors) + ] + ).T + + factor_to_best_celltype = np.argmax(factor_to_cluster_map, axis=0) + + factor_to_best_celltype_matrix = np.zeros((factors, n_types)) + for i, j in enumerate(factor_to_best_celltype): + factor_to_best_celltype_matrix[i, j] = 1 + + Ha_norm = StandardScaler(with_mean=False).fit_transform(Ha) + sc_deconv = np.dot(Ha_norm**2, factor_to_best_celltype_matrix) + + sc_deconv = sc_deconv / sc_deconv.sum(1)[:, np.newaxis] + + # Start run on actual spatial data + if issparse(adata.X): + X_sp = adata.X.toarray() + else: + X_sp = adata.X + X_sp_norm = X_sp / X_sp.sum(1)[:, np.newaxis] + X_sp_scaled = StandardScaler(with_mean=False).fit_transform(X_sp_norm) + + bead_prop_soln = np.array( + [nnls(Wa.T, X_sp_scaled[b, :])[0] for b in range(X_sp_scaled.shape[0])] + ) + bead_prop_soln = StandardScaler(with_mean=False).fit_transform(bead_prop_soln) + bead_prop = np.dot(bead_prop_soln, factor_to_best_celltype_matrix) + + prop = bead_prop / bead_prop.sum(1)[:, np.newaxis] + adata.obsm["proportions_pred"] = prop + + adata.uns["method_code_version"] = check_version("scikit-learn") + + return adata diff --git a/openproblems/tasks/spatial_decomposition/methods/nnls.py b/openproblems/tasks/spatial_decomposition/methods/nnls.py new file mode 100644 index 0000000000..d1caf7b532 --- /dev/null +++ b/openproblems/tasks/spatial_decomposition/methods/nnls.py @@ -0,0 +1,39 @@ +from ....tools.decorators import method +from ....tools.utils import check_version +from ..utils import normalize_coefficients +from ..utils import obs_means +from ..utils import split_sc_and_sp + +import numpy as np + + +@method( + method_name="Non-Negative Least Squares", + paper_name="Solving Least Squares Problems", + paper_url="https://doi.org/10.1137/1.9781611971217", + paper_year=1987, + code_url="https://docs.scipy.org/doc/scipy/" + "reference/generated/scipy.optimize.nnls.html", +) +def nnls_scipy(adata, test=False): + from scipy.optimize import nnls + from scipy.sparse import issparse + + adata_sc, adata = split_sc_and_sp(adata) + adata_means = obs_means(adata_sc, "label") + + X = adata_means.X.T + y = adata.X.T + if issparse(y): + y = y.toarray() + res = np.zeros((y.shape[1], X.shape[1])) # (voxels,cells) + for i in range(y.shape[1]): + x, _ = nnls(X, y[:, i]) + res[i] = x + + res_prop = normalize_coefficients(res) + + adata.obsm["proportions_pred"] = res_prop + adata.uns["method_code_version"] = check_version("scipy") + + return adata diff --git a/openproblems/tasks/spatial_decomposition/methods/random.py b/openproblems/tasks/spatial_decomposition/methods/random.py new file mode 100644 index 0000000000..bb3f930e78 --- /dev/null +++ b/openproblems/tasks/spatial_decomposition/methods/random.py @@ -0,0 +1,26 @@ +from ....tools.decorators import method +from ....tools.utils import check_version +from ..utils import split_sc_and_sp + +import numpy as np + + +@method( + method_name="Random assignment (baseline)", + paper_name="Open Problems for Single Cell Analysis", + paper_url="https://openproblems.bio", + paper_year=2022, + code_url="https://github.com/openproblems-bio/openproblems", +) +def random_proportion_assignment(adata, test=False): + _, adata = split_sc_and_sp(adata) + n_types = adata.obsm["proportions_true"].shape[1] + props = np.random.dirichlet( + np.ones(n_types), + size=adata.shape[0], + ) + + adata.obsm["proportions_pred"] = props + adata.uns["method_code_version"] = check_version("openproblems") + + return adata diff --git a/openproblems/tasks/spatial_decomposition/methods/rctd.R b/openproblems/tasks/spatial_decomposition/methods/rctd.R new file mode 100644 index 0000000000..5989b3eb7c --- /dev/null +++ b/openproblems/tasks/spatial_decomposition/methods/rctd.R @@ -0,0 +1,52 @@ +#' RCTD +#' +#' R base for rctd.py +#' +#' @param sce_sc SingleCellExperiment single-cell dataset +#' @param sce_sp SingleCellExperiment spatial dataset +#' @return sce_sp SingleCellExperiment spatial dataset with predictions in obs + +library(spacexr) +library(SingleCellExperiment) +library(Matrix) + +# get single cell reference counts +sc_counts <- assay(sce_sc, "X") +# get single cell reference labels +sc_cell_types <- factor(colData(sce_sc)$label) +names(sc_cell_types) <- colnames(sce_sc) + +# construct reference object (specific for RCTD) +reference <- Reference(sc_counts, sc_cell_types) +# get spatial data counts +sp_counts <- assay(sce_sp, "X") +# get spatial data coordinates +sp_coords <- as.data.frame(reducedDim(sce_sp, "spatial")) +colnames(sp_coords) <- c("x", "y") +rownames(sp_coords) <- colnames(sce_sp) +rownames(sp_counts) <- rownames(sce_sp) +colnames(sp_counts) <- colnames(sce_sp) +# create spatial object to use in RCTD +puck <- SpatialRNA(sp_coords, sp_counts) +# create RCTD object from reference and spatialRNA objects +my_rctd <- create.RCTD( + puck, reference, + max_cores = 1, + test_mode = FALSE, UMI_min_sigma = 100 +) +# run analysis and get results +my_rctd <- run.RCTD(my_rctd) +results <- my_rctd@results +cell_type_names <- my_rctd@cell_type_info$info[[2]] +# extract proportions and normalize them (to sum to one) +norm_weights <- sweep(results$weights, 1, rowSums(results$weights), "/") +norm_weights <- as.data.frame(as.matrix(norm_weights)) + +# proportions estimates needs to be added to colData due +# to bad conversion from reducedDims to obsm (loss of colnames) +colnames(norm_weights) <- paste0("xCT_", cell_type_names) +rownames(norm_weights) <- colnames(sce_sp) +colData(sce_sp) <- cbind(colData(sce_sp), norm_weights) + +# return results +sce_sp diff --git a/openproblems/tasks/spatial_decomposition/methods/rctd.py b/openproblems/tasks/spatial_decomposition/methods/rctd.py new file mode 100644 index 0000000000..c05b703463 --- /dev/null +++ b/openproblems/tasks/spatial_decomposition/methods/rctd.py @@ -0,0 +1,36 @@ +from ....tools.conversion import r_function +from ....tools.decorators import method +from ....tools.utils import check_r_version +from ..utils import split_sc_and_sp + +import numpy as np + +_rctd = r_function("rctd.R", args="sce_sc, sce_sp") + + +@method( + method_name="RCTD", + paper_name="Robust decomposition of cell type mixtures in spatial transcriptomics", + paper_url="https://doi.org/10.1038/s41587-021-00830-w", + paper_year=2020, + code_url="https://github.com/dmcable/spacexr", + image="openproblems-r-extras", +) +def rctd(adata, test=False): + # exctract single cell reference data + adata_sc, adata = split_sc_and_sp(adata) + + # set spatial coordinates for the single cell data + adata_sc.obsm["spatial"] = np.ones((adata_sc.shape[0], 2)) + # run RCTD + adata = _rctd(adata_sc, adata) + + # get predicted cell type proportions from obs + cell_type_names = [x for x in adata.obs.columns if x.startswith("xCT")] + + # add proportions + adata.obsm["proportions_pred"] = adata.obs[cell_type_names].to_numpy() + + adata.uns["method_code_version"] = check_r_version("spacexr") + + return adata diff --git a/openproblems/tasks/spatial_decomposition/methods/seuratv3.R b/openproblems/tasks/spatial_decomposition/methods/seuratv3.R new file mode 100644 index 0000000000..7aa7d05273 --- /dev/null +++ b/openproblems/tasks/spatial_decomposition/methods/seuratv3.R @@ -0,0 +1,61 @@ +#' Seuratv3 TransferData +#' @param sce_sc SingleCellExperiment single-cell data +#' @param sce_sp SingleCellExperiment spatial data +#' @param n_pcs int Number of principal components + +options(error = rlang::entrace) + +library(Seurat) +library(future) + +# 8GB max size up from default of 500MB +options(future.globals.maxSize = 8 * 1024^3) +plan(multicore, workers = availableCores()) + +args <- readRDS("/tmp/openproblems_seurat_args.rds") +sce_sc <- args$sce_sc +sce_sp <- args$sce_sp +n_pcs <- args$n_pcs + +# R base for seuratv3.py +sce_sc <- as.Seurat(sce_sc, counts = "X", data = NULL) +sce_sp <- as.Seurat(sce_sp, counts = "X", data = NULL) + +# Normalize and do dimred for spatial data +sce_sp <- SCTransform(sce_sp, assay = "originalexp", verbose = TRUE) + +sce_sp <- RunPCA(sce_sp, assay = "SCT", verbose = FALSE, n_pcs = n_pcs) + +# Normalize and do dimred for single cell data +sce_sc <- SCTransform( + sce_sc, + assay = "originalexp", ncells = min(3000, nrow(sce_sc)), verbose = TRUE +) +sce_sc <- RunPCA(sce_sc, verbose = FALSE, n_pcs = n_pcs) + +# find anchors (MNN's to compute adjustmen vectors) +anchors <- FindTransferAnchors( + reference = sce_sc, + query = sce_sp, + normalization.method = "SCT" +) + +# transfer labels from single cell data to spatial +predictions_assay <- TransferData( + anchorset = anchors, + refdata = as.factor(as.character(sce_sc@meta.data$label)), + prediction.assay = TRUE, + weight.reduction = sce_sp[["pca"]], + dims = 1:n_pcs +) + +# format data and return results +predictions <- GetAssayData(predictions_assay, slot = "data") +predictions <- predictions[!(rownames(predictions) == "max"), ] +rownames(predictions) <- paste0("xCT_", rownames(predictions)) +colnames(predictions) <- colnames(sce_sp) +predictions <- as.data.frame(t(predictions)) +sce_sp@meta.data <- cbind(sce_sp@meta.data, predictions) +sce_sp <- as.SingleCellExperiment(sce_sp) + +saveRDS(sce_sp, "/tmp/openproblems_seurat_sce_sp_out.rds") diff --git a/openproblems/tasks/spatial_decomposition/methods/seuratv3.py b/openproblems/tasks/spatial_decomposition/methods/seuratv3.py new file mode 100644 index 0000000000..9e5809d33f --- /dev/null +++ b/openproblems/tasks/spatial_decomposition/methods/seuratv3.py @@ -0,0 +1,46 @@ +from ....tools.conversion import r_function +from ....tools.decorators import method +from ....tools.utils import check_r_version +from ..utils import split_sc_and_sp +from typing import Optional + +import pandas as pd +import pathlib + +_seuratv3 = r_function("seuratv3_wrapper.R", args="sce_sc, sce_sp, n_pcs, script_path") + + +@method( + method_name="SeuratV3", + paper_name="Comprehensive Integration of Single-Cell Data", + paper_url="https://doi.org/10.1016/j.cell.2019.05.031", + paper_year=2019, + code_url="https://satijalab.org/seurat/archive/v3.2/spatial_vignette.html", + image="openproblems-r-extras", +) +def seuratv3(adata, test: bool = False, n_pca: Optional[int] = None): + if test: + n_pca = n_pca or 10 + else: # pragma: nocover + n_pca = n_pca or 30 + # extract single cell reference data + adata_sc, adata = split_sc_and_sp(adata) + # proportions_true gets lost in translation + proportions_true = adata.obsm["proportions_true"] + adata = _seuratv3( + adata_sc, + adata, + n_pcs=n_pca, + script_path=pathlib.Path(__file__).parent.joinpath("seuratv3.R").as_posix(), + ) + # get predicted cell type proportions from obs + cell_type_names = pd.Index([x for x in adata.obs.columns if x.startswith("xCT_")]) + proportions_pred = adata.obs[cell_type_names].to_numpy() + + # add proportions + adata.obsm["proportions_pred"] = proportions_pred + adata.obsm["proportions_true"] = proportions_true + + adata.uns["method_code_version"] = check_r_version("Seurat") + + return adata diff --git a/openproblems/tasks/spatial_decomposition/methods/seuratv3_wrapper.R b/openproblems/tasks/spatial_decomposition/methods/seuratv3_wrapper.R new file mode 100644 index 0000000000..f5fb26a1aa --- /dev/null +++ b/openproblems/tasks/spatial_decomposition/methods/seuratv3_wrapper.R @@ -0,0 +1,24 @@ +#' Seuratv3 TransferData wrapper +#' +#' rpy2 + seurat causes R to hang indefinitely. +#' This hack forks R to a separate process. +#' +#' @param sce_sc SingleCellExperiment single-cell data +#' @param sce_sp SingleCellExperiment spatial data +#' @param n_pcs int Number of principal components +#' @param script_path character Path to seuratv3.R + +saveRDS( + list(sce_sc = sce_sc, sce_sp = sce_sp, n_pcs = n_pcs), + "/tmp/openproblems_seurat_args.rds" +) +# clear memory +rm("sce_sc", "sce_sp") + +exitcode <- system2("Rscript", script_path) +if (exitcode != 0) { + stop(paste0("Rscript failed with error code ", as.character(exitcode))) +} + +sce_sp <- readRDS("/tmp/openproblems_seurat_sce_sp_out.rds") +sce_sp diff --git a/openproblems/tasks/spatial_decomposition/methods/stereoscope.py b/openproblems/tasks/spatial_decomposition/methods/stereoscope.py new file mode 100644 index 0000000000..c2695e4253 --- /dev/null +++ b/openproblems/tasks/spatial_decomposition/methods/stereoscope.py @@ -0,0 +1,45 @@ +from ....tools.decorators import method +from ....tools.utils import check_version +from ..utils import split_sc_and_sp + + +@method( + method_name="Stereoscope", + paper_name="Single-cell and spatial transcriptomics enables probabilistic " + "inference of cell type topography", + paper_url="https://doi.org/10.1038/s41587-022-01272-8", + paper_year=2020, + code_url="https://github.com/scverse/scvi-tools", + image="openproblems-python-extras", +) +def stereoscope(adata, test=False, max_epochs_sc=None, max_epochs_sp=None): + from scvi.external import RNAStereoscope + from scvi.external import SpatialStereoscope + + if test: + max_epochs_sp = max_epochs_sp or 10 + max_epochs_sc = max_epochs_sc or 10 + else: # pragma: nocover + max_epochs_sc = max_epochs_sc or 100 + max_epochs_sp = max_epochs_sp or 1000 + + adata_sc, adata = split_sc_and_sp(adata) + + RNAStereoscope.setup_anndata(adata_sc, labels_key="label") + sc_model = RNAStereoscope(adata_sc) + sc_model.train( + max_epochs=max_epochs_sc, + early_stopping=True, + early_stopping_monitor="elbo_train", + ) + + SpatialStereoscope.setup_anndata(adata) + st_model = SpatialStereoscope.from_rna_model(adata, sc_model) + st_model.train( + max_epochs=max_epochs_sp, + early_stopping=True, + early_stopping_monitor="elbo_train", + ) + adata.obsm["proportions_pred"] = st_model.get_proportions().to_numpy() + adata.uns["method_code_version"] = check_version("scvi-tools") + return