@@ -601,7 +609,7 @@
**What it does**
-Given a matrix of counts (e.g. from featureCounts) and optional information about the genes, performs differential expression (DE) using the limma_ Bioconductor package and produces plots and tables useful in DE analysis.
+Given a matrix of counts (e.g. from featureCounts) and optional information about the genes, performs differential expression (DE) using the limma_ Bioconductor package and produces plots and tables useful in DE analysis. If an annotation file is provided, interactive Glimma_ plots and a table of differentially expressed genes will also be generated. See an example workflow here_.
In the `limma approach`_ to RNA-seq, read counts are converted to log2-counts-per-million (logCPM) and the mean-variance relationship is modelled either with precision weights or with an empirical Bayes prior trend. The precision weights approach is called “voom” and the prior trend approach is called “limma-trend”. For more information, see the Help section below.
@@ -647,7 +655,7 @@
**Gene Annotations:**
Optional input for gene annotations, this can contain more
information about the genes than just an ID number. The annotations will
-be available in the differential expression results table and the optional normalised counts table. The file must contain a header row and have the gene IDs in the first column. The second column will be used to label the genes in the Volcano plot instead of the default Gene IDs. The number of rows should match that of the counts files, add NA for any gene IDs with no annotation. The Galaxy tool **annotateMyIDs** can be used to obtain annotations for human, mouse, fly and zebrafish.
+be available in the differential expression results table and the optional normalised counts table. They will also be used to generate interactive Glimma_ MD plots and table of differential expression, a link to the Glimma plots will be provided in the report. The input annotation file must contain a header row and have the gene IDs in the first column. The second column will be used to label the genes in the Volcano plot and interactive Glimma plots, additional columns will be available in the Glimma interactive table. The number of rows should match that of the counts files, add NA for any gene IDs with no annotation. The Galaxy tool **annotateMyIDs** can be used to obtain annotations for human, mouse, fly and zebrafish.
Example:
@@ -783,6 +791,7 @@
* a table of differentially expressed genes for each contrast of interest
* a HTML report with plots and additional information
+ * an interactive Glimma MD plot and table (if annotation file provided)
Optionally, under **Output Options** you can choose to output
@@ -844,6 +853,8 @@
Please report problems or suggestions to: su.s@wehi.edu.au
.. _limma: http://www.bioconductor.org/packages/release/bioc/html/limma.html
+.. _Glimma: https://bioconductor.org/packages/release/bioc/html/Glimma.html
+.. _here: https://f1000research.com/articles/5-1408/v2
.. _limma approach: https://www.ncbi.nlm.nih.gov/pubmed/25605792
.. _limma User's Guide: http://bioconductor.org/packages/release/bioc/vignettes/limma/inst/doc/usersguide.pdf
.. _edgeR: http://www.bioconductor.org/packages/release/bioc/html/edgeR.html
diff -r 00a42b66e522 -r 4255881bebb1 test-data/out_rscript.txt
--- a/test-data/out_rscript.txt Tue May 15 02:36:36 2018 -0400
+++ /dev/null Thu Jan 01 00:00:00 1970 +0000
@@ -1,1211 +0,0 @@
-# This tool takes in a matrix of feature counts as well as gene annotations and
-# outputs a table of top expressions as well as various plots for differential
-# expression analysis
-#
-# ARGS: htmlPath", "R", 1, "character" -Path to html file linking to other outputs
-# outPath", "o", 1, "character" -Path to folder to write all output to
-# filesPath", "j", 2, "character" -JSON list object if multiple files input
-# matrixPath", "m", 2, "character" -Path to count matrix
-# factFile", "f", 2, "character" -Path to factor information file
-# factInput", "i", 2, "character" -String containing factors if manually input
-# annoPath", "a", 2, "character" -Path to input containing gene annotations
-# contrastData", "C", 1, "character" -String containing contrasts of interest
-# cpmReq", "c", 2, "double" -Float specifying cpm requirement
-# cntReq", "z", 2, "integer" -Integer specifying minimum total count requirement
-# sampleReq", "s", 2, "integer" -Integer specifying cpm requirement
-# normCounts", "x", 0, "logical" -String specifying if normalised counts should be output
