--- a/scripts/cluster.R	Wed Jan 29 17:17:19 2020 -0500
+++ b/scripts/cluster.R	Thu Apr 15 18:59:31 2021 +0000
@@ -1,91 +1,77 @@
 #!/usr/bin/env R
-VERSION = "0.5"
+VERSION <- "0.5" # nolint
 
-args = commandArgs(trailingOnly = T)
+args <- commandArgs(trailingOnly = T)
 
-if (length(args) != 1){
+if (length(args) != 1) {
      message(paste("VERSION:", VERSION))
      stop("Please provide the config file")
 }
 
 suppressWarnings(suppressPackageStartupMessages(require(RaceID)))
-suppressWarnings(suppressPackageStartupMessages(require(scran)))
+## suppressWarnings(suppressPackageStartupMessages(require(scran)))  # nolint
 source(args[1])
 
 
-do.filter <- function(sc){
-    if (!is.null(filt.lbatch.regexes)){
+do.filter <- function(sc) { # nolint
+    if (!is.null(filt.lbatch.regexes)) {
         lar <- filt.lbatch.regexes
         nn <- colnames(sc@expdata)
-        filt$LBatch <- lapply(1:length(lar), function(m){ return( nn[grep(lar[[m]], nn)] ) })
+        filt$LBatch <- lapply(1:length(lar), function(m) {  # nolint
+            return(nn[grep(lar[[m]], nn)])})
     }
 
     sc <- do.call(filterdata, c(sc, filt))
 
     ## Get histogram metrics for library size and number of features
-    raw.lib <- log10(colSums(as.matrix(sc@expdata)))
-    raw.feat <- log10(colSums(as.matrix(sc@expdata)>0))
-    filt.lib <- log10(colSums(getfdata(sc)))
-    filt.feat <- log10(colSums(getfdata(sc)>0))
+    raw_lib <- log10(colSums(as.matrix(sc@expdata)))
+    raw_feat <- log10(colSums(as.matrix(sc@expdata) > 0))
+    filt_lib <- log10(colSums(as.matrix(getfdata(sc))))
+    filt_feat <- log10(colSums(as.matrix(getfdata(sc) > 0)))
 
-    if (filt.geqone){
-        filt.feat <- log10(colSums(getfdata(sc)>=1))
+    if (filt.geqone) {
+        filt_feat <- log10(colSums(as.matrix(getfdata(sc) >= 1))) # nolint
     }
 
     br <- 50
-    ## Determine limits on plots based on the unfiltered data
-    ## (doesn't work, R rejects limits and norm data is too different to compare to exp data
-    ##  so let them keep their own ranges)
-
-    ## betterrange <- function(floatval){
-    ##     return(10 * (floor(floatval / 10) + 1))
-    ## }
-
-    ## tmp.lib <- hist(raw.lib, breaks=br, plot=F)
-    ## tmp.feat <- hist(raw.feat, breaks=br, plot=F)
-
-    ## lib.y_lim <- c(0,betterrange(max(tmp.lib$counts)))
-    ## lib.x_lim <- c(0,betterrange(max(tmp.lib$breaks)))
-
-    ## feat.y_lim <- c(0,betterrange(max(tmp.feat$counts)))
-    ## feat.x_lim <- c(0,betterrange(max(tmp.feat$breaks)))
-
-    par(mfrow=c(2,2))
-    print(hist(raw.lib, breaks=br, main="RawData Log10 LibSize")) # , xlim=lib.x_lim, ylim=lib.y_lim)
-    print(hist(raw.feat, breaks=br, main="RawData Log10 NumFeat")) #, xlim=feat.x_lim, ylim=feat.y_lim)
-    print(hist(filt.lib, breaks=br, main="FiltData Log10 LibSize")) # , xlim=lib.x_lim, ylim=lib.y_lim)
-    tmp <- hist(filt.feat, breaks=br, main="FiltData Log10 NumFeat") # , xlim=feat.x_lim, ylim=feat.y_lim)
+    par(mfrow = c(2, 2))
+    print(hist(raw_lib, breaks = br, main = "RawData Log10 LibSize"))
+    print(hist(raw_feat, breaks = br, main = "RawData Log10 NumFeat"))
+    print(hist(filt_lib, breaks = br, main = "FiltData Log10 LibSize"))
+    tmp <- hist(filt_feat, breaks = br, main = "FiltData Log10 NumFeat")
     print(tmp)
     ## required, for extracting midpoint
-    unq <- unique(filt.feat)
-    if (length(unq) == 1){
-        abline(v=unq, col="red", lw=2)
-        text(tmp$mids, table(filt.feat)[[1]] - 100, pos=1, paste(10^unq, "\nFeatures\nin remaining\nCells", sep=""), cex=0.8)
+    unq <- unique(filt_feat)
+    if (length(unq) == 1) {
+        abline(v = unq, col = "red", lw = 2)
+        text(tmp$mids, table(filt_feat)[[1]] - 100, pos = 1,
+             paste(10^unq, "\nFeatures\nin remaining\nCells",
+                   sep = ""), cex = 0.8)
     }
 