adata diff --git a/openproblems/tasks/spatial_decomposition/methods/tangram.py b/openproblems/tasks/spatial_decomposition/methods/tangram.py new file mode 100644 index 0000000000..329676c4aa --- /dev/null +++ b/openproblems/tasks/spatial_decomposition/methods/tangram.py @@ -0,0 +1,74 @@ +from ....tools.decorators import method +from ....tools.utils import check_version +from ..utils import split_sc_and_sp + + +@method( + method_name="Tangram", + paper_name="Deep learning and alignment of spatially resolved single-cell " + "transcriptomes with Tangram", + paper_url="https://doi.org/10.1038/s41592-021-01264-7", + paper_year=2021, + code_url="https://github.com/broadinstitute/Tangram", + image="openproblems-python-extras", +) +def tangram(adata, test=False, num_epochs=None, n_markers=None): + # analysis based on: + # github.com/broadinstitute/Tangram/blob/master/tutorial_tangram_with_squidpy.ipynb + # using tangram from PyPi, not github version + + import pandas as pd + import scanpy as sc + import tangram as tg + import torch + + if test: + num_epochs = num_epochs or 10 + n_markers = n_markers or 10 + else: # pragma: nocover + num_epochs = num_epochs or 1000 + n_markers = n_markers or 100 + + # get single cell reference + ad_sc, adata = split_sc_and_sp(adata) + # pre-process single cell data + sc.pp.normalize_total(ad_sc, 1e4) + sc.pp.log1p(ad_sc) + # identify marker genes + sc.tl.rank_genes_groups(ad_sc, groupby="label", use_raw=False) + + # extract marker genes to data frame + markers_df = pd.DataFrame(ad_sc.uns["rank_genes_groups"]["names"]).iloc[ + 0:n_markers, : + ] + + # get union of all marker genes + markers = list(set(markers_df.melt().value.values)) + + # match genes between single cell and spatial data + tg.pp_adatas(ad_sc, adata, genes=markers) + + # get device + device = torch.device("cuda" if torch.cuda.is_available() else "cpu") + + # map single cells to spatial locations + ad_map = tg.map_cells_to_space( + ad_sc, + adata, + device=device, + num_epochs=num_epochs, + ) + + # transfer labels from mapped cells to spatial location + tg.project_cell_annotations(adata_map=ad_map, adata_sp=adata, annotation="label") + + # normalize scores + pred_props = adata.obsm["tangram_ct_pred"].to_numpy() + adata.obsm["proportions_pred"] = pred_props / pred_props.sum(axis=1)[:, None] + + # remove un-normalized predictions + del adata.obsm["tangram_ct_pred"] + + adata.uns["method_code_version"] = check_version("tangram-sc") + + return adata diff --git a/openproblems/tasks/spatial_decomposition/methods/vanillanmf.py b/openproblems/tasks/spatial_decomposition/methods/vanillanmf.py new file mode 100644 index 0000000000..e9ff3e4d16 --- /dev/null +++ b/openproblems/tasks/spatial_decomposition/methods/vanillanmf.py @@ -0,0 +1,61 @@ +from ....tools.decorators import method +from ....tools.utils import check_version +from ..utils import obs_means +from ..utils import split_sc_and_sp + + +@method( + method_name="Non-Negative Matrix Factorization (NMF)", + paper_name="Fast local algorithms for large scale nonnegative " + "matrix and tensor factorizations", + paper_url="https://doi.org/10.1587/transfun.E92.A.708", + paper_year=2009, + code_url="https://scikit-learn.org/stable/modules/generated/" + "sklearn.decomposition.NMF.html", +) +def nmf(adata, test=False, max_iter=None, random_state=17): + """NMF for spatial deconvolution.""" + from scipy.sparse import issparse + from sklearn.decomposition import NMF + + import numpy as np + + adata_sc, adata = split_sc_and_sp(adata) + n_types = adata_sc.obs["label"].cat.categories.shape[0] + + if test: + max_iter = max_iter or 10 + else: # pragma: nocover + max_iter = max_iter or 4000 + + vanila_nmf_model = NMF( + n_components=n_types, + beta_loss="kullback-leibler", + solver="mu", + max_iter=max_iter, + alpha_W=0.1, + alpha_H=0.1, + init="custom", + random_state=random_state, + ) + + # Make profiles from single-cell expression dataset + adata_means = obs_means(adata_sc, "label") + + if issparse(adata.X): + X = adata.X.toarray() + else: + X = adata.X + + Wa = vanila_nmf_model.fit_transform( + X.astype(adata_means.X.dtype), + H=adata_means.X, + W=np.ones((adata.shape[0], n_types), dtype=adata_means.X.dtype), + ) + + prop = Wa / Wa.sum(1)[:, np.newaxis] + adata.obsm["proportions_pred"] = prop + + adata.uns["method_code_version"] = check_version("scikit-learn") + + return adata diff --git a/openproblems/tasks/spatial_decomposition/metrics/__init__.py b/openproblems/tasks/spatial_decomposition/metrics/__init__.py new file mode 100644 index 0000000000..6d3ea04b3a --- /dev/null +++ b/openproblems/tasks/spatial_decomposition/metrics/__init__.py @@ -0,0 +1 @@ +from .r2 import r2 diff --git a/openproblems/tasks/spatial_decomposition/metrics/r2.py b/openproblems/tasks/spatial_decomposition/metrics/r2.py new file mode 100644 index 0000000000..083f23ffde --- /dev/null +++ b/openproblems/tasks/spatial_decomposition/metrics/r2.py @@ -0,0 +1,14 @@ +from ....tools.decorators import metric + + +@metric(metric_name="r2", maximize=True) +def r2(adata): + import sklearn.metrics + + prop_true = adata.obsm["proportions_true"] + prop_pred = adata.obsm["proportions_pred"] + + r2_score = sklearn.metrics.r2_score( + prop_true, prop_pred, sample_weight=None, multioutput="uniform_average" + ) + return r2_score diff --git a/openproblems/tasks/spatial_decomposition/utils.py b/openproblems/tasks/spatial_decomposition/utils.py new file mode 100644 index 0000000000..7f6d329594 --- /dev/null +++ b/openproblems/tasks/spatial_decomposition/utils.py @@ -0,0 +1,72 @@ +from typing import Optional +from typing import Tuple + +import anndata as ad +import numpy as np + + +def merge_sc_and_sp( + adata_sc: ad.AnnData, + adata_sp: ad.AnnData, + batch_key: str = "modality", + test: bool = False, + test_n_genes: int = 1000, +) -> ad.AnnData: + + # merge single cell and spatial data + adata_merged = ad.concat( + {"sp": adata_sp, "sc": adata_sc}, + label=batch_key, + join="outer", + index_unique=None, + merge="unique", + uns_merge="unique", + ) + + adata_merged.strings_to_categoricals() + + if test: + adata_merged = adata_merged[:, :test_n_genes].copy() + + return adata_merged + + +def split_sc_and_sp( + adata_merged: ad.AnnData, + batch_key: str = "modality", +) -> Tuple[ad.AnnData, ad.AnnData]: + + # split single cell and spatial data + is_sp = adata_merged.obs[batch_key] == "sp" + adata_sp = adata_merged[is_sp, :].copy() + adata_sc = adata_merged[~is_sp, :].copy() + + return adata_sc, adata_sp + + +def obs_means( + adata: ad.AnnData, cluster_key: str, obsm: Optional[str] = None +) -> ad.AnnData: + """Return means over observation key.""" + + labels = adata.obs[cluster_key].cat.categories + n_var = adata.shape[1] if obsm is None else adata.obsm[obsm].shape[1] + means = np.empty((labels.shape[0], n_var)) + for i, lab in enumerate(labels): + adata_lab = adata[adata.obs[cluster_key] == lab] + x_lab = adata_lab.X if obsm is None else adata_lab.obsm[obsm] + means[i, :] = x_lab.mean(axis=0).flatten() + adata_means = ad.AnnData(means) + adata_means.obs_names = labels + if obsm is None: + adata_means.var_names = adata.var_names + + return adata_means + + +def normalize_coefficients(prop: np.array) -> np.array: + """Normalize coefficients to sum to 1.""" + prop = prop.copy() + prop[prop < 0] = 0 + prop = prop / prop.sum(axis=1, keepdims=1) + return prop diff --git a/openproblems/tasks/task_overview.md b/openproblems/tasks/task_overview.md deleted file mode 100644 index b843dda736..0000000000 --- a/openproblems/tasks/task_overview.md +++ /dev/null @@ -1,8 +0,0 @@ -# Dimensionality reduction - -The goal of dimensionality reduction is to take a high-dimensional dataset and transform the data into a low-dimensional representation while preserving salient features of the original data. - - -# Regulatory effect prediction - -Chromatin accessibility, measured using [ATAC-seq](https://en.wikipedia.org/wiki/ATAC-seq) gives information about what parts of DNA are available for transcription. The goal of this task is to use scATAC-seq to predict gene expression in the same cells using joint-profiling experiments like [SHARE-seq](https://doi.org/10.1016/j.cell.2020.09.056). diff --git a/openproblems/tools/decorators.py b/openproblems/tools/decorators.py index 437c9fe049..2e102eca4a 100644 --- a/openproblems/tools/decorators.py +++ b/openproblems/tools/decorators.py @@ -126,7 +126,11 @@ def apply_metric(*args, **kwargs): def dataset( - dataset_name=None, data_url=None, dataset_summary=None, image="openproblems" + dataset_name=None, + data_url=None, + data_reference=None, + dataset_summary=None, + image="openproblems", ): """Decorate a dataset function. @@ -136,6 +140,8 @@ def dataset( Unique human readable name of the dataset data_url : str Link to the original source of the dataset + data_reference : str + Link to the paper describing how the dataset was generated dataset_summary : str Short (<80 character) summary of the dataset image : str, optional (default: "openproblems") @@ -154,6 +160,7 @@ def apply_func(*args, **kwargs): dataset_name=dataset_name, image=image, data_url=data_url, + data_reference=data_reference, dataset_summary=dataset_summary, ) return apply_func diff --git a/openproblems/tools/normalize.py b/openproblems/tools/normalize.py index 7e9e2deb30..e8fba61c23 100644 --- a/openproblems/tools/normalize.py +++ b/openproblems/tools/normalize.py @@ -14,11 +14,15 @@ """, args="sce, min.mean=0.1", body=""" + if(is(sce@assays@data$X, "RsparseMatrix")) { + # scran does not work with RsparseMatrix + sce@assays@data$X <- as(sce@assays@data$X, "CsparseMatrix") + } sce <- computeSumFactors( - sce, min.mean=min.mean, - assay.type="X", - BPPARAM=SerialParam() - ) + sce, min.mean=min.mean, + assay.type="X", + BPPARAM=SerialParam() + ) sizeFactors(sce) """, ) diff --git a/pytest.ini b/pytest.ini new file mode 100644 index 0000000000..447d92b2a1 --- /dev/null +++ b/pytest.ini @@ -0,0 +1,13 @@ +[pytest] +filterwarnings = + error + ignore:The `inplace` parameter in pandas\.Categorical\.reorder_categories is deprecated:FutureWarning + ignore:This package has been superseded by the `leidenalg` package:DeprecationWarning + ignore:Please use `spmatrix` from the `scipy\.sparse` namespace:DeprecationWarning + ignore:The global conversion available with activate\(\) is deprecated:DeprecationWarning + ignore:`np\.[a-z]*` is a deprecated alias for the builtin `[a-z]*`:DeprecationWarning + ignore:Element '.*' was written without encoding metadata: + ignore:X\.dtype being converted to np\.float32 from float64:FutureWarning + ignore:is_categorical is deprecated and will be removed in a future version:FutureWarning + ignore:The use of (converter|py2rpy|rpy2py) in module rpy2.robjects.conversion is deprecated.:DeprecationWarning + always:Container failed with AssertionError\. Retrying [0-9]* more time:RuntimeWarning diff --git a/scripts/install_renv.R b/scripts/install_renv.R index a2aac629f3..b3c6698850 100644 --- a/scripts/install_renv.R +++ b/scripts/install_renv.R @@ -6,7 +6,7 @@ as_integer_version <- function(v) { compare_version <- function(v1, v2) { v1 <- as_integer_version(v1) v2 <- as_integer_version(v2) - for (i in 1:min(length(v1), length(v2))) { + for (i in seq_len(min(length(v1), length(v2)))) { if (v1[i] != v2[i]) { return(FALSE) } diff --git a/scripts/upgrade_renv.R b/scripts/upgrade_renv.R index 70697e92db..6c74adb033 100644 --- a/scripts/upgrade_renv.R +++ b/scripts/upgrade_renv.R @@ -19,10 +19,15 @@ upgrade_first_available <- function(remotes) { for (remote in remotes) { cat(paste0(remote, "\n")) parsed_spec <- renv:::renv_remotes_parse(remote) - result <- renv::update(parsed_spec$package, prompt = FALSE) + package <- parsed_spec$package + if (is.null(package)) { + package <- parsed_spec$repo + } + result <- renv::update(package, prompt = FALSE) if (class(result) != "logical") { upgraded_remotes <- sapply(result, upgraded_remote_version) - return(paste(upgraded_remotes, collapse = "\n")) + primary <- upgraded_remotes[[package]] + return(list(primary = primary, upgraded = upgraded_remotes)) } } } @@ -39,22 +44,34 @@ strip_comments <- function(remote) { gsub("\\s*#.*", "", remote) } +write_updates_to_file <- function(curr_remotes, new_remotes, filename) { + current_names <- gsub("@.*", "", curr_remotes) + new_names <- gsub("@.*", "", new_remotes) + keep_current <- !(current_names %in% new_names) + write_remotes <- sort(c(curr_remotes[keep_current], new_remotes)) + writeLines(write_remotes, filename, sep = "\n") +} + upgrade_renv <- function(requirements_file) { remotes <- scan(requirements_file, what = character(), sep = "\n") if (length(remotes) > 0) { - remotes <- drop_pinned(remotes) - remotes <- sapply(remotes, strip_comments) - capture.output(suppressWarnings(suppressMessages( - output <- upgrade_first_available(remotes) - )), file = nullfile()) - if (!is.null(output)) { - cat("Upgrades are available:\n") - cat(output) + remotes_parsed <- drop_pinned(remotes) + remotes_parsed <- sapply(remotes_parsed, strip_comments) + upgraded_remotes <- upgrade_first_available(remotes_parsed) + if (!is.null(upgraded_remotes)) { + cat("\nUpgrades are available:\n") + cat(paste(upgraded_remotes$upgraded, collapse = "\n")) cat("\n") + write_updates_to_file( + remotes, upgraded_remotes$upgraded, requirements_file + ) + cat(paste0("\nUpgrade triggered by:\n", upgraded_remotes$primary, "\n")) } else { - cat("No upgrades available\n") + cat("\nNo upgrades available\n\n") } + } else { + cat("\n") } } -upgrade_renv(commandArgs(trailingOnly = TRUE)[1]) +suppressWarnings(upgrade_renv(commandArgs(trailingOnly = TRUE)[1])) diff --git a/setup.py b/setup.py index dee71d4d09..6012e3335f 100644 --- a/setup.py +++ b/setup.py @@ -7,10 +7,11 @@ "numpy>=1.22,<1.24", "scikit-learn==1.1.