diff --git a/R/bootstrapCompartments.R b/R/bootstrapCompartments.R
index b99e2e6..16d9c0f 100644
--- a/R/bootstrapCompartments.R
+++ b/R/bootstrapCompartments.R
@@ -22,94 +22,93 @@
 #' @import SummarizedExperiment
 #'
 #' @examples
-#'   
+#'
 #' # this needs a good example
-#' 
+#'
 #' @export
-bootstrapCompartments <- function(obj, original.obj, bootstrap.samples = 1000,
-                                  chr = "chr14", assay = c("rna", "atac", "array"),
-                                  parallel = TRUE, cores = 2, targets = NULL, res = 1e6,
-                                  genome = c("hg19", "hg38", "mm9", "mm10"), q = 0.95,
-                                  svd = NULL, group = FALSE, bootstrap.means = NULL) {
-  #function for nonparametric bootstrap of compartments and compute 95% CIs
-  #check input
-  #match the assay args
+bootstrapCompartments <- function(
+  obj,
+  original.obj,
+  bootstrap.samples = 1000,
+  chr = "chr14",
+  assay = c("rna", "atac", "array"),
+  parallel = TRUE,
+  cores = 2,
+  targets = NULL,
+  res = 1e6,
+  genome = c("hg19", "hg38", "mm9", "mm10"),
+  q = 0.95,
+  svd = NULL,
+  group = FALSE,
+  bootstrap.means = NULL
+) {
+  # function for nonparametric bootstrap of compartments and compute 95% CIs
+  # check input
+  # match the assay args
   assay <- match.arg(assay)
 
-  #double check the obj class is compatible
+  # double check the obj class is compatible
   if (!checkAssayType(original.obj)) stop("Input needs to be a SummarizedExperiment")
-  
-  #check the names of the assays
+
+  # check the names of the assays
   if (!any(getAssayNames(original.obj) %in% c("counts", "Beta"))) {
     message("The assay slot should contain 'counts' for atac/rna.")
     stop("The assay slot should contain 'Beta' for methylation arrays.")
   }
-  
-  #if we are using targeted means
-  if (!is.null(targets)) original.obj <- original.obj[,targets]
-  
-  #get the global means we are going to use
-  #this could theoretically break if you ask for more bootstraps here than were pre-computed...
-  #let's check for one more optimization
+
+  # if we are using targeted means
+  if (!is.null(targets)) original.obj <- original.obj[, targets]
+
+  # get the global means we are going to use
+  # this could theoretically break if you ask for more bootstraps here than were pre-computed...
+  # let's check for one more optimization
   if (bootstrap.samples == ncol(bootstrap.means)) {
     bmeans <- bootstrap.means
   } else {
     bmeans <- sample.int(bootstrap.means, size = bootstrap.samples, replace = FALSE)
     colnames(bmeans) <- rep("globalMean", ncol(bmeans))
-    }
-  
-  #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)
-      if (group) cor.bins <- getCorMatrix(s.bins, squeeze = FALSE)
-      if (isFALSE(group)) cor.bins <- getCorMatrix(s.bins, squeeze = TRUE)
-      #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 (ncol(original.obj) < 6) stop("We need more than 5 samples to bootstrap with for the results to be meaningful.")
+  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 = TRUE)
-      #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...")
   }
 
-  #summarize the bootstraps and compute confidence intervals
-  resamp.compartments <- summarizeBootstraps(resamp.compartments, svd,
-                                             q = q, assay = assay)
+  # 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 = ifelse(parallel, cores, 1))
+
+  # summarize the bootstraps and compute confidence intervals
+  resamp.compartments <- summarizeBootstraps(resamp.compartments, svd, q = q, assay = assay)
   return(resamp.compartments)
 }
 
-#helper function to re-sample
-#this was inspired by https://github.com/sgibb/bootstrap/blob/master/R/helper-functions.R
-.resampleMatrix <- function(x, size=ncol(x)) {
-  samp.to.select <- sample.int(ncol(x), size=size, replace=TRUE)
+# helper function to re-sample
+# this was inspired by https://github.com/sgibb/bootstrap/blob/master/R/helper-functions.R
+.resampleMatrix <- function(x, size = ncol(x)) {
+  samp.to.select <- sample.int(ncol(x), size = size, replace = TRUE)
   return(x[, samp.to.select])
 }