--- /dev/null	Thu Jan 01 00:00:00 1970 +0000
+++ b/ruvseq.R	Wed Sep 05 15:54:16 2018 -0400
@@ -0,0 +1,198 @@
+# setup R error handling to go to stderr
+library("getopt")
+options( show.error.messages=F, error = function () { cat( geterrmessage(), file=stderr() ); q( "no", 1, F ) } )
+options(stringAsFactors = FALSE, useFancyQuotes = FALSE)
+
+setup_cmdline_options <- function() {
+  args <- commandArgs(trailingOnly = TRUE)
+  spec <- matrix(c(
+    "help", "h", 0, "logical",
+    "alpha", "a", 1, "double",
+    "min_mean_count", "min_c", 1, "double",
+    "min_k", "min_k", 1, "double",
+    "max_k", "max_k", 1, "double",
+    "sample_json", "s", 1, "character",
+    "plots" , "p", 1, "character",
+    "header", "H", 0, "logical",
+    "txtype", "y", 1, "character",
+    "tx2gene", "x", 1, "character"), # a space-sep tx-to-gene map or GTF file (auto detect .gtf/.GTF)
+    byrow=TRUE, ncol=4)
+
+  opt <- getopt(spec)
+  # if help was asked for print a friendly message
+  # and exit with a non-zero error code
+  if (!is.null(opt$help)) {
+    cat(getopt(spec, usage=TRUE))
+    q(status=1)
+  } else {
+    load_libraries()
+  }
+  return(opt)
+}
+
+load_libraries <- function() {
+  # Allows displaying help without waiting for libraries to load
+  library("tools")
+  library("jsonlite")
+  library("reshape2")
+  library("RUVSeq")
+  library("RColorBrewer")
+  library("tximport")
+  library("DESeq2")
+  library("ggrepel")
+}
+
+source_local <- function(fname){
+    argv <- commandArgs(trailingOnly = FALSE)
+    base_dir <- dirname(substring(argv[grep("--file=", argv)], 8))
+    source(paste(base_dir, fname, sep="/"))
+}
+
+# Source get_deseq_dataset.R for getting deseq dataset from htseq/featurecounts/tximport
+source_local('get_deseq_dataset.R')
+
+# RUVseq function definitions
+
+plot_pca_rle <- function (set, title) {
+  x <- pData(set)[,1]
+  colors <- brewer.pal(3, "Set2")
+  label <- paste0(' for ', title)
+  plotRLE(set, outline=FALSE, ylim=c(-4, 4), col=colors[x])
+  title(main=paste0("RLE", label))
+  plotPCA(set, col=colors[x], cex=1.2)
+  title(main=paste0("PCA", label))
+}
+
+plot_factors_of_unwanted_variation <- function(set, method, k){
+  pd <- pData(set)
+  pd['sample'] <- row.names(pd)
+  colnames(pd)[1] <- 'condition'
+  d = melt(pd, id.vars = c('sample', 'condition'))
+  d['x'] <- 1  # There is no information on the X, so we just fake it to be able to do a scatterplot
+  print(ggplot(d, aes(x=x, y=value, color=condition, label=sample)) +
+  geom_point() +
+  ggtitle(paste0('Factors of unwanted variation for method: ', method, ", k=", k)) +
+  facet_wrap( ~ variable, scales = "free_x") +
+  geom_text_repel() +
+  theme(axis.title.x=element_blank(),
+        axis.text.x=element_blank(),
+        axis.ticks.x=element_blank(),
+        plot.title = element_text(hjust = 0.5))
+  )
+}
+
+create_seq_expression_set <- function (dds, min_mean_count) {
+  count_values <- counts(dds)
+  filter <- apply(count_values, 1, function(x) mean(x) > min_mean_count)
+  filtered <- count_values[filter,]
+  set = newSeqExpressionSet(as.matrix(count_values),
+                            phenoData = data.frame(colData(dds)$condition, row.names=colnames(filtered)))
+  plot_pca_rle(set = set, title = 'raw data')
+  set <- betweenLaneNormalization(set, which="upper")
+  plot_pca_rle(set = set, title = 'upper quartile normalized')
+  return(set)
+}
+
+get_empirical_control_genes <- function(set, cutoff_p) {
+  x <- pData(set)[,1]
+  design <- model.matrix(~x, data=pData(set))
+  y <- DGEList(counts=counts(set), group=x)