-# rdaOpt", "r", 0, "logical" -String specifying if RData should be output
-# lfcReq", "l", 1, "double" -Float specifying the log-fold-change requirement
-# pValReq", "p", 1, "double" -Float specifying the p-value requirement
-# pAdjOpt", "d", 1, "character" -String specifying the p-value adjustment method
-# normOpt", "n", 1, "character" -String specifying type of normalisation used
-# robOpt", "b", 0, "logical" -String specifying if robust options should be used
-# trend", "t", 1, "double" -Float for prior.count if limma-trend is used instead of voom
-# weightOpt", "w", 0, "logical" -String specifying if voomWithQualityWeights should be used
-# topgenes", "G", 1, "integer" -Integer specifying no. of genes to highlight in volcano and heatmap
-# treatOpt", "T", 0, "logical" -String specifying if TREAT function should be used
-# plots, "P", 1, "character" -String specifying additional plots to be created
-#
-# OUT:
-# Density Plots (if filtering)
-# Box Plots (if normalising)
-# MDS Plot
-# Voom/SA plot
-# MD Plot
-# Volcano Plot
-# Heatmap
-# Expression Table
-# HTML file linking to the ouputs
-# Optional:
-# Normalised counts Table
-# RData file
-#
-#
-# Author: Shian Su - registertonysu@gmail.com - Jan 2014
-# Modified by: Maria Doyle - Jun 2017, Jan 2018, May 2018
-
-# Record starting time
-timeStart <- as.character(Sys.time())
-
-# Load all required libraries
-library(methods, quietly=TRUE, warn.conflicts=FALSE)
-library(statmod, quietly=TRUE, warn.conflicts=FALSE)
-library(splines, quietly=TRUE, warn.conflicts=FALSE)
-library(edgeR, quietly=TRUE, warn.conflicts=FALSE)
-library(limma, quietly=TRUE, warn.conflicts=FALSE)
-library(scales, quietly=TRUE, warn.conflicts=FALSE)
-library(getopt, quietly=TRUE, warn.conflicts=FALSE)
-library(gplots, quietly=TRUE, warn.conflicts=FALSE)
-
-################################################################################
-### Function Declaration
-################################################################################
-# Function to sanitise contrast equations so there are no whitespaces
-# surrounding the arithmetic operators, leading or trailing whitespace
-sanitiseEquation <- function(equation) {
- equation <- gsub(" *[+] *", "+", equation)
- equation <- gsub(" *[-] *", "-", equation)
- equation <- gsub(" *[/] *", "/", equation)
- equation <- gsub(" *[*] *", "*", equation)
- equation <- gsub("^\\s+|\\s+$", "", equation)
- return(equation)
-}
-
-# Function to sanitise group information
-sanitiseGroups <- function(string) {
- string <- gsub(" *[,] *", ",", string)
- string <- gsub("^\\s+|\\s+$", "", string)
- return(string)
-}
-
-# Function to change periods to whitespace in a string
-unmake.names <- function(string) {
- string <- gsub(".", " ", string, fixed=TRUE)
- return(string)
-}
-
-# Generate output folder and paths
-makeOut <- function(filename) {
- return(paste0(opt$outPath, "/", filename))
-}
-
-# Generating design information
-pasteListName <- function(string) {
- return(paste0("factors$", string))
-}
-
-# Create cata function: default path set, default seperator empty and appending
-# true by default (Ripped straight from the cat function with altered argument
-# defaults)
-cata <- function(..., file = opt$htmlPath, sep = "", fill = FALSE, labels = NULL,
- append = TRUE) {
- if (is.character(file))
- if (file == "")
- file <- stdout()
- else if (substring(file, 1L, 1L) == "|") {
- file <- pipe(substring(file, 2L), "w")
- on.exit(close(file))
- }
- else {
- file <- file(file, ifelse(append, "a", "w"))
- on.exit(close(file))
- }
- .Internal(cat(list(...), file, sep, fill, labels, append))
-}
-
-# Function to write code for html head and title
-HtmlHead <- function(title) {
- cata("\n")
- cata("", title, "\n")
- cata("\n")
-}
-
-# Function to write code for html links
-HtmlLink <- function(address, label=address) {
- cata("", label, "
\n")
-}
-
-# Function to write code for html images
-HtmlImage <- function(source, label=source, height=500, width=500) {
- cata("![\"",](\"",)
\n")
-}
-
-# Function to write code for html list items
-ListItem <- function(...) {
- cata("", ..., "\n")
-}
-
-TableItem <- function(...) {
- cata("", ..., " | \n")
-}
-
-TableHeadItem <- function(...) {
- cata("", ..., " | \n")
-}
-
-################################################################################
-### Input Processing
-################################################################################
-
-# Collect arguments from command line
-args <- commandArgs(trailingOnly=TRUE)
-
-# Get options, using the spec as defined by the enclosed list.