-    if (filt.use.ccorrect){
-        par(mfrow=c(2,2))
+    if (filt.use.ccorrect) {
+        par(mfrow = c(2, 2))
         sc <- do.call(CCcorrect, c(sc, filt.ccc))
-        print(plotdimsat(sc, change=T))
-        print(plotdimsat(sc, change=F))
+        print(plotdimsat(sc, change = T))
+        print(plotdimsat(sc, change = F))
     }
     return(sc)
 }
 
-do.cluster <- function(sc){
+do.cluster <- function(sc) { # nolint
     sc <- do.call(compdist, c(sc, clust.compdist))
     sc <- do.call(clustexp, c(sc, clust.clustexp))
-    if (clust.clustexp$sat){
-        print(plotsaturation(sc, disp=F))
-        print(plotsaturation(sc, disp=T))
+    if (clust.clustexp$sat) {
+        print(plotsaturation(sc, disp = F))
+        print(plotsaturation(sc, disp = T))
     }
     print(plotjaccard(sc))
     return(sc)
 }
 
-do.outlier <- function(sc){
+do.outlier <- function(sc) { # nolint
     sc <- do.call(findoutliers, c(sc, outlier.findoutliers))
-    if (outlier.use.randomforest){
+    if (outlier.use.randomforest) {
         sc <- do.call(rfcorrect, c(sc, outlier.rfcorrect))
     }
     print(plotbackground(sc))
@@ -93,54 +79,57 @@
     print(plotoutlierprobs(sc))
     ## Heatmaps
     test1 <- list()
-    test1$side = 3
-    test1$line = 0  #1 #3
+    test1$side <- 3
+    test1$line <- 0  #1 #3
 
-    x <- clustheatmap(sc, final=FALSE)
-    print(do.call(mtext, c(paste("(Initial)"), test1)))  ## spacing is a hack
-    x <- clustheatmap(sc, final=TRUE)
-    print(do.call(mtext, c(paste("(Final)"), test1)))  ## spacing is a hack
+    x <- clustheatmap(sc, final = FALSE)
+    print(do.call(mtext, c(paste("(Initial)"), test1)))
+    x <- clustheatmap(sc, final = TRUE)
+    print(do.call(mtext, c(paste("(Final)"), test1)))
     return(sc)
 }
 
-do.clustmap <- function(sc){
+do.clustmap <- function(sc) { # nolint
     sc <- do.call(comptsne, c(sc, cluster.comptsne))
     sc <- do.call(compfr, c(sc, cluster.compfr))
+    sc <- do.call(compumap, c(sc, cluster.compumap))
     return(sc)
 }
 
 
-mkgenelist <- function(sc){
+mkgenelist <- function(sc) {
     ## Layout
     test <- list()
-    test$side = 3
-    test$line = 0  #1 #3
-    test$cex = 0.8
+    test$side <- 4
+    test$line <- -2
+    test$cex <- 0.8
 
     df <- c()
     options(cex = 1)
-    lapply(unique(sc@cpart), function(n){
-        dg <- clustdiffgenes(sc, cl=n, pvalue=genelist.pvalue)
+    plot.new()
+    lapply(unique(sc@cpart), function(n) {
+        dg <- clustdiffgenes(sc, cl = n, pvalue = genelist.pvalue)$dg
 