*", "anndata==0.8.*", - "scprep>=1.2.0", - "scipy==1.8.*", + "scprep>=1.2.1", + "scipy>=1.8,<1.10", "scanpy>=1.6", "louvain==0.7.*", + "python-igraph<0.10", "decorator<5.0", # pinned in #324 "memory-profiler==0.60", "colorama==0.4.*", @@ -19,18 +20,18 @@ ] r_requires = [ - "rpy2<3.5.3", + "rpy2<3.5.5", "anndata2ri==1.1.*", ] -evaluate_requires = ["snakemake==7.8.*"] +evaluate_requires = ["snakemake>=7.8,<7.15", "tabulate<0.9"] process_requires = ["numpyencoder==0.3.*"] test_requires = [ "pytest==7.1.*", - "pytest-cov==3.0.*", - "black==22.6.0", + "pytest-cov>=3.0,<4.1", + "black==22.8.0", "coverage==6.4.*", "codecov==2.1.*", "parameterized==0.8.*", diff --git a/test/test_0_tasks.py b/test/test_0_tasks.py index e208f14920..f8851f56ec 100644 --- a/test/test_0_tasks.py +++ b/test/test_0_tasks.py @@ -1,11 +1,9 @@ -import utils.warnings # noqa: F401 - -# isort: split import openproblems import parameterized import unittest import utils.name +TASK_SUMMARY_MINLEN = 40 TASK_SUMMARY_MAXLEN = 80 MODULE = type(openproblems) @@ -27,7 +25,11 @@ def test_members(self): assert isinstance(self.task._task_name, str) assert hasattr(self.task, "_task_summary") assert isinstance(self.task._task_summary, str) + assert len(self.task._task_summary) > TASK_SUMMARY_MINLEN assert len(self.task._task_summary) < TASK_SUMMARY_MAXLEN + assert hasattr(self.task, "DEFAULT_LAYER") + assert isinstance(self.task.DEFAULT_LAYER, str) + assert self.task.DEFAULT_LAYER in ["counts", "log_normalized"] assert hasattr(self.task, "api") assert isinstance(self.task.api, MODULE) for list_name in ["DATASETS", "METHODS", "METRICS"]: diff --git a/test/test_1_methods.py b/test/test_1_methods.py index 3116dc9dc2..e765724f9d 100644 --- a/test/test_1_methods.py +++ b/test/test_1_methods.py @@ -1,7 +1,5 @@ -import utils.warnings # noqa: F401 - -# isort: split import openproblems +import os import parameterized import pytest import utils.docker @@ -11,6 +9,9 @@ pytestmark = pytest.mark.skipif( len(utils.git.list_modified_tasks()) == 0, reason="No tasks have been modified" ) +RETRIES = ( + int(os.environ["PYTEST_MAX_RETRIES"]) if "PYTEST_MAX_RETRIES" in os.environ else 2 +) @parameterized.parameterized.expand( @@ -26,7 +27,7 @@ name_func=utils.name.name_test, skip_on_empty=True, ) -@utils.docker.docker_test(timeout=600, retries=2) +@utils.docker.docker_test(timeout=600, retries=RETRIES) def test_method(task_name, method_name, image): """Test application of a method.""" import anndata diff --git a/test/test_1_metrics.py b/test/test_1_metrics.py index 8e801c395f..97ac695c99 100644 --- a/test/test_1_metrics.py +++ b/test/test_1_metrics.py @@ -1,6 +1,3 @@ -import utils.warnings # noqa: F401 - -# isort: split import openproblems import parameterized import pytest diff --git a/test/test_2_load_data.py b/test/test_2_load_data.py index cc204cf3e7..bff0acee8c 100644 --- a/test/test_2_load_data.py +++ b/test/test_2_load_data.py @@ -1,6 +1,3 @@ -import utils.warnings # noqa: F401 - -# isort: split import openproblems import parameterized import pytest @@ -39,7 +36,7 @@ def test_load_dataset(task_name, dataset_name, test, tempdir, image): dataset = getattr(task.datasets, dataset_name) adata = dataset(test=test) utils.asserts.assert_finite(adata.X) - utils.asserts.assert_array_equal(adata.X, adata.layers["counts"]) + utils.asserts.assert_array_equal(adata.X, adata.layers[task.DEFAULT_LAYER]) adata2 = dataset(test=test) assert adata2.shape == adata.shape assert adata2.uns["_from_cache"] diff --git a/test/test_3_datasets.py b/test/test_3_datasets.py index ef8c525156..cc0638baa4 100644 --- a/test/test_3_datasets.py +++ b/test/test_3_datasets.py @@ -1,6 +1,3 @@ -import utils.warnings # noqa: F401 - -# isort: split import anndata import openproblems import openproblems.utils @@ -15,7 +12,8 @@ import utils.git import utils.name -DATASET_SUMMARY_MAXLEN = 80 +DATASET_SUMMARY_MINLEN = 40 +DATASET_SUMMARY_MAXLEN = 280 pytestmark = pytest.mark.skipif( len(utils.git.list_modified_tasks()) == 0, reason="No tasks have been modified" @@ -95,14 +93,7 @@ def test_adata_shape(self): def test_sparse(self): """Ensure output is sparse.""" - if not scipy.sparse.issparse(self.adata.X): - openproblems.utils.future_warning( - "{}-{}: self.adata.X is loaded as dense.".format( - self.task.__name__.split(".")[-1], self.dataset.__name__ - ), - error_version="1.0", - error_category=TypeError, - ) + assert scipy.sparse.issparse(self.adata.X) def test_not_bytes(self): """Ensure output does not contain byte strings.""" @@ -134,17 +125,31 @@ def test_normalize(self, normalizer): adata = normalizer(adata) utils.asserts.assert_finite(adata.X) - def test_metadata(self): - """Test for existence of dataset metadata.""" - assert hasattr(self.dataset, "metadata") - for attr in ["dataset_name", "data_url", "dataset_summary", "image"]: - assert attr in self.dataset.metadata - assert self.dataset.metadata[attr] is not None - - assert isinstance(self.dataset.metadata["dataset_name"], str) - assert isinstance(self.dataset.metadata["image"], str) - assert self.dataset.metadata["image"].startswith("openproblems") - assert isinstance(self.dataset.metadata["dataset_summary"], str) - assert len(self.dataset.metadata["dataset_summary"]) < DATASET_SUMMARY_MAXLEN - assert isinstance(self.dataset.metadata["data_url"], str) - assert utils.asserts.assert_url_accessible(self.dataset.metadata["data_url"]) + +@parameterized.parameterized.expand( + [(dataset,) for task in openproblems.TASKS for dataset in task.DATASETS], + name_func=utils.name.name_test, +) +def test_dataset_metadata(dataset): + """Test for existence of dataset metadata.""" + assert hasattr(dataset, "metadata") + for attr in [ + "dataset_name", + "data_url", + "data_reference", + "dataset_summary", + "image", + ]: + assert attr in dataset.metadata + assert dataset.metadata[attr] is not None + + assert isinstance(dataset.metadata["dataset_name"], str) + assert isinstance(dataset.metadata["image"], str) + assert dataset.metadata["image"].startswith("openproblems") + assert isinstance(dataset.metadata["dataset_summary"], str) + assert len(dataset.metadata["dataset_summary"]) > DATASET_SUMMARY_MINLEN + assert len(dataset.metadata["dataset_summary"]) < DATASET_SUMMARY_MAXLEN + assert isinstance(dataset.metadata["data_url"], str) + assert utils.asserts.assert_url_accessible(dataset.metadata["data_url"]) + assert isinstance(dataset.metadata["data_reference"], str) + assert utils.asserts.assert_url_accessible(dataset.metadata["data_reference"]) diff --git a/test/test_4_cell_cell_communication.py b/test/test_4_cell_cell_communication.py new file mode 100644 index 0000000000..192069df79 --- /dev/null +++ b/test/test_4_cell_cell_communication.py @@ -0,0 +1,129 @@ +"""Specific tests for the dimensionality_reduction task""" +import numpy as np +import openproblems +import openproblems.tasks._cell_cell_communication._common as common +import openproblems.tasks._cell_cell_communication._common.api +import openproblems.tasks._cell_cell_communication._common.utils +import os +import pandas as pd +import pytest +import tempfile +import unittest +import utils.docker +import utils.git + +# global skip +SUBTASKS = [ + openproblems.tasks.cell_cell_communication_source_target, + openproblems.tasks.cell_cell_communication_ligand_target, +] +pytestmark = pytest.mark.skipif( + any([task not in utils.git.list_modified_tasks() for task in SUBTASKS]), + reason="Relevant task has not been modified", +) + + +class TestApi(unittest.TestCase): + def test_assert_is_subset(self): + assert ( + common.api.assert_is_subset(["a"], ["a", "b", "c"], prop_missing_allowed=0) + is None + ) + assert ( + common.api.assert_is_subset( + ["a", "b", "c", "d"], ["a", "b", "c"], prop_missing_allowed=0.25 + ) + is None + ) + self.assertRaisesRegex( + AssertionError, + r"test_subset is not a subset of test_superset\. " + "d missing from test_superset", + common.api.assert_is_subset, + ["a", "b", "c", "d"], + ["a", "b", "c"], + superset_name="test_superset", + subset_name="test_subset", + prop_missing_allowed=0, + ) + self.assertRaisesRegex( + AssertionError, + r"Allowed proportion \(0.24\) of missing test_subset elements exceeded " + r"\(0\.25\)\. d missing from test_superset", + common.api.assert_is_subset, + ["a", "b", "c", "d"], + ["a", "b", "c"], + superset_name="test_superset", + subset_name="test_subset", + prop_missing_allowed=0.24, + ) + + def test_map_gene_symbols(self): + adata = common.api.sample_dataset(SUBTASKS[0].api.MERGE_KEYS) + index = adata.var.index.to_numpy() + index[0] = "many_to_one_1" + index[1] = "many_to_one_2" + index[2] = "one_to_many" + index[3] = "one_to_one" + index[4] = "one_to_none" + adata.var.index = index + map_df = pd.DataFrame( + { + "gene": [ + "one_from_many", + "one_from_many", + "many_from_one_1", + "many_from_one_2", + "many_from_one_3", + "one_from_one", + "one_from_none", + ], + "alias": np.concatenate( + [adata.var.index[[0, 1, 2, 2, 2, 3]], ["none_to_one"]] + ), + } + ) + with tempfile.TemporaryDirectory() as tempdir: + map_filename = os.path.join(tempdir, "gene_map.csv") + map_df.to_csv(map_filename) + adata_mapped = common.utils.map_gene_symbols(adata, map_filename) + self.assertEqual(adata_mapped.shape[0], adata.shape[0]) + self.assertEqual(adata_mapped.shape[1], adata.shape[1] + 1) + adata_mapped.X = adata_mapped.X.tocsr() + np.testing.assert_array_equal( + adata[:, ["one_to_one"]].X.toarray(), + adata_mapped[:, ["one_from_one"]].X.toarray(), + ) + np.testing.assert_array_equal( + adata[:, ["one_to_none"]].X.toarray(), + adata_mapped[:, ["one_to_none"]].X.toarray(), + ) + np.testing.assert_array_equal( + adata[:, ["one_to_many", "one_to_many", "one_to_many"]].X.toarray(), + adata_mapped[ + :, ["many_from_one_1", "many_from_one_2", "many_from_one_3"] + ].X.toarray(), + ) + np.testing.assert_array_equal( + adata[:, ["many_to_one_1", "many_to_one_2"]].X.sum(axis=1).A, + adata_mapped[:, ["one_from_many"]].X.toarray(), + ) + self.assertNotIn("one_from_none", adata_mapped.var.index) + + +@utils.docker.docker_test(image=SUBTASKS[0].metrics.odds_ratio.metadata["image"]) +def test_odds_ratio_no_match(): + import numpy as np + + task = openproblems.tasks.cell_cell_communication_source_target + metric = task.metrics.odds_ratio + + adata = task.api.sample_dataset() + adata = task.api.sample_method(adata) + openproblems.log.debug( + "Testing {} metric from {} task".format(metric.__name__, task.__name__) + ) + adata.uns["ccc_target"]["response"] = np.nan + m = metric(adata) + + assert m == 1 diff --git a/test/test_4_dimensionality_reduction.py b/test/test_4_dimensionality_reduction.py index 60d8eb4cf8..e713c8d6ae 100644 --- a/test/test_4_dimensionality_reduction.py +++ b/test/test_4_dimensionality_reduction.py @@ -1,7 +1,4 @@ """Specific tests for the dimensionality_reduction task""" -import utils.warnings # noqa: F401 - -# isort: split import openproblems import pytest import utils.docker diff --git a/test/test_5_cli.py b/test/test_5_cli.py index a5cdd9fd45..389b35d35c 100644 --- a/test/test_5_cli.py +++ b/test/test_5_cli.py @@ -1,6 +1,4 @@ -import utils.warnings # noqa: F401 - -# isort: split +from openproblems.api.hash import docker_labels_from_api from openproblems.api.main import main from openproblems.api.utils import print_output @@ -10,7 +8,7 @@ import parameterized import sklearn import tempfile -import utils +import utils.name def test_print(capsys): @@ -149,6 +147,10 @@ def test_hash(task, function_type, function_name): assert h1 == h2 +def test_hash_docker_api(): + assert docker_labels_from_api("circleci/python", tag="3.8-bullseye") is None + + @parameterized.parameterized.expand( [ (dataset, method, metric) @@ -190,8 +192,8 @@ def __zero_metric(*args): adata = task.api.sample_dataset() with tempfile.TemporaryDirectory() as tempdir: dataset_file = os.path.join(tempdir, "dataset.h5ad") + adata.var = adata.var[[]] adata.write_h5ad(dataset_file) - result = main( [ "evaluate", @@ -212,6 +214,8 @@ def test_pipeline(): with tempfile.TemporaryDirectory() as tempdir: dataset_file = os.path.join(tempdir, "dataset.h5ad") method_file = os.path.join(tempdir, "method.h5ad") + version_file = os.path.join(tempdir, "version.txt") + result_file = os.path.join(tempdir, "result.txt") assert not os.path.isfile(dataset_file) assert not os.path.isfile(method_file) main( @@ -227,7 +231,7 @@ def test_pipeline(): do_print=False, ) assert os.path.isfile(dataset_file) - code_version = main( + main( [ "run", "--task", @@ -236,21 +240,30 @@ def test_pipeline(): dataset_file, "--output", method_file, + "--version-file", + version_file, "logistic_regression_log_cpm", ], do_print=False, ) assert os.path.isfile(method_file) + assert os.path.isfile(version_file) + with open(version_file, "r") as handle: + code_version = handle.read() assert code_version == sklearn.