-# Read the options from the default: commandArgs(TRUE).
-spec <- matrix(c(
- "htmlPath", "R", 1, "character",
- "outPath", "o", 1, "character",
- "filesPath", "j", 2, "character",
- "matrixPath", "m", 2, "character",
- "factFile", "f", 2, "character",
- "factInput", "i", 2, "character",
- "annoPath", "a", 2, "character",
- "contrastData", "C", 1, "character",
- "cpmReq", "c", 1, "double",
- "totReq", "y", 0, "logical",
- "cntReq", "z", 1, "integer",
- "sampleReq", "s", 1, "integer",
- "normCounts", "x", 0, "logical",
- "rdaOpt", "r", 0, "logical",
- "lfcReq", "l", 1, "double",
- "pValReq", "p", 1, "double",
- "pAdjOpt", "d", 1, "character",
- "normOpt", "n", 1, "character",
- "robOpt", "b", 0, "logical",
- "trend", "t", 1, "double",
- "weightOpt", "w", 0, "logical",
- "topgenes", "G", 1, "integer",
- "treatOpt", "T", 0, "logical",
- "plots", "P", 1, "character"),
- byrow=TRUE, ncol=4)
-opt <- getopt(spec)
-
-
-if (is.null(opt$matrixPath) & is.null(opt$filesPath)) {
- cat("A counts matrix (or a set of counts files) is required.\n")
- q(status=1)
-}
-
-if (is.null(opt$cpmReq)) {
- filtCPM <- FALSE
-} else {
- filtCPM <- TRUE
-}
-
-if (is.null(opt$cntReq) || is.null(opt$sampleReq)) {
- filtSmpCount <- FALSE
-} else {
- filtSmpCount <- TRUE
-}
-
-if (is.null(opt$totReq)) {
- filtTotCount <- FALSE
-} else {
- filtTotCount <- TRUE
-}
-
-if (is.null(opt$rdaOpt)) {
- wantRda <- FALSE
-} else {
- wantRda <- TRUE
-}
-
-if (is.null(opt$annoPath)) {
- haveAnno <- FALSE
-} else {
- haveAnno <- TRUE
-}
-
-if (is.null(opt$normCounts)) {
- wantNorm <- FALSE
-} else {
- wantNorm <- TRUE
-}
-
-if (is.null(opt$robOpt)) {
- wantRobust <- FALSE
-} else {
- wantRobust <- TRUE
-}
-
-if (is.null(opt$weightOpt)) {
- wantWeight <- FALSE
-} else {
- wantWeight <- TRUE
-}
-
-if (is.null(opt$trend)) {
- wantTrend <- FALSE
- deMethod <- "limma-voom"
-} else {
- wantTrend <- TRUE
- deMethod <- "limma-trend"
- priorCount <- opt$trend
-}
-
-if (is.null(opt$treatOpt)) {
- wantTreat <- FALSE
-} else {
- wantTreat <- TRUE
-}
-
-
-if (!is.null(opt$filesPath)) {
- # Process the separate count files (adapted from DESeq2 wrapper)
- library("rjson")
- parser <- newJSONParser()
- parser$addData(opt$filesPath)
- factorList <- parser$getObject()
- factors <- sapply(factorList, function(x) x[[1]])
- filenamesIn <- unname(unlist(factorList[[1]][[2]]))
- sampleTable <- data.frame(sample=basename(filenamesIn),
- filename=filenamesIn,
- row.names=filenamesIn,
- stringsAsFactors=FALSE)
- for (factor in factorList) {
- factorName <- factor[[1]]
- sampleTable[[factorName]] <- character(nrow(sampleTable))
- lvls <- sapply(factor[[2]], function(x) names(x))
- for (i in seq_along(factor[[2]])) {
- files <- factor[[2]][[i]][[1]]
- sampleTable[files,factorName] <- lvls[i]
- }
- sampleTable[[factorName]] <- factor(sampleTable[[factorName]], levels=lvls)
- }
- rownames(sampleTable) <- sampleTable$sample
- rem <- c("sample","filename")
- factors <- sampleTable[, !(names(sampleTable) %in% rem), drop=FALSE]
-
- #read in count files and create single table
- countfiles <- lapply(sampleTable$filename, function(x){read.delim(x, row.names=1)})
- counts <- do.call("cbind", countfiles)
-
-} else {
- # Process the single count matrix
- counts <- read.table(opt$matrixPath, header=TRUE, sep="\t", stringsAsFactors=FALSE)
- row.names(counts) <- counts[, 1]
- counts <- counts[ , -1]
- countsRows <- nrow(counts)
-
- # Process factors
- if (is.null(opt$factInput)) {
- factorData <- read.table(opt$factFile, header=TRUE, sep="\t")
- factors <- factorData[, -1, drop=FALSE]
- } else {
- factors <- unlist(strsplit(opt$factInput, "|", fixed=TRUE))
- factorData <- list()
- for (fact in factors) {
- newFact <- unlist(strsplit(fact, split="::"))
- factorData <- rbind(factorData, newFact)
- } # Factors have the form: FACT_NAME::LEVEL,LEVEL,LEVEL,LEVEL,... The first factor is the Primary Factor.