+  y <- calcNormFactors(y, method="upperquartile")
+  y <- estimateGLMCommonDisp(y, design)
+  y <- estimateGLMTagwiseDisp(y, design)
+  fit <- glmFit(y, design)
+  lrt <- glmLRT(fit, coef=2)
+  top <- topTags(lrt, n=nrow(set))$table
+  top_rows <- rownames(top)[which(top$PValue > cutoff_p)]
+  empirical <- rownames(set)[which(!(rownames(set) %in% top_rows))]
+  return(empirical)
+}
+
+ruv_control_gene_method <- function(set, k, control_genes='empirical', cutoff_p=0.2) {
+  if (control_genes == 'empirical') {
+    control_genes = get_empirical_control_genes(set, cutoff_p=cutoff_p)
+  }
+  set <- RUVg(set, control_genes, k=k)
+  plot_pca_rle(set, paste0("RUVg with empirical control genes, k=", k))
+  plot_factors_of_unwanted_variation(set, method="RUVg with empirical control genes", k=k)
+  return(set)
+}
+
+ruv_residual_method <- function(set, k) {
+  genes <- rownames(counts(set))
+  x <- pData(set)[,1]
+  # Initial edger residuals
+  design <- model.matrix(~x, data=pData(set))
+  y <- DGEList(counts=counts(set), group=x)
+  y <- calcNormFactors(y, method="upperquartile")
+  y <- estimateGLMCommonDisp(y, design)
+  y <- estimateGLMTagwiseDisp(y, design)
+  fit <- glmFit(y, design)
+  res <- residuals(fit, type="deviance")
+  set <- RUVr(set, genes, k=k, res)
+  plot_pca_rle(set = set, title = paste0('RUVr using residuals, k=', k))
+  plot_factors_of_unwanted_variation(set, method="RUVr using residuals", k=k)
+  return(set)
+}
+
+ruv_replicate_method <- function (set, k) {
+  genes <- rownames(counts(set))
+  x <- pData(set)[,1]
+  differences <- makeGroups(x)
+  set <- RUVs(set, genes, k=k, differences)
+  plot_pca_rle(set, paste0('RUVs with replicate samples, k=', k))
+  plot_factors_of_unwanted_variation(set, method="RUVs using replicates", k=k)
+  return(set)
+}
+
+get_differentially_expressed_genes <- function(dds, contrast, alpha=0.01) {
+  r <- results(dds, contrast=contrast, alpha=alpha)
+  return(rownames(r[which(r$padj < alpha),]))
+}
+
+opt <- setup_cmdline_options()
+alpha <- opt$alpha
+min_k <- opt$min_k
+max_k <- opt$max_k
+sample_json <- fromJSON(opt$sample_json)
+sample_paths <- sample_json$path
+sample_names <- sample_json$label
+condition <- as.factor(sample_json$condition)
+sampleTable <- data.frame(samplename=sample_names,
+                          filename = sample_paths,
+                          condition=condition)
+rownames(sampleTable) <- sample_names
+
+dds <- get_deseq_dataset(sampleTable, header=opt$header, designFormula= ~ condition, tximport=opt$txtype, txtype=opt$txtype, tx2gene=opt$tx2gene)
+if (!is.null(opt$plots)) {
+  pdf(opt$plots)
+}
+
+# Run through the ruvseq variants
+set <- create_seq_expression_set(dds, min_mean_count = opt$min_mean_count)
+result <- list(no_correction = set)
+for (k in seq(min_k, max_k)) {
+  result[[paste0('residual_method_k', k)]] <- ruv_residual_method(set, k=k)
+  result[[paste0('replicate_method_k', k)]] <- ruv_replicate_method(set, k=k)
+  result[[paste0('control_method_k', k)]] <- ruv_control_gene_method(set, k=k, cutoff_p=0.5)
+}
+
+for (name in names(result)) {
+  if (!startsWith(name, "no_correction")) {
+    set <- result[[name]]
+    unwanted_variation <- pData(set)
+    df <- data.frame(identifier = rownames(unwanted_variation))
+    df <- cbind(df, unwanted_variation)
+    colnames(df)[2] <- 'condition'
+    write.table(df, file=paste0("batch_effects_", name, ".tabular"),  sep="\t", quote=F, row.names=F)
+  }
+}
+
+# close the plot device
+if (!is.null(opt$plots)) {
+  cat("closing plot device\n")
+  dev.off()
+}
+
+cat("Session information:\n\n")
+sessionInfo()