Re-try PR

.github/workflows/ci.yml

@@ -46,39 +46,6 @@ jobs:
       - name: Check unit test code coverage
         run: pixi run --environment ${{ matrix.environment }} --manifest-path /tmp/pixi/pixi.toml codecov
 
-  ci_osx-64:
-    name: osx-64 CI
-    runs-on: macos-13
-    strategy:
-      fail-fast: false
-      matrix:
-        environment: ["r43", "r44"]
-
-    steps:
-      - name: Checkout pull request branch
-        uses: actions/checkout@v5
-        with:
-          fetch-depth: 0
-
-      - name: Create TOML from recipe
-        run: |
-          .github/workflows/create_toml_from_yaml.sh ${GITHUB_WORKSPACE}
-          mkdir /tmp/pixi
-          mv ${GITHUB_WORKSPACE}/pixi.toml /tmp/pixi
-
-      - name: Setup pixi
-        uses: prefix-dev/setup-pixi@v0.9.2
-        with:
-          manifest-path: /tmp/pixi/pixi.toml
-
-      - name: Run unit tests
-        run: pixi run --environment ${{ matrix.environment }} --manifest-path /tmp/pixi/pixi.toml devtools_test
-
-      - name: Run R CMD CHECK
-        run: |
-          pixi run --environment ${{ matrix.environment }} --manifest-path /tmp/pixi/pixi.toml build
-          pixi run --environment ${{ matrix.environment }} --manifest-path /tmp/pixi/pixi.toml rcmdcheck
-
   ci_osx-arm64:
     name: osx-arm64 CI
     runs-on: macos-14

R/colocboost.R

@@ -247,7 +247,17 @@ colocboost <- function(X = NULL, Y = NULL, # individual data
         return(xx)
       })
     }
-
+
+    # Remove duplicates and report: if duplicate columns of X
+    X <- lapply(seq_along(X), function(i) {
+      df <- X[[i]]
+      if (anyDuplicated(colnames(df))) {
+        message(paste("Removed duplicate columns from X matrix ", i))
+        df[, !duplicated(colnames(df)), drop = FALSE]
+      } else {
+        df
+      }
+    })
     keep_variable_individual <- lapply(X, colnames)
     if (!is.list(X) & !is.list(Y)) {
       warning("Error: Input X and Y must be the list containing genotype matrics and all phenotype vectors!")
@@ -333,11 +343,19 @@ colocboost <- function(X = NULL, Y = NULL, # individual data
         warning("Error: effect_est and effect_se should be the same dimension! Please check!")
         return(NULL)
       }
-      variables <- rownames(effect_est)
       effect_est <- as.matrix(effect_est)
       effect_se <- as.matrix(effect_se)
+      variables <- rownames(effect_est)
       if (is.null(variables)) {
         variables <- paste0("variant_", 1:nrow(effect_est))
+      } else {
+        # add - if duplciates occurs, only keep one
+        if (anyDuplicated(variables)) {
+          keep_idx <- !duplicated(variables)
+          variables <- variables[keep_idx]
+          effect_est <- effect_est[keep_idx, , drop = FALSE]
+          effect_se <- effect_se[keep_idx, , drop = FALSE]
+        }
       }
       sumstat <- list()
       for (sum_iy in 1:ncol(effect_est)) {
@@ -388,6 +406,17 @@ colocboost <- function(X = NULL, Y = NULL, # individual data
         })
       }
     }
+
+    # Remove duplicates and report: if duplicate variant in summary statistics
+    sumstat <- lapply(seq_along(sumstat), function(i) {
+      xx <- sumstat[[i]]
+      if (anyDuplicated(xx$variant)) {
+        message(paste("Removed duplicate variants from sumstat", i))
+        xx[!duplicated(xx$variant), , drop = FALSE]
+      } else {
+        xx
+      }
+    })
     keep_variable_sumstat <- lapply(sumstat, function(xx) {
       xx$variant
     })