-
- # Set the row names to be the name of the factor and delete first row
- row.names(factorData) <- factorData[, 1]
- factorData <- factorData[, -1]
- factorData <- sapply(factorData, sanitiseGroups)
- factorData <- sapply(factorData, strsplit, split=",")
- factorData <- sapply(factorData, make.names)
- # Transform factor data into data frame of R factor objects
- factors <- data.frame(factorData)
- }
-}
-
- # if annotation file provided
-if (haveAnno) {
- geneanno <- read.table(opt$annoPath, header=TRUE, sep="\t", stringsAsFactors=FALSE)
-}
-
-#Create output directory
-dir.create(opt$outPath, showWarnings=FALSE)
-
-# Split up contrasts seperated by comma into a vector then sanitise
-contrastData <- unlist(strsplit(opt$contrastData, split=","))
-contrastData <- sanitiseEquation(contrastData)
-contrastData <- gsub(" ", ".", contrastData, fixed=TRUE)
-
-plots <- character()
-if (!is.null(opt$plots)) {
- plots <- unlist(strsplit(opt$plots, split=","))
-}
-
-denOutPng <- makeOut("densityplots.png")
-denOutPdf <- makeOut("densityplots.pdf")
-cpmOutPdf <- makeOut("cpmplots.pdf")
-boxOutPng <- makeOut("boxplots.png")
-boxOutPdf <- makeOut("boxplots.pdf")
-mdsscreeOutPng <- makeOut("mdsscree.png")
-mdsscreeOutPdf <- makeOut("mdsscree.pdf")
-mdsxOutPdf <- makeOut("mdsplot_extra.pdf")
-mdsxOutPng <- makeOut("mdsplot_extra.png")
-mdsamOutPdf <- makeOut("mdplots_samples.pdf")
-mdOutPdf <- character() # Initialise character vector
-volOutPdf <- character()
-heatOutPdf <- character()
-stripOutPdf <- character()
-mdvolOutPng <- character()
-topOut <- character()
-for (i in 1:length(contrastData)) {
- con <- contrastData[i]
- con <- gsub("\\(|\\)", "", con)
- mdOutPdf[i] <- makeOut(paste0("mdplot_", con, ".pdf"))
- volOutPdf[i] <- makeOut(paste0("volplot_", con, ".pdf"))
- heatOutPdf[i] <- makeOut(paste0("heatmap_", con, ".pdf"))
- stripOutPdf[i] <- makeOut(paste0("stripcharts_", con, ".pdf"))
- mdvolOutPng[i] <- makeOut(paste0("mdvolplot_", con, ".png"))
- topOut[i] <- makeOut(paste0(deMethod, "_", con, ".tsv"))
-}
-
-normOut <- makeOut(paste0(deMethod, "_normcounts.tsv"))
-rdaOut <- makeOut(paste0(deMethod, "_analysis.RData"))
-sessionOut <- makeOut("session_info.txt")
-
-# Initialise data for html links and images, data frame with columns Label and
-# Link
-linkData <- data.frame(Label=character(), Link=character(),
- stringsAsFactors=FALSE)
-imageData <- data.frame(Label=character(), Link=character(),
- stringsAsFactors=FALSE)
-
-# Initialise vectors for storage of up/down/neutral regulated counts
-upCount <- numeric()
-downCount <- numeric()
-flatCount <- numeric()
-
-################################################################################
-### Data Processing
-################################################################################
-
-# Extract counts and annotation data
-print("Extracting counts")
-data <- list()
-data$counts <- counts
-if (haveAnno) {
- # order annotation by genes in counts (assumes gene ids are in 1st column of geneanno)
- annoord <- geneanno[match(row.names(counts), geneanno[,1]), ]
- data$genes <- annoord
-} else {
- data$genes <- data.frame(GeneID=row.names(counts))
