--- a/cpm_tpm_rpk.R	Fri Apr 12 12:01:35 2019 -0400
+++ b/cpm_tpm_rpk.R	Thu Oct 05 13:50:22 2023 +0000
@@ -1,142 +1,150 @@
 if (length(commandArgs(TRUE)) == 0) {
-  system("Rscript cpm_tpm_rpk.R -h", intern = F)
+  system("Rscript cpm_tpm_rpk.R -h", intern = FALSE)
   q("no")
 }
 
 
 # load packages that are provided in the conda env
-options( show.error.messages=F,
-       error = function () { cat( geterrmessage(), file=stderr() ); q( "no", 1, F ) } )
-loc <- Sys.setlocale("LC_MESSAGES", "en_US.UTF-8")
+options(show.error.messages = FALSE,
+  error = function() {
+    cat(geterrmessage(), file = stderr())
+    q("no", 1, FALSE)
+  }
+)
+loc <- Sys.setlocale("LC_MESSAGES", "en_US.UTF-8") # nolint⁠
 warnings()
 library(optparse)
 library(ggplot2)
 library(reshape2)
-library(Rtsne)
+library(Rtsne) # nolint⁠
 library(ggfortify)
 
 
 
 #Arguments
-option_list = list(
+option_list <- list(
   make_option(
     c("-d", "--data"),
     default = NA,
-    type = 'character',
+    type = "character",
     help = "Input file that contains count values to transform"
   ),
   make_option(
     c("-t", "--type"),
-    default = 'cpm',
-    type = 'character',
+    default = "cpm",
+    type = "character",
     help = "Transformation type, either cpm, tpm, rpk or none[default : '%default' ]"
   ),
   make_option(
     c("-s", "--sep"),
-    default = '\t',
-    type = 'character',
+    default = "\t",
+    type = "character",
     help = "File separator [default : '%default' ]"
   ),
   make_option(
     c("-c", "--colnames"),
     default = TRUE,
-    type = 'logical',
+    type = "logical",
     help = "Consider first line as header ? [default : '%default' ]"
   ),
   make_option(
     c("-f", "--gene"),
     default = NA,
-    type = 'character',
+    type = "character",
     help = "Two column of gene length file"
   ),
   make_option(
     c("-a", "--gene_sep"),
-    default = '\t',
-    type = 'character',
+    default = "\t",
+    type = "character",
     help = "Gene length file separator [default : '%default' ]"
   ),
   make_option(
     c("-b", "--gene_header"),
     default = TRUE,
-    type = 'logical',
+    type = "logical",
     help = "Consider first line of gene length as header ? [default : '%default' ]"
   ),
   make_option(
     c("-l", "--log"),
     default = FALSE,
-    type = 'logical',
+    type = "logical",
     help = "Should be log transformed as well ? (log2(data +1)) [default : '%default' ]"
   ),
   make_option(
     c("-o", "--out"),
     default = "res.tab",
-    type = 'character',
+    type = "character",
     help = "Output name [default : '%default' ]"
   ),
   make_option(
     "--visu",
     default = FALSE,
-    type = 'logical',
+    type = "logical",
     help = "performs T-SNE [default : '%default' ]"
   ),
   make_option(
     "--tsne_labels",
     default = FALSE,
-    type = 'logical',
+    type = "logical",
     help = "add labels to t-SNE plot [default : '%default' ]"
   ),
   make_option(
     "--seed",
     default = 42,
-    type = 'integer',
+    type = "integer",
     help = "Seed value for reproducibility [default : '%default' ]"
   ),
   make_option(
     "--perp",
     default = 5.0,
-    type = 'numeric',
+    type = "numeric",
     help = "perplexity [default : '%default' ]"
   ),
   make_option(
     "--theta",
     default = 1.0,
-    type = 'numeric',
+    type = "numeric",
     help = "theta [default : '%default' ]"
   ),
   make_option(
     c("-D", "--tsne_out"),
     default = "tsne.pdf",
-    type = 'character',
+    type = "character",
     help = "T-SNE pdf [default : '%default' ]"
   ),
   make_option(
     "--pca_out",
     default = "pca.pdf",
-    type = 'character',
+    type = "character",
     help = "PCA pdf [default : '%default' ]"
   )
 
 )
 
