More fixes for reproducibility

R/runBatchCorrection.R

@@ -296,6 +296,7 @@ runFastMNN <- function(inSCE, useAssay = "logcounts", useReducedDim = NULL,
 #' make soft kmeans cluster approach hard clustering. Default \code{0.1}.
 #' @param nIter An integer. The max number of iterations to perform. Default
 #' \code{10L}.
+#' @param Set seed for reproducibility. Default is \code{12345}.
 #' @param verbose Whether to print progress messages. Default \code{TRUE}.
 #' @param ... Other arguments passed to \code{\link[harmony]{HarmonyMatrix}}.
 #' See details.
@@ -318,7 +319,7 @@ runFastMNN <- function(inSCE, useAssay = "logcounts", useReducedDim = NULL,
 runHarmony <- function(inSCE, useAssay = "logcounts", useReducedDim = NULL,
                        batch = "batch", reducedDimName = "HARMONY",
                        nComponents = 50, lambda = 0.1, theta = 5,
-                       sigma = 0.1, nIter = 10, verbose = TRUE, ...) {
+                       sigma = 0.1, nIter = 10, seed = 12345, verbose = TRUE, ...) {
   if (!requireNamespace("harmony", quietly = TRUE)) {
     stop("The Harmony package is required to run this function. ",
          "Install harmony with: ",
@@ -342,11 +343,13 @@ runHarmony <- function(inSCE, useAssay = "logcounts", useReducedDim = NULL,
     }
     mat <- mat[,seq(nComponents)]
   }
-  h <- harmony::HarmonyMatrix(data_mat = mat, meta_data = batchVec,
-                              do_pca = do_pca, npcs = nComponents,
-                              lambda = lambda, theta = theta,
-                              sigma = sigma, max.iter.harmony = nIter,
-                              verbose = verbose, return_object = FALSE, ...)
+  h <- withr::with_seed(seed, {
+    harmony::HarmonyMatrix(data_mat = mat, meta_data = batchVec,
+                           do_pca = do_pca, npcs = nComponents,
+                           lambda = lambda, theta = theta,
+                           sigma = sigma, max.iter.harmony = nIter,
+                           verbose = verbose, return_object = FALSE, ...)
+  })
   SingleCellExperiment::reducedDim(inSCE, reducedDimName) <- h
   S4Vectors::metadata(inSCE)$batchCorr[[reducedDimName]] <-
     list(useAssay = useAssay, origLogged = TRUE, method = "harmony",

R/seuratFunctions.R

@@ -733,6 +733,7 @@ plotSeuratReduction <-
 #' not. Default \code{TRUE}.
 #' @param resolution Set the resolution parameter to find larger (value above 1)
 #' or smaller (value below 1) number of communities. Default \code{0.8}.
+#' @param seed Specify the seed value. Default \code{12345}.
 #' @param externalReduction Pass DimReduc object if PCA/ICA computed through
 #' other libraries. Default \code{NULL}.
 #' @param verbose Logical value indicating if informative messages should
@@ -755,6 +756,7 @@ runSeuratFindClusters <- function(inSCE,
                                   algorithm = c("louvain", "multilevel", "SLM"),
                                   groupSingletons = TRUE,
                                   resolution = 0.8,
+                                  seed = 12345,
                                   externalReduction = NULL,
                                   verbose = TRUE) {
   algorithm <- match.arg(algorithm)
@@ -773,12 +775,14 @@ runSeuratFindClusters <- function(inSCE,
     }
   }
 
-  seuratObject <- Seurat::FindNeighbors(
-    seuratObject,
-    reduction = useReduction,
-    dims = seq(dims),
-    verbose = verbose
-  )
+  seuratObject <- withr::with_seed(seed, {
+    Seurat::FindNeighbors(
+      seuratObject,
+      reduction = useReduction,
+      dims = seq(dims),
+      verbose = verbose
+    )
+  })
 
   no_algorithm <- 1
   if (algorithm == "louvain") {
@@ -790,13 +794,15 @@ runSeuratFindClusters <- function(inSCE,
   }
   tempSeuratObject <- seuratObject
   tempSeuratObject@meta.data <- data.frame()
-  tempSeuratObject <- Seurat::FindClusters(
-    tempSeuratObject,
-    algorithm = no_algorithm,
-    group.singletons = groupSingletons,
-    resolution = resolution,
-    verbose = verbose
-  )
+  tempSeuratObject <- withr::with_seed(seed, {
+    Seurat::FindClusters(
+      tempSeuratObject,
+      algorithm = no_algorithm,
+      group.singletons = groupSingletons,
+      resolution = resolution,
+      verbose = verbose
+    )
+  })
   seuratObject@meta.data$seurat_clusters <-
     tempSeuratObject@meta.data$seurat_clusters
   inSCE <- .addSeuratToMetaDataSCE(inSCE, seuratObject)