-}
-
-# Creating naming data
-samplenames <- colnames(data$counts)
-sampleanno <- data.frame("sampleID"=samplenames, factors)
-
-# Creating colours for the groups
-cols <- as.numeric(factors[, 1])
-col.group <- palette()[cols]
-
-# If filter crieteria set, filter out genes that do not have a required cpm/counts in a required number of
-# samples. Default is no filtering
-preFilterCount <- nrow(data$counts)
-nsamples <- ncol(data$counts)
-
-if (filtCPM || filtSmpCount || filtTotCount) {
-
- if (filtTotCount) {
- keep <- rowSums(data$counts) >= opt$cntReq
- } else if (filtSmpCount) {
- keep <- rowSums(data$counts >= opt$cntReq) >= opt$sampleReq
- } else if (filtCPM) {
- myCPM <- cpm(data$counts)
- thresh <- myCPM >= opt$cpmReq
- keep <- rowSums(thresh) >= opt$sampleReq
-
- if ("c" %in% plots) {
- # Plot CPM vs raw counts (to check threshold)
- pdf(cpmOutPdf, width=6.5, height=10)
- par(mfrow=c(3, 2))
- for (i in 1:nsamples) {
- plot(data$counts[, i], myCPM[, i], xlim=c(0,50), ylim=c(0,3), main=samplenames[i], xlab="Raw counts", ylab="CPM")
- abline(v=10, col="red", lty=2, lwd=2)
- abline(h=opt$cpmReq, col=4)
- }
- linkName <- "CpmPlots.pdf"
- linkAddr <- "cpmplots.pdf"
- linkData <- rbind(linkData, data.frame(Label=linkName, Link=linkAddr, stringsAsFactors=FALSE))
- invisible(dev.off())
- }
- }
-
- data$counts <- data$counts[keep, ]
- data$genes <- data$genes[keep, , drop=FALSE]
-
- # Plot Density
- if ("d" %in% plots) {
- # PNG
- png(denOutPng, width=1000, height=500)
- par(mfrow=c(1,2), cex.axis=0.8)
-
- # before filtering
- lcpm1 <- cpm(counts, log=TRUE)
- plot(density(lcpm1[, 1]), col=col.group[1], lwd=2, las=2, main="", xlab="")
- title(main="Density Plot: Raw counts", xlab="Log-cpm")
- for (i in 2:nsamples){
- den <- density(lcpm1[, i])
- lines(den$x, den$y, col=col.group[i], lwd=2)
- }
-
- # after filtering
- lcpm2 <- cpm(data$counts, log=TRUE)
- plot(density(lcpm2[,1]), col=col.group[1], lwd=2, las=2, main="", xlab="")
- title(main="Density Plot: Filtered counts", xlab="Log-cpm")
- for (i in 2:nsamples){
- den <- density(lcpm2[, i])
- lines(den$x, den$y, col=col.group[i], lwd=2)
- }
- legend("topright", samplenames, text.col=col.group, bty="n")
- imgName <- "Densityplots.png"
- imgAddr <- "densityplots.png"
- imageData <- rbind(imageData, data.frame(Label=imgName, Link=imgAddr, stringsAsFactors=FALSE))
- invisible(dev.off())
-
- # PDF
- pdf(denOutPdf, width=14)
- par(mfrow=c(1,2), cex.axis=0.8)
- plot(density(lcpm1[, 1]), col=col.group[1], lwd=2, las=2, main="", xlab="")
- title(main="Density Plot: Raw counts", xlab="Log-cpm")
- for (i in 2:nsamples){
- den <- density(lcpm1[, i])
- lines(den$x, den$y, col=col.group[i], lwd=2)
- }
- plot(density(lcpm2[, 1]), col=col.group[1], lwd=2, las=2, main="", xlab="")
- title(main="Density Plot: Filtered counts", xlab="Log-cpm")
- for (i in 2:nsamples){
- den <- density(lcpm2[, i])
- lines(den$x, den$y, col=col.group[i], lwd=2)
- }
- legend("topright", samplenames, text.col=col.group, bty="n")
- linkName <- "DensityPlots.pdf"
- linkAddr <- "densityplots.pdf"
- linkData <- rbind(linkData, data.frame(Label=linkName, Link=linkAddr, stringsAsFactors=FALSE))