R/colocboost_assemble.R

@@ -58,7 +58,7 @@ colocboost_assemble <- function(cb_obj,
     )
     # - save model and all coloc and single information for diagnostic
     if (output_level != 1) {
-      tmp <- get_full_output(cb_obj = cb_obj, past_out = NULL, variables = NULL)
+      tmp <- get_full_output(cb_obj = cb_obj, past_out = NULL, variables = data_info$variables)
       if (output_level == 2) {
         cb_output <- c(cb_output, list("ucos_details" = tmp$ucos_detials))
         cb_output <- cb_output[c("cos_summary", "vcp", "cos_details", "data_info", "model_info", "ucos_details")]
@@ -79,6 +79,7 @@ colocboost_assemble <- function(cb_obj,
     } else if (cb_obj$cb_model_para$model_used == "one_causal"){
       # fixme later
       check_null_max <- check_null_max * check_null
+      check_null_max_ucos <- check_null_max_ucos * check_null
     }
     cb_obj <- get_max_profile(cb_obj, check_null_max = check_null_max, 
                               check_null_max_ucos = check_null_max_ucos, 
@@ -209,6 +210,9 @@ colocboost_assemble <- function(cb_obj,
     # - colocalization results
     cb_obj$cb_model_para$weight_fudge_factor <- weight_fudge_factor
     cb_obj$cb_model_para$coverage <- coverage
+    cb_obj$cb_model_para$min_abs_corr <- min_abs_corr
+    cb_obj$cb_model_para$median_abs_corr <- median_abs_corr
+    cb_obj$cb_model_para$n_purity <- n_purity
     cos_results <- get_cos_details(cb_obj, coloc_out = past_out$cos$cos, data_info = data_info)
     cb_output <- list(
       "vcp" = cos_results$vcp,

R/colocboost_check_update_jk.R

@@ -66,15 +66,15 @@ boost_check_update_jk_nofocal <- function(cb_model, cb_model_para, cb_data) {
 
     # -- define jk and jk_each
     cor_vals_each <- do.call(cbind, lapply(model_update, function(cc) cc$correlation))
-    abs_cor_vals_each <- sweep(abs(cor_vals_each), 2, adj_dep, `*`)
+    # abs_cor_vals_each <- sweep(abs(cor_vals_each), 2, adj_dep, `*`)
+    abs_cor_vals_each <- abs(cor_vals_each) * rep(adj_dep, each = nrow(cor_vals_each))
     abs_cor_vals <- rowSums(abs_cor_vals_each)
 
-    jk <- which(abs_cor_vals == max(abs_cor_vals))
-    jk <- ifelse(length(jk) == 1, jk, sample(jk, 1))
-    jk_each <- apply(abs_cor_vals_each, 2, function(temp) {
-      jk_temp <- which(temp == max(temp))
-      return(ifelse(length(jk_temp) == 1, jk_temp, sample(jk_temp, 1)))
-    })
+    # jk <- which(abs_cor_vals == max(abs_cor_vals))
+    # jk <- ifelse(length(jk) == 1, jk, sample(jk, 1))
+    # jk_each <- apply(abs_cor_vals_each, 2, which.max)
+    jk <- which.max(abs_cor_vals)
+    jk_each <- sapply(1:ncol(abs_cor_vals_each), function(j) which.max(abs_cor_vals_each[, j]))
     update_jk[c(1, pos.update + 1)] <- c(jk, jk_each)
 
 
@@ -142,7 +142,7 @@ boost_check_update_jk_nofocal <- function(cb_model, cb_model_para, cb_data) {
         update_status[pos.update[true_update]] <- 1
         # - re-define jk_star
         abs_cor_vals_redefine <- rowSums(abs_cor_vals_each[, true_update, drop = FALSE])
-        jk_redefine <- which(abs_cor_vals_redefine == max(abs_cor_vals_redefine))
+        jk_redefine <- which.max(abs_cor_vals_redefine == max(abs_cor_vals_redefine))
         jk_redefine <- ifelse(length(jk_redefine) == 1, jk_redefine, sample(jk_redefine, 1))
         real_update_jk[pos.update[true_update]] <- jk_redefine
       } else {
@@ -351,11 +351,10 @@ boost_check_update_jk_focal <- function(cb_model, cb_model_para, cb_data,
 
     # -- define jk and jk_each
     cor_vals_each <- do.call(cbind, lapply(model_update, function(cc) as.vector(cc$correlation)))
-    abs_cor_vals_each <- sweep(abs(cor_vals_each), 2, adj_dep, `*`)
-    jk_each <- apply(abs_cor_vals_each, 2, function(temp) {
-      jk_temp <- which(temp == max(temp))
-      return(ifelse(length(jk_temp) == 1, jk_temp, sample(jk_temp, 1)))
-    })
+    # abs_cor_vals_each <- sweep(abs(cor_vals_each), 2, adj_dep, `*`)
+    abs_cor_vals_each <- abs(cor_vals_each) * rep(adj_dep, each = nrow(cor_vals_each))
+    # jk_each <- apply(abs_cor_vals_each, 2, which.max)
+    jk_each <- sapply(1:ncol(abs_cor_vals_each), function(j) which.max(abs_cor_vals_each[, j]))
     pp_focal <- which(pos.update == focal_outcome_idx)
     jk_focal <- jk_each[pp_focal]
 