-opt = parse_args(OptionParser(option_list = option_list),
-                 args = commandArgs(trailingOnly = TRUE))
+opt <- parse_args(OptionParser(option_list = option_list),
+                  args = commandArgs(trailingOnly = TRUE))
 
-if (opt$data == "" & !(opt$help)) {
+if (opt$data == "" && !(opt$help)) {
   stop("At least one argument must be supplied (count data).\n",
        call. = FALSE)
-} else if ((opt$type == "tpm" | opt$type == "rpk") & opt$gene == "") {
+} else if ((opt$type == "tpm" || opt$type == "rpk") && opt$gene == "") {
   stop("At least two arguments must be supplied (count data and gene length file).\n",
        call. = FALSE)
-} else if (opt$type != "tpm" & opt$type != "rpk" & opt$type != "cpm" & opt$type != "none") {
+} else if (opt$type != "tpm" && opt$type != "rpk" && opt$type != "cpm" && opt$type != "none") {
   stop("Wrong transformation requested (--type option) must be : cpm, tpm or rpk.\n",
        call. = FALSE)
 }
 
-if (opt$sep == "tab") {opt$sep = "\t"}
-if (opt$gene_sep == "tab") {opt$gene_sep = "\t"}
+if (opt$sep == "tab") {
+  opt$sep <- "\t"
+}
+if (opt$gene_sep == "tab") {
+  opt$gene_sep <- "\t"
+}
 
 cpm <- function(count) {
-  t(t(count) / colSums(count)) * 1000000
+  (count / colSums(count)) * 1000000
 }
 
 
@@ -145,13 +153,15 @@
 }
 
 tpm <- function(count, length) {
-  RPK = rpk(count, length)
-  perMillion_factor = colSums(RPK) / 1000000
-  TPM = RPK / perMillion_factor
-  return(TPM)
+  rpk <- rpk(count, length)
+  per_million_factor <- colSums(rpk) / 1000000
+  tpm <- rpk / per_million_factor
+  return(tpm)
 }
 
-data = read.table(
+#### running code ####
+
+data <- read.table(
   opt$data,
   check.names = FALSE,
   header = opt$colnames,
@@ -159,94 +169,84 @@
   sep = opt$sep
 )
 
-if (opt$type == "tpm" | opt$type == "rpk") {
-  gene_length = as.data.frame(
+if (opt$type == "tpm" || opt$type == "rpk") {
+  gene_length <- as.data.frame(
     read.table(
       opt$gene,
-      h = opt$gene_header,
+      header = opt$gene_header,
       row.names = 1,
       sep = opt$gene_sep
     )
   )
-  gene_length = as.data.frame(gene_length[match(rownames(data), rownames(gene_length)), ], rownames(data))
+  gene_length <- as.data.frame(gene_length[match(rownames(data), rownames(gene_length)), ], rownames(data))
 }
 
 
 if (opt$type == "cpm")
-  res = cpm(data)
+  res <- cpm(data)
 if (opt$type == "tpm")
-  res = as.data.frame(apply(data, 2, tpm, length = gene_length), row.names = rownames(data))
+  res <- as.data.frame(apply(data, 2, tpm, length = gene_length), row.names = rownames(data))
 if (opt$type == "rpk")
-  res = as.data.frame(apply(data, 2, rpk, length = gene_length), row.names = rownames(data))
+  res <- as.data.frame(apply(data, 2, rpk, length = gene_length), row.names = rownames(data))
 if (opt$type == "none")
-  res = data
-colnames(res) = colnames(data)
-
+  res <- data
+colnames(res) <- colnames(data)
 