- invisible(dev.off())
- }
-}
-
-postFilterCount <- nrow(data$counts)
-filteredCount <- preFilterCount-postFilterCount
-
-# Generating the DGEList object "y"
-print("Generating DGEList object")
-data$samples <- sampleanno
-data$samples$lib.size <- colSums(data$counts)
-data$samples$norm.factors <- 1
-row.names(data$samples) <- colnames(data$counts)
-y <- new("DGEList", data)
-
-print("Generating Design")
-# Name rows of factors according to their sample
-row.names(factors) <- names(data$counts)
-factorList <- sapply(names(factors), pasteListName)
-formula <- "~0"
-for (i in 1:length(factorList)) {
- formula <- paste(formula,factorList[i], sep="+")
-}
-formula <- formula(formula)
-design <- model.matrix(formula)
-for (i in 1:length(factorList)) {
- colnames(design) <- gsub(factorList[i], "", colnames(design), fixed=TRUE)
-}
-
-# Calculating normalising factors
-print("Calculating Normalisation Factors")
-y <- calcNormFactors(y, method=opt$normOpt)
-
-# Generate contrasts information
-print("Generating Contrasts")
-contrasts <- makeContrasts(contrasts=contrastData, levels=design)
-
-################################################################################
-### Data Output
-################################################################################
-
-# Plot Box plots (before and after normalisation)
-if (opt$normOpt != "none" & "b" %in% plots) {
- png(boxOutPng, width=1000, height=500)
- par(mfrow=c(1,2), mar=c(6,4,2,2)+0.1)
- labels <- colnames(counts)
-
- lcpm1 <- cpm(y$counts, log=TRUE)
- boxplot(lcpm1, las=2, col=col.group, xaxt="n", xlab="")
- axis(1, at=seq_along(labels), labels = FALSE)
- abline(h=median(lcpm1), col=4)
- text(x=seq_along(labels), y=par("usr")[3]-1, srt=45, adj=1, labels=labels, xpd=TRUE)
- title(main="Box Plot: Unnormalised counts", ylab="Log-cpm")
-
- lcpm2 <- cpm(y, log=TRUE)
- boxplot(lcpm2, las=2, col=col.group, xaxt="n", xlab="")
- axis(1, at=seq_along(labels), labels = FALSE)
- text(x=seq_along(labels), y=par("usr")[3]-1, srt=45, adj=1, labels=labels, xpd=TRUE)
- abline(h=median(lcpm2), col=4)
- title(main="Box Plot: Normalised counts", ylab="Log-cpm")
-
- imgName <- "Boxplots.png"
- imgAddr <- "boxplots.png"
- imageData <- rbind(imageData, data.frame(Label=imgName, Link=imgAddr, stringsAsFactors=FALSE))
- invisible(dev.off())
-
- pdf(boxOutPdf, width=14)
- par(mfrow=c(1,2), mar=c(6,4,2,2)+0.1)
- boxplot(lcpm1, las=2, col=col.group, xaxt="n", xlab="")
- axis(1, at=seq_along(labels), labels = FALSE)
- abline(h=median(lcpm1), col=4)
- text(x=seq_along(labels), y=par("usr")[3]-1, srt=45, adj=1, labels=labels, xpd=TRUE)
- title(main="Box Plot: Unnormalised counts", ylab="Log-cpm")
- boxplot(lcpm2, las=2, col=col.group, xaxt="n", xlab="")
- axis(1, at=seq_along(labels), labels = FALSE)
- text(x=seq_along(labels), y=par("usr")[3]-1, srt=45, adj=1, labels=labels, xpd=TRUE)
- abline(h=median(lcpm2), col=4)
- title(main="Box Plot: Normalised counts", ylab="Log-cpm")
- linkName <- "BoxPlots.pdf"
- linkAddr <- "boxplots.pdf"
- linkData <- rbind(linkData, data.frame(Label=linkName, Link=linkAddr, stringsAsFactors=FALSE))