@@ -503,8 +502,8 @@ get_LD_jk1_jk2 <- function(jk1, jk2,
     if (length(XtX) == 1){
       LD_temp <- 0
     } else {
-      jk1.remain <- which(remain_jk == jk1)
-      jk2.remain <- which(remain_jk == jk2)
+      jk1.remain <- match(jk1, remain_jk)
+      jk2.remain <- match(jk2, remain_jk)
       LD_temp <- XtX[jk1.remain, jk2.remain]
     }
   }
@@ -545,29 +544,59 @@ check_pair_jkeach <- function(jk_each,
                               cb_data, X_dict,
                               jk_equiv_corr = 0.8,
                               jk_equiv_loglik = 0.001) {
+
+
+  #' @importFrom stats cor
+  get_LD_jk_each <- function(jk_each,
+                             X = NULL, XtX = NULL, N = NULL,
+                             remain_jk = NULL) {
+    if (!is.null(X)) {
+      LD_temp <- suppressWarnings({
+        get_cormat(X[, jk_each])
+      })
+      LD_temp[which(is.na(LD_temp))] <- 0
+      # LD_temp <- LD_temp[1, 2]
+    } else if (!is.null(XtX)) {
+      if (length(XtX) == 1){
+        LD_temp <- matrix(0, length(jk_each), length(jk_each))
+      } else {
+        jk.remain <- match(jk_each, remain_jk)
+        LD_temp <- XtX[jk.remain, jk.remain]
+        LD_temp[which(is.na(LD_temp))] <- 0
+      }
+    }
+    return(LD_temp)
+  }
+
+  detect_func <- function(idx, LD_all, jk_i, jk_j, i, j){
+    change_log_jk_i <- model_update[[idx]]$change_loglike[jk_i]
+    change_log_jk_j <- model_update[[idx]]$change_loglike[jk_j]
+    change_each <- abs(change_log_jk_i - change_log_jk_j)
+    LD_temp <- LD_all[[idx]][i, j]
+    return((change_each <= jk_equiv_loglik) & (abs(LD_temp) >= jk_equiv_corr))
+  }
+
   data_update <- cb_data$data[pos.update]
+  LD_all <- lapply(1:length(jk_each), function(idx){
+    get_LD_jk_each(jk_each,
+                   X = cb_data$data[[X_dict[idx]]]$X,
+                   XtX = cb_data$data[[X_dict[idx]]]$XtX,
+                   N = data_update[[idx]]$N,
+                   remain_jk = setdiff(1:length(model_update[[idx]]$res), data_update[[idx]]$variable_miss)
+    )
+  })
+
   # -- check if jk_i ~ jk_j
   change_each_pair <- matrix(FALSE, nrow = length(jk_each), ncol = length(jk_each))
   for (i in 1:length(jk_each)) {
-
     jk_i <- jk_each[i]
-    change_log_jk_i <- model_update[[i]]$change_loglike[jk_i]
-    for (j in 1:length(jk_each)) {
+    for (j in i:length(jk_each)) {
       if (j != i) {
         jk_j <- jk_each[j]
-
-        if (!(jk_i %in% data_update[[i]]$variable_miss) & !(jk_j %in% data_update[[i]]$variable_miss)) {
-          change_log_jk_j <- model_update[[i]]$change_loglike[jk_j]
-          change_each <- abs(change_log_jk_i - change_log_jk_j)
-          LD_temp <- get_LD_jk1_jk2(jk_i, jk_j,
-            X = cb_data$data[[X_dict[i]]]$X,
-            XtX = cb_data$data[[X_dict[i]]]$XtX,
-            N = data_update[[i]]$N,
-            remain_jk = setdiff(1:length(model_update[[i]]$res), data_update[[i]]$variable_miss)
-          )
-          change_each_pair[i, j] <- (change_each <= jk_equiv_loglik) & (abs(LD_temp) >= jk_equiv_corr)
-        } else {
-          change_each_pair[i, j] <- FALSE
+        change_each_pair[i, j] <- detect_func(idx = i, LD_all, jk_i, jk_j, i, j)
+        # if jk_i and jk_j are equivalent on dataset i, then we don't need to check dataset j
+        if ( !change_each_pair[i, j] ){
+          change_each_pair[j, i] <- detect_func(idx = j, LD_all, jk_i, jk_j, i, j)
         }
       } else {
         change_each_pair[i, j] <- FALSE

R/colocboost_inference.R

@@ -132,7 +132,8 @@ get_modularity <- function(Weight, B) {
 
   if (m == 0) return(0)
 
-  cate <- B %*% t(B)
+  # cate <- B %*% t(B)
+  cate <- tcrossprod(B)
 
   if (m_pos == 0 & m_neg == 0) return(0)
 
