Replace colsum with scrapper for consistent parallelization.

SingleR-inc · Dec 15, 2024 · 2845bcb · 2845bcb
1 parent 07121b6
commit 2845bcb
Show file tree

Hide file tree

Showing 3 changed files with 7 additions and 13 deletions.
diff --git a/NAMESPACE b/NAMESPACE
@@ -35,13 +35,9 @@ importFrom(BiocNeighbors,defineBuilder)
 importFrom(BiocParallel,SerialParam)
 importFrom(BiocParallel,bpnworkers)
 importFrom(DelayedArray,DelayedArray)
-importFrom(DelayedArray,colsum)
-importFrom(DelayedArray,getAutoBPPARAM)
 importFrom(DelayedArray,is_sparse)
 importFrom(DelayedArray,rowMaxs)
 importFrom(DelayedArray,rowMins)
-importFrom(DelayedArray,setAutoBPPARAM)
-importFrom(DelayedArray,sweep)
 importFrom(DelayedMatrixStats,rowAnyNAs)
 importFrom(DelayedMatrixStats,rowMedians)
 importFrom(Matrix,rowMeans)

diff --git a/R/SingleR.R b/R/SingleR.R
@@ -72,7 +72,7 @@
 #' @importFrom SummarizedExperiment assay
 #' @importClassesFrom SummarizedExperiment SummarizedExperiment
 #' @importFrom methods is
-#' @importFrom DelayedArray colsum DelayedArray getAutoBPPARAM setAutoBPPARAM
+#' @importFrom DelayedArray DelayedArray
 #' @importFrom BiocParallel SerialParam
 SingleR <- function(
     test, 
@@ -144,10 +144,9 @@ SingleR <- function(
     }
 
     if (!is.null(clusters)) {
-        oldp <- getAutoBPPARAM()
-        setAutoBPPARAM(BPPARAM)
-        on.exit(setAutoBPPARAM(oldp), add=TRUE)
-        test <- colsum(DelayedArray(test), clusters)
+        agg <- scrapper::aggregateAcrossCells(test, list(clusters=clusters), num.threads=num.threads)
+        test <- agg$sums
+        colnames(test) <- agg$combinations$clusters
     }
 
     classifySingleR(

diff --git a/R/aggregateReference.R b/R/aggregateReference.R
@@ -77,7 +77,7 @@
 #' @importFrom SummarizedExperiment SummarizedExperiment
 #' @importFrom S4Vectors DataFrame
 #' @importFrom Matrix rowMeans
-#' @importFrom DelayedArray sweep colsum DelayedArray
+#' @importFrom DelayedArray DelayedArray
 #' @importFrom BiocParallel SerialParam bpnworkers
 aggregateReference <- function(
     ref,
@@ -125,9 +125,8 @@ aggregateReference <- function(
             }
 
             clustered <- scrapper::clusterKmeans(pcs, k=cur.ncenters, num.threads=num.threads)
-            agg <- colsum(current, clustered$cluster)
-            tab <- table(clustered$cluster)[colnames(agg)]
-            output <- sweep(agg, 2, tab, "/")
+            agg <- scrapper::aggregateAcrossCells(current, list(cluster=clustered$cluster), num.threads=num.threads)
+            output <- sweep(agg$sums, 2, agg$counts, "/")
         }
 
         output.vals[[lab]] <- output