- invisible(dev.off())
-}
-
-# Plot MDS
-print("Generating MDS plot")
-labels <- names(counts)
-
-# Scree plot (Variance Explained) code copied from Glimma
-
-# get column of matrix
-getCols <- function(x, inds) {
- x[, inds, drop=FALSE]
-}
-
-x <- cpm(y, log=TRUE)
-ndim <- nsamples - 1
-nprobes <- nrow(x)
-top <- 500
-top <- min(top, nprobes)
-cn <- colnames(x)
-bad <- rowSums(is.finite(x)) < nsamples
-
-if (any(bad)) {
- warning("Rows containing infinite values have been removed")
- x <- x[!bad, , drop=FALSE]
-}
-
-dd <- matrix(0, nrow=nsamples, ncol=nsamples, dimnames=list(cn, cn))
-topindex <- nprobes - top + 1L
-for (i in 2L:(nsamples)) {
- for (j in 1L:(i - 1L)) {
- dists <- (getCols(x, i) - getCols(x, j))^2
- dists <- sort.int(dists, partial = topindex )
- topdist <- dists[topindex:nprobes]
- dd[i, j] <- sqrt(mean(topdist))
- }
-}
-
-a1 <- suppressWarnings(cmdscale(as.dist(dd), k=min(ndim, 8), eig=TRUE))
-eigen <- data.frame(name = 1:min(ndim, 8), eigen = round(a1$eig[1:min(ndim, 8)]/sum(a1$eig), 2))
-
-png(mdsscreeOutPng, width=1000, height=500)
-par(mfrow=c(1, 2))
-plotMDS(y, labels=samplenames, col=as.numeric(factors[, 1]), main="MDS Plot: Dims 1 and 2")
-barplot(eigen$eigen, names.arg=eigen$name, main = "Scree Plot: Variance Explained", xlab = "Dimension", ylab = "Proportion", las=1)
-imgName <- paste0("MDSPlot_", names(factors)[1], ".png")
-imgAddr <- "mdsscree.png"
-imageData <- rbind(imageData, data.frame(Label=imgName, Link=imgAddr, stringsAsFactors=FALSE))
-invisible(dev.off())
-
-pdf(mdsscreeOutPdf, width=14)
-par(mfrow=c(1, 2))
-plotMDS(y, labels=samplenames, col=as.numeric(factors[, 1]), main="MDS Plot: Dims 1 and 2")
-barplot(eigen$eigen, names.arg=eigen$name, main = "Scree Plot: Variance Explained", xlab = "Dimension", ylab = "Proportion", las=1)
-linkName <- paste0("MDSPlot_", names(factors)[1], ".pdf")
-linkAddr <- "mdsscree.pdf"
-linkData <- rbind(linkData, data.frame(Label=linkName, Link=linkAddr, stringsAsFactors=FALSE))
-invisible(dev.off())
-
-if ("x" %in% plots) {
- png(mdsxOutPng, width=1000, height=500)
- par(mfrow=c(1, 2))
- for (i in 2:3) {
- dim1 <- i
- dim2 <- i + 1
- plotMDS(y, dim=c(dim1, dim2), labels=samplenames, col=as.numeric(factors[, 1]), main=paste("MDS Plot: Dims", dim1, "and", dim2))
- }
- imgName <- paste0("MDSPlot_extra.png")
- imgAddr <- paste0("mdsplot_extra.png")
- imageData <- rbind(imageData, data.frame(Label=imgName, Link=imgAddr, stringsAsFactors=FALSE))
- invisible(dev.off())
-
- pdf(mdsxOutPdf, width=14)
- par(mfrow=c(1, 2))
- for (i in 2:3) {
- dim1 <- i
- dim2 <- i + 1
- plotMDS(y, dim=c(dim1, dim2), labels=samplenames, col=as.numeric(factors[, 1]), main=paste("MDS Plot: Dims", dim1, "and", dim2))
- }
- linkName <- "MDSPlot_extra.pdf"
- linkAddr <- "mdsplot_extra.pdf"
- linkData <- rbind(linkData, data.frame(Label=linkName, Link=linkAddr, stringsAsFactors=FALSE))
- invisible(dev.off())
-}
-
-if ("m" %in% plots) {
- # Plot MD plots for individual samples
- print("Generating MD plots for samples")
- pdf(mdsamOutPdf, width=6.5, height=10)
- par(mfrow=c(3, 2))
- for (i in 1:nsamples) {
- plotMD(y, column = i)
- abline(h=0, col="red", lty=2