__version__ - result = main( + main( [ "evaluate", "--task", "label_projection", "--input", method_file, + "--output", + result_file, "accuracy", ], do_print=False, ) + with open(result_file, "r") as handle: + result = float(handle.read()) assert isinstance(result, float) diff --git a/test/test_5_tools.py b/test/test_5_tools.py index 577207739b..d0667fd9a8 100644 --- a/test/test_5_tools.py +++ b/test/test_5_tools.py @@ -1,6 +1,3 @@ -import utils.warnings # noqa: F401 - -# isort: split import numpy as np import openproblems import parameterized diff --git a/test/test_5_utils.py b/test/test_5_utils.py index 258c1d7cf2..8ea5084879 100644 --- a/test/test_5_utils.py +++ b/test/test_5_utils.py @@ -1,6 +1,3 @@ -import utils.warnings # noqa: F401 - -# isort: split import numpy as np import openproblems import openproblems.tools.utils diff --git a/test/utils/asserts.py b/test/utils/asserts.py index 630c571716..d5663eed29 100644 --- a/test/utils/asserts.py +++ b/test/utils/asserts.py @@ -1,5 +1,6 @@ from openproblems.tools.utils import assert_finite # noqa +import functools import numpy as np import scipy.sparse @@ -15,6 +16,8 @@ def assert_array_equal(X, Y): if scipy.sparse.issparse(X) and scipy.sparse.issparse(Y): X = X.tocsr() Y = Y.tocsr() + X.sort_indices() + Y.sort_indices() np.testing.assert_array_equal(X.data, Y.data) np.testing.assert_array_equal(X.indices, Y.indices) np.testing.assert_array_equal(X.indptr, Y.indptr) @@ -24,9 +27,19 @@ def assert_array_equal(X, Y): np.testing.assert_array_equal(X, Y) +def _response_ok(response): + if response.ok: + return True + if response.status_code == 429: + # rejected; too many requests + return True + return False + + +@functools.lru_cache(None) def assert_url_accessible(url): import requests with requests.head(url, headers=_REQUEST_HEADERS) as response: - assert response.ok, (url, response.status_code) + assert _response_ok(response), (url, response.status_code) return True diff --git a/test/utils/data.py b/test/utils/data.py index 3b43202856..2e1d8921b9 100644 --- a/test/utils/data.py +++ b/test/utils/data.py @@ -4,7 +4,7 @@ def data(obsm=None): """Create fake data.""" - adata = anndata.AnnData(np.random.poisson(2, (100, 30))) + adata = anndata.AnnData(np.random.poisson(2, (100, 30)).astype(np.float32)) if obsm is not None: adata.obsm[obsm] = adata.X * 2 + 1 adata.uns["{}_obs".format(obsm)] = np.arange(adata.shape[0]) + 5 diff --git a/test/utils/docker.py b/test/utils/docker.py index cdebbc8398..1c6476191f 100644 --- a/test/utils/docker.py +++ b/test/utils/docker.py @@ -106,7 +106,7 @@ def image_requires_docker(image): if not docker_available(): raise RuntimeError( "The Dockerfile for image {} is newer than the " - "latest push, but Docker is not available." + "latest push, but Docker is not available.".format(image) ) if docker_image_age(image) < git_file_age: import sys @@ -223,8 +223,10 @@ def run_image(image, script, *args, timeout=None, retries=0): return run.run(command, timeout=timeout) except Exception as e: if retries > 0 and not isinstance(e, exceptions.TimeoutError): + time = "time" if retries == 1 else "times" warnings.warn( - "Container failed with {}; retrying".format(type(e).__name__), + f"Container failed with {type(e).__name__}. " + f"Retrying {retries} more {time}", RuntimeWarning, ) retries -= 1 diff --git a/test/utils/warnings.py b/test/utils/warnings.py deleted file mode 100644 index dc966e2397..0000000000 --- a/test/utils/warnings.py +++ /dev/null @@ -1,60 +0,0 @@ -import warnings - - -def ignore_warnings(): - """Ignore irrelevant warnings.""" - warnings.filterwarnings( - "ignore", - category=FutureWarning, - message="is_categorical is deprecated and will be removed in a future version." - " Use is_categorical_dtype instead", - module="anndata._core.anndata", - ) - warnings.filterwarnings( - "ignore", - category=FutureWarning, - message="The `inplace` parameter in pandas.Categorical.reorder_categories" - " is deprecated", - module="anndata._core.anndata", - ) - - warnings.filterwarnings( - "ignore", - category=DeprecationWarning, - message="This package has been superseded by the `leidenalg` package", - module="scanpy.tools._louvain", - ) - warnings.filterwarnings( - "ignore", - category=DeprecationWarning, - message="Please use `spmatrix` from the `scipy.sparse` namespace", - module="anndata._core.merge", - ) - warnings.filterwarnings( - "ignore", - category=FutureWarning, - message="The `inplace` parameter in pandas.Categorical.reorder_categories", - module="anndata._core.anndata", - ) - warnings.filterwarnings( - "ignore", - category=DeprecationWarning, - message=r"The global conversion available with activate\(\) is deprecated", - module="rpy2.robjects.numpy2ri", - ) - warnings.filterwarnings( - "ignore", - category=DeprecationWarning, - message=r"The global conversion available with activate\(\) is deprecated", - module="rpy2.robjects.pandas2ri", - ) - - try: - import numba - - warnings.filterwarnings("ignore", category=numba.NumbaWarning) - except ImportError: - pass - - -ignore_warnings() diff --git a/workflow/Snakefile b/workflow/Snakefile index f2a812444f..c8378a3cea 100644 --- a/workflow/Snakefile +++ b/workflow/Snakefile @@ -36,9 +36,10 @@ rule refresh_docker_image: params: sourcedir = os.path.dirname(tools.SCRIPTS_DIR), user = "singlecellopenproblems", - label = "github_actions" if "GITHUB_ACTIONS" in os.environ else "local" + label = tools.build_type, + hash = tools.build_hash, shell: - "docker build --label bio.openproblems.build={params.label} -f {input.dockerfile} -t {params.user}/{wildcards.image} .." + "docker build --label bio.openproblems.build={params.label} --label bio.openproblems.hash={params.hash} -f {input.dockerfile} -t {params.user}/{wildcards.image} .." rule refresh_dockerfile: priority: 50 @@ -86,9 +87,10 @@ rule build_docker_image: params: sourcedir = os.path.dirname(tools.SCRIPTS_DIR), user = "singlecellopenproblems", - label = tools.build_type + label = tools.build_type, + hash = tools.build_hash, shell: - "docker build --label bio.openproblems.build={params.label} -f {input.dockerfile} -t {params.user}/{wildcards.image} .." + "docker build --label bio.openproblems.build={params.label} --label bio.openproblems.hash={params.hash} -f {input.dockerfile} -t {params.user}/{wildcards.image} .." rule password_docker: output: diff --git a/workflow/generate_website_markdown.py b/workflow/generate_website_markdown.py index ff04be0f0d..9bb3bbf643 100644 --- a/workflow/generate_website_markdown.py +++ b/workflow/generate_website_markdown.py @@ -3,6 +3,7 @@ import pathlib import re import sys +import workflow_utils INDEX_TOML_TEMPLATE = """+++ title = "{task_name}" @@ -56,6 +57,9 @@ def write_dataset_md(dataset, outdir): def main(outdir): for task in openproblems.TASKS: + if workflow_utils.task_is_incomplete(task): + # don't write md for incomplete tasks + continue task_outdir = os.path.join(outdir, task.__name__.split(".")