refactor(bootstrapCompartments): collapse parallel/serial chunks into…

… one mclapply

R/bootstrapCompartments.R

@@ -59,51 +59,35 @@ bootstrapCompartments <- function(obj, original.obj, bootstrap.samples = 1000,
   }
 
   # if (ncol(original.obj) < 6) stop("We need more than 5 samples to bootstrap with for the results to be meaningful.")
-  if (!parallel) {
-    message("Not bootstrapping in parallel will take a long time...")
-    # bootstrap and recompute compartments
-    resamp.compartments <- lapply(1:ncol(bmeans), function(b) {
-      # resample the global means with replacement
-      message("Working on bootstrap ", b)
 
-      # get the shrunken bins with new global mean
-      boot.mean <- as.matrix(bmeans[, b])
-      colnames(boot.mean) <- "globalMean"
-      s.bins <- shrinkBins(obj, original.obj,
-        prior.means = boot.mean,
-        chr = chr, res = res, assay = assay, genome = genome
-      )
-      cor.bins <- getCorMatrix(s.bins, squeeze = !group)
-      # Stupid check for perfect correlation with global mean
-      if (any(is.na(cor.bins$binmat.cor))) {
-        absig <- matrix(rep(NA, nrow(cor.bins$binmat.cor)))
-      } else {
-        absig <- getABSignal(cor.bins, assay = assay)
-      }
-      return(absig)
-    })
-  } else {
+  if (parallel) {
     message("Bootstrapping in parallel with ", cores, " cores.")
-    # bootstrap and recompute compartments
-    resamp.compartments <- mclapply(1:ncol(bmeans), function(b) {
-      # get the shrunken bins with new global mean
-      boot.mean <- as.matrix(bmeans[, b])
-      colnames(boot.mean) <- "globalMean"
-      s.bins <- shrinkBins(obj, original.obj,
-        prior.means = boot.mean,
-        chr = chr, res = res, assay = assay, genome = genome
-      )
-      cor.bins <- getCorMatrix(s.bins, squeeze = !group)
-      # Stupid check for perfect correlation with global mean
-      if (any(is.na(cor.bins$binmat.cor))) {
-        absig <- matrix(rep(NA, nrow(cor.bins$binmat.cor)))
-      } else {
-        absig <- getABSignal(cor.bins, assay = assay)
-      }
-      return(absig)
-    }, mc.cores = cores)
+  } else {
+    message("Not bootstrapping in parallel will take a long time...")
   }
 
+  # bootstrap and recompute compartments
+  resamp.compartments <- mclapply(1:ncol(bmeans), function(b) {
+    message("Working on bootstrap ", b)
+
+    # get the shrunken bins with new global mean
+    boot.mean <- as.matrix(bmeans[, b])
+    colnames(boot.mean) <- "globalMean"
+    s.bins <- shrinkBins(obj, original.obj,
+      prior.means = boot.mean,
+      chr = chr, res = res, assay = assay, genome = genome
+    )
+    cor.bins <- getCorMatrix(s.bins, squeeze = !group)
+
+    # Stupid check for perfect correlation with global mean
+    if (any(is.na(cor.bins$binmat.cor))) {
+      absig <- matrix(rep(NA, nrow(cor.bins$binmat.cor)))
+    } else {
+      absig <- getABSignal(cor.bins, assay = assay)
+    }
+    return(absig)
+  }, mc.cores = ifelse(parallel, cores, 1))
+
   # summarize the bootstraps and compute confidence intervals
   resamp.compartments <- summarizeBootstraps(resamp.compartments, svd,
     q = q, assay = assay