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Accommodate the susie-ash and susie-inf into mnm_regression.ipynb #1269

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Accommodate the susie-ash and susie-inf into mnm_regression.ipynb #1269

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0872c09
add --finemap-extra-opts in univariate_analysis_pipeline
rl3328 Nov 12, 2025
d9a92b6
update the finemapping-extra-opts
rl3328 Nov 12, 2025
d42df99
Merge branch 'StatFunGen:main' into main
rl3328 Jan 7, 2026
9d71a2a
Merge branch 'StatFunGen:main' into main
rl3328 Jan 8, 2026
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4 changes: 4 additions & 0 deletions code/mnm_analysis/mnm_methods/mnm_regression.ipynb
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Expand Up @@ -555,6 +555,8 @@
"# to determine if follow up analysis will be continued or to simply return NULL\n",
"# If this is negative we use a default way to determine this cutoff which is conservative but still useful\n",
"parameter: skip_analysis_pip_cutoff = []\n",
"# Modify Finemapping parameters\n",
"parameter: finemapping_extra_opts = \"\"\n",
"# Skip fine-mapping\n",
"parameter: skip_fine_mapping = False\n",
"# Skip TWAS weights computation\n",
Expand Down Expand Up @@ -993,6 +995,7 @@
" # fine-mapping results summary\n",
" signal_cutoff = ${pip_cutoff},\n",
" coverage = c(${\",\".join([str(x) for x in coverage])}),\n",
" finemapping_extra_opts = ${finemapping_extra_opts if finemapping_extra_opts else \"list(refine = TRUE)\"},\n",
" # TWAS weights and CV for TWAS weights\n",
" twas_weights = FALSE, \n",
" max_cv_variants=${max_cv_variants},\n",
Expand Down Expand Up @@ -1029,6 +1032,7 @@
" # fine-mapping results summary\n",
" signal_cutoff = ${pip_cutoff},\n",
" coverage = c(${\",\".join([str(x) for x in coverage])}),\n",
" finemapping_extra_opts = ${finemapping_extra_opts if finemapping_extra_opts else \"list(refine = TRUE)\"},\n",
" # TWAS weights and CV for TWAS weights\n",
" twas_weights = TRUE, \n",
" max_cv_variants=${max_cv_variants},\n",
Expand Down