 if (opt$log == TRUE) {
-  res = log2(res + 1)
+  res <- log2(res + 1)
 }
 
+if (opt$visu == TRUE) {
+  df <- res
+  # filter and transpose df for tsne and pca
+  df <- df[rowSums(df) != 0, ] # remove lines without information (with only 0 counts)
+  tdf <- t(df)
+  # make tsne and plot results
+  set.seed(opt$seed) ## Sets seed for reproducibility
+  tsne_out <- Rtsne(tdf, perplexity = opt$perp, theta = opt$theta)
+  embedding <- as.data.frame(tsne_out$Y)
+  embedding$Class <- as.factor(sub("Class_", "", rownames(tdf)))
+  gg_legend <- theme(legend.position = "none")
+  ggplot(embedding, aes(x = V1, y = V2)) +
+    geom_point(size = 1, color = "red") +
+    gg_legend +
+    xlab("") +
+    ylab("") +
+    ggtitle("t-SNE") +
+    if (opt$tsne_labels == TRUE) {
+      geom_text(aes(label = Class), hjust = -0.2, vjust = -0.5, size = 2.5, color = "darkblue")
+    }
+  ggsave(file = opt$tsne_out, device = "pdf")
+  # make PCA and plot result with ggfortify (autoplot)
+  tdf_pca <- prcomp(tdf, center = TRUE, scale. = TRUE)
+  if (opt$tsne_labels == TRUE) {
+    autoplot(tdf_pca, shape = FALSE, label = TRUE, label.size = 2.5, label.vjust = 1.2,
+             label.hjust = 1.2,
+             colour = "darkblue") +
+      geom_point(size = 1, color = "red") +
+      xlab(paste("PC1", summary(tdf_pca)$importance[2, 1] * 100, "%")) +
+      ylab(paste("PC2", summary(tdf_pca)$importance[2, 2] * 100, "%")) +
+      ggtitle("PCA")
+    ggsave(file = opt$pca_out, device = "pdf")
+  } else {
+    autoplot(tdf_pca, shape = TRUE, colour = "darkblue") +
+      geom_point(size = 1, color = "red") +
+      xlab(paste("PC1", summary(tdf_pca)$importance[2, 1] * 100, "%")) +
+      ylab(paste("PC2", summary(tdf_pca)$importance[2, 2] * 100, "%")) +
+      ggtitle("PCA")
+    ggsave(file = opt$pca_out, device = "pdf")
+  }
+}
+
+# at this stage, we select numeric columns and round theirs values to 8 decimals for cleaner output
+is_num <- sapply(res, is.numeric)
+res[is_num] <- lapply(res[is_num], round, 8)
+
 write.table(
   cbind(Features = rownames(res), res),
   opt$out,
   col.names = opt$colnames,
-  row.names = F,
-  quote = F,
+  row.names = FALSE,
+  quote = FALSE,
   sep = "\t"
 )
-
-## 
-if (opt$visu == TRUE) {
-  df = res
-  # filter and transpose df for tsne and pca
-  df = df[rowSums(df) != 0,] # remove lines without information (with only 0 counts)
-  tdf = t(df)
-  # make tsne and plot results
-  set.seed(opt$seed) ## Sets seed for reproducibility
-  tsne_out <- Rtsne(tdf, perplexity=opt$perp, theta=opt$theta) # 
-  embedding <- as.data.frame(tsne_out$Y)
-  embedding$Class <- as.factor(sub("Class_", "", rownames(tdf)))
-  gg_legend = theme(legend.position="none")
-  ggplot(embedding, aes(x=V1, y=V2)) +
-    geom_point(size=1, color='red') +
-    gg_legend +
-    xlab("") +
-    ylab("") +
-    ggtitle('t-SNE') +
-    if (opt$tsne_labels == TRUE) {
-      geom_text(aes(label=Class),hjust=-0.2, vjust=-0.5, size=2.5, color='darkblue')
-    }
-  ggsave(file=opt$tsne_out, device="pdf")
-  # make PCA and plot result with ggfortify (autoplot)
-  tdf.pca <- prcomp(tdf, center = TRUE, scale. = T)
-  if (opt$tsne_labels == TRUE) {
-      autoplot(tdf.pca, shape=F, label=T, label.size=2.5, label.vjust=1.2,
-               label.hjust=1.2,
-               colour="darkblue") +
-      geom_point(size=1, color='red') +
-      xlab(paste("PC1",summary(tdf.pca)$importance[2,1]*100, "%")) +
-      ylab(paste("PC2",summary(tdf.pca)$importance[2,2]*100, "%")) +
-      ggtitle('PCA')
-      ggsave(file=opt$pca_out, device="pdf")   
-      } else {
-      autoplot(tdf.pca, shape=T, colour="darkblue") +
-      geom_point(size=1, color='red') +
-      xlab(paste("PC1",summary(tdf.pca)$importance[2,1]*100, "%")) +
-      ylab(paste("PC2",summary(tdf.pca)$importance[2,2]*100, "%")) +
-      ggtitle('PCA') 
-      ggsave(file=opt$pca_out, device="pdf")
-  }
-}
-  
-
-
-
-
-
-
-
-
-
-
-