[-1]) if not os.path.isdir(task_outdir): pathlib.Path(task_outdir).mkdir(parents=True, exist_ok=True) diff --git a/workflow/parse_nextflow.py b/workflow/parse_nextflow.py index dcd6a59dd3..bc9ef3a21a 100644 --- a/workflow/parse_nextflow.py +++ b/workflow/parse_nextflow.py @@ -2,11 +2,12 @@ import json import numpy as np import numpyencoder -import openproblems.api.utils as utils +import openproblems.api.utils import os import pandas as pd import sys import warnings +import workflow_utils def dump_json(obj, fp): @@ -154,7 +155,7 @@ def compute_ranking(task_name, dataset_results): rankings = np.zeros(len(dataset_results)) metric_names = list(dataset_results.values())[0]["metrics"].keys() for metric_name in metric_names: - metric = utils.get_function(task_name, "metrics", metric_name) + metric = openproblems.api.utils.get_function(task_name, "metrics", metric_name) sorted_order = np.argsort( [ dataset_results[method_name]["metrics"][metric_name] @@ -164,26 +165,32 @@ def compute_ranking(task_name, dataset_results): if metric.metadata["maximize"]: sorted_order = sorted_order[::-1] rankings += np.argsort(sorted_order) + + method_names = list(dataset_results.keys()) final_ranking = { - method_name: rank + 1 - for method_name, rank in zip(dataset_results, np.argsort(np.argsort(rankings))) + method_names[method_idx]: rank + 1 + for method_idx, rank in zip( + np.argsort(rankings), np.arange(len(dataset_results)) + ) } return final_ranking def dataset_results_to_json(task_name, dataset_name, dataset_results): """Convert the raw dataset results to pretty JSON for web.""" - dataset = utils.get_function(task_name, "datasets", dataset_name) + dataset = openproblems.api.utils.get_function(task_name, "datasets", dataset_name) output = dict( name=dataset.metadata["dataset_name"], data_url=dataset.metadata["data_url"], + data_reference=dataset.metadata["data_reference"], headers=dict(names=["Rank"], fixed=["Name", "Paper", "Website", "Code"]), results=list(), ) ranking = compute_ranking(task_name, dataset_results) metric_names = set() - for method_name, method_results in dataset_results.items(): - method = utils.get_function(task_name, "methods", method_name) + for method_name, rank in ranking.items(): + method_results = dataset_results[method_name] + method = openproblems.api.utils.get_function(task_name, "methods", method_name) result = { "Name": method.metadata["method_name"], "Paper": method.metadata["paper_name"], @@ -196,10 +203,12 @@ def dataset_results_to_json(task_name, dataset_name, dataset_results): "Runtime (min)": parse_time_to_min(method_results["realtime"]), "CPU (%)": float(method_results["%cpu"].replace("%", "")), "Memory (GB)": parse_size_to_gb(method_results["peak_rss"]), - "Rank": ranking[method_name], + "Rank": rank, } for metric_name, metric_result in method_results["metrics"].items(): - metric = utils.get_function(task_name, "metrics", metric_name) + metric = openproblems.api.utils.get_function( + task_name, "metrics", metric_name + ) result[metric.metadata["metric_name"]] = metric_result metric_names.add(metric.metadata["metric_name"]) output["results"].append(result) @@ -223,14 +232,25 @@ def results_to_json(results, outdir): if not os.path.isdir(outdir): os.mkdir(outdir) for task_name, task_results in results.items(): + if workflow_utils.task_is_incomplete( + openproblems.api.utils.str_to_task(task_name) + ): + # don't write results for incomplete tasks + continue for dataset_name, dataset_results in task_results.items(): results_dir = os.path.join(outdir, task_name) if not os.path.isdir(results_dir): os.mkdir(results_dir) filename = os.path.join(results_dir, "{}.json".format(dataset_name)) + try: + dataset_results_json = dataset_results_to_json( + task_name, dataset_name, dataset_results + ) + except openproblems.api.utils.NoSuchFunctionError: + continue with open(filename, "w") as handle: dump_json( - dataset_results_to_json(task_name, dataset_name, dataset_results), + dataset_results_json, handle, ) diff --git a/workflow/snakemake_tools.py b/workflow/snakemake_tools.py index 32344f7cd4..8c9b92e42b 100644 --- a/workflow/snakemake_tools.py +++ b/workflow/snakemake_tools.py @@ -3,7 +3,6 @@ import multiprocessing import openproblems import os -import packaging.version import subprocess import sys import time @@ -48,6 +47,19 @@ def build_type(wildcards): return "github_actions" if "GITHUB_ACTIONS" in os.environ else "local" +def _build_age(image): + age = git_file_age(os.path.join(IMAGES_DIR, image)) + base = _docker_base(image) + if base is not None: + age += _build_age(base) + return age + + +def build_hash(wildcards): + age = _build_age(wildcards.image) + return hash(age) + + def push_images(wildcards): """Get Docker push timestamp for all images.""" images = _images(".docker_push") @@ -230,21 +242,6 @@ def docker_file_age(image): return result -def version_not_changed(): - """Check that openproblems has not changed version since last build.""" - try: - with open(VERSION_FILE, "r") as handle: - build_version = handle.read().strip() - except FileNotFoundError: - return False - build_version = packaging.version.parse(build_version) - curr_version = packaging.version.parse(openproblems.__version__) - if curr_version > build_version: - return False - else: - return True - - def format_timestamp(ts): """Format a unix timestamp as a string.""" return datetime.datetime.fromtimestamp(ts).isoformat() @@ -320,17 +317,11 @@ def docker_image_marker(image, refresh=True): if "docker_build" in sys.argv: # building everything from scratch - requirement_file = docker_build - elif not version_not_changed(): - print( - "Code version changed: {}".format(openproblems.__version__), file=sys.stderr - ) - # codebase has changed, let's rebuild - print("{}: rebuilding".format(image), file=sys.stderr) + print("{}: rebuild requested".format(image), file=sys.stderr) requirement_file = docker_build elif docker_imagespec_changed(image, dockerfile): # image has changed, let's rebuild - print("{}: rebuilding".format(image), file=sys.stderr) + print("{}: image changed; rebuilding".format(image), file=sys.stderr) requirement_file = docker_build elif docker_image_exists(image, local=True) or docker_image_exists( image, local=False @@ -340,8 +331,9 @@ def docker_image_marker(image, refresh=True): requirement_file = no_change else: # image doesn't exist anywhere, need to build it - print("{}: building".format(image), file=sys.stderr) + print("{}: doesn't exist; building".format(image), file=sys.stderr) requirement_file = docker_build + print(f"Fetched update requirements: {image}", file=sys.stderr) sys.stderr.flush() return requirement_file diff --git a/workflow/workflow_utils.py b/workflow/workflow_utils.py new file mode 100644 index 0000000000..b0e7d0cb47 --- /dev/null +++ b/workflow/workflow_utils.py @@ -0,0 +1,13 @@ +TASK_MIN_DATASETS = 1 +TASK_MIN_METHODS = 3 +TASK_MIN_METRICS = 1 + + +def task_is_incomplete(task): + if len(task.DATASETS) < TASK_MIN_DATASETS: + return True + if len(task.METHODS) < TASK_MIN_METHODS: + return True + if len(task.METRICS) < TASK_MIN_METRICS: + return True + return False