-        dg.goi <- dg[dg$fc > genelist.foldchange,]
-        dg.goi.table <- head(dg.goi, genelist.tablelim)
-        df <<- rbind(df, cbind(n, dg.goi.table))
+        dg_goi <- dg[dg$fc > genelist.foldchange, ]
+        dg_goi_table <- head(dg_goi, genelist.tablelim)
+        df <<- rbind(df, cbind(n, dg_goi_table))
 
-        goi <- head(rownames(dg.goi.table), genelist.plotlim)
+        goi <- head(rownames(dg_goi_table), genelist.plotlim)
+
         print(plotmarkergenes(sc, goi))
-        buffer <- paste(rep("", 36), collapse=" ")
-        print(do.call(mtext, c(paste(buffer, "Cluster ",n), test)))  ## spacing is a hack
-        test$line=-1
-        print(do.call(mtext, c(paste(buffer, "Sig. Genes"), test)))  ## spacing is a hack
-        test$line=-2
-        print(do.call(mtext, c(paste(buffer, "(fc > ", genelist.foldchange,")"), test)))  ## spacing is a hack
-
+        buffer <- paste(rep("", 36), collapse = " ")
+        print(do.call(mtext, c(paste(buffer, "Cluster ", n), test)))
+        test$line <- -1
+        print(do.call(mtext, c(paste(buffer, "Sig. Genes"), test)))
+        test$line <- 0
+        print(do.call(mtext, c(paste(buffer, "(fc > ",
+                                     genelist.foldchange, ")"), test)))
     })
-    write.table(df, file=out.genelist, sep="\t", quote=F)
+    write.table(df, file = out.genelist, sep = "\t", quote = F)
 }
 
 
-writecellassignments <- function(sc){
+writecellassignments <- function(sc) {
     dat <- sc@cluster$kpart
     tab <- data.frame(row.names = NULL,
                       cells = names(dat),
@@ -148,30 +137,38 @@
                       cluster.final = sc@cpart,
                       is.outlier = names(dat) %in% sc@out$out)
 
-    write.table(tab, file=out.assignments, sep="\t", quote=F, row.names = F)
+    write.table(tab, file = out.assignments, sep = "\t",
+                quote = F, row.names = F)
 }
 
 
 pdf(out.pdf)
 
-if (use.filtnormconf){
+if (use.filtnormconf) {
     sc <- do.filter(sc)
-    message(paste(" - Source:: genes:",nrow(sc@expdata),", cells:",ncol(sc@expdata)))
-    message(paste(" - Filter:: genes:",nrow(getfdata(sc)),", cells:",ncol(getfdata(sc))))
+    message(paste(" - Source:: genes:", nrow(sc@expdata),
+                  ", cells:", ncol(sc@expdata)))
+    message(paste(" - Filter:: genes:", nrow(as.matrix(getfdata(sc))),
+                  ", cells:", ncol(as.matrix(getfdata(sc)))))
     message(paste("         :: ",
-                  sprintf("%.1f", 100 * nrow(getfdata(sc))/nrow(sc@expdata)), "% of genes remain,",
-                  sprintf("%.1f", 100 * ncol(getfdata(sc))/ncol(sc@expdata)), "% of cells remain"))
-    write.table(as.matrix(sc@ndata), file=out.table, col.names=NA, row.names=T, sep="\t", quote=F)
+                  sprintf("%.1f", 100 * nrow(as.matrix(
+                                            getfdata(sc))) / nrow(sc@expdata)),
+                  "% of genes remain,",
+                  sprintf("%.1f", 100 * ncol(as.matrix(
+                                            getfdata(sc))) / ncol(sc@expdata)),
+                  "% of cells remain"))
+    write.table(as.matrix(sc@ndata), file = out.table, col.names = NA,
+                row.names = T, sep = "\t", quote = F)
 }
 
-if (use.cluster){
-    par(mfrow=c(2,2))
+if (use.cluster) {
+    par(mfrow = c(2, 2))
     sc <- do.cluster(sc)
 
-    par(mfrow=c(2,2))
+    par(mfrow = c(2, 2))
     sc <- do.outlier(sc)
 
-    par(mfrow=c(2,2), mar=c(1,1,6,1))
+    par(mfrow = c(2, 2), mar = c(1, 1, 6, 1))
     sc <- do.clustmap(sc)
 
     mkgenelist(sc)