@@ -190,12 +191,12 @@ get_n_cluster <- function(hc, Sigma, m = ncol(Sigma), min_cluster_corr = 0.8) {
 #' @noRd
 w_purity <- function(weights, X = NULL, Xcorr = NULL, N = NULL, n = 100, coverage = 0.95,
                      min_abs_corr = 0.5, median_abs_corr = NULL, miss_idx = NULL) {
-  tmp <- apply(weights, 2, w_cs, coverage = coverage)
-  tmp_purity <- apply(tmp, 2, function(tt) {
-    pos <- which(tt == 1)
+
+  tmp_purity <- apply(weights, 2, function(w) {
+    pos <- w_cs(w, coverage = coverage)
     # deal with missing snp here
     if (!is.null(Xcorr)) {
-      pos <- match(pos, setdiff(1:length(tmp), miss_idx))
+      pos <- match(pos, setdiff(1:length(w), miss_idx))
     }
     get_purity(pos, X = X, Xcorr = Xcorr, N = N, n = n)
   })
@@ -373,7 +374,12 @@ check_null_post <- function(cb_obj,
   }
   if (!weaker_effect) {
     check_cs_change <- cs_change
-    check_null_tmp <- sapply(1:cb_obj$cb_model_para$L, function(j) cb_obj$cb_model[[j]]$check_null_max)
+    if (cb_obj$cb_model_para$L == 1){
+      check_null_max_tmp <- cb_obj$cb_model[[j]]$check_null_max_ucos
+    } else {
+      check_null_max_tmp <- cb_obj$cb_model[[j]]$check_null_max
+    }
+    check_null_tmp <- sapply(1:cb_obj$cb_model_para$L, function(j) check_null_max_tmp)
   } else {
     check_null_tmp <- rep(check_null, cb_obj$cb_model_para$L)
   }
@@ -420,7 +426,7 @@ get_purity <- function(pos, X = NULL, Xcorr = NULL, N = NULL, n = 100) {
       corr[which(is.na(corr))] <- 0
       value <- abs(get_upper_tri(corr))
     } else {
-      if (sum(Xcorr) == 1){
+      if (length(Xcorr) == 1){
         value <- 0
       } else {
         Xcorr <- Xcorr # if (!is.null(N)) Xcorr/(N-1) else Xcorr

R/colocboost_init.R

@@ -163,7 +163,8 @@ colocboost_init_model <- function(cb_data,
     tmp <- list(
       "res" = NULL,
       "beta" = rep(0, P),
-      "weights_path" = c(),
+      # "weights_path" = c(),
+      "weights_path" = list(),
       "profile_loglike_each" = NULL,
       "obj_path" = 999999,
       "obj_single" = 999999,
@@ -619,7 +620,7 @@ process_sumstat <- function(Z, N, Var_y, SeBhat, ld_matrices, variant_lists, dic
     Z_extend[pos_target] <- current_z[pos_z]
 
     # Calculate submatrix for each unique entry (not duplicates)
-    if (sum(current_ld_matrix) == 1){
+    if (length(current_ld_matrix) == 1){
       ld_submatrix <- current_ld_matrix
     } else {
       ld_submatrix <- NULL

R/colocboost_one_causal.R

@@ -79,9 +79,8 @@ colocboost_one_iteration <- function(cb_model, cb_model_para, cb_data) {
   cb_model_para[rm_elements] <- NULL
   for (i in 1:length(cb_model)) {
     cb_model[[i]]$obj_path <- as.numeric(unlist(cb_model[[i]]$obj_path[-1]))
-  }
-  for (i in 1:length(cb_model)) {
     cb_model[[i]]$obj_single <- as.numeric(unlist(cb_model[[i]]$obj_single[-1]))
+    cb_model[[i]]$weights_path <- do.call(rbind, cb_model[[i]]$weights_path)
   }
 
   cb_obj <- list("cb_data" = cb_data, "cb_model" = cb_model, "cb_model_para" = cb_model_para)
@@ -238,7 +237,7 @@ colocboost_diagLD <- function(cb_model, cb_model_para, cb_data) {
         cb_model_para, 
         cb_data
       )
-      weights <- rbind(weights, cb_model_tmp[[iy]]$weights_path)
+      weights <- rbind(weights, unlist(cb_model_tmp[[iy]]$weights_path))
     }
     ###### overlap weights
     overlap_pair <- check_pair_overlap(weights, coverage = 0.95)
@@ -307,11 +306,10 @@ colocboost_diagLD <- function(cb_model, cb_model_para, cb_data) {
   cb_model_para[rm_elements] <- NULL
   for (i in 1:length(cb_model)) {
     cb_model[[i]]$obj_path <- as.numeric(unlist(cb_model[[i]]$obj_path[-1]))
-  }
-  for (i in 1:length(cb_model)) {
     cb_model[[i]]$obj_single <- as.numeric(unlist(cb_model[[i]]$obj_single[-1]))
+    cb_model[[i]]$weights_path <- do.call(rbind, cb_model[[i]]$weights_path)
   }
-
+  
   cb_obj <- list("cb_data" = cb_data, "cb_model" = cb_model, "cb_model_para" = cb_model_para)
   class(cb_obj) <- "colocboost"
   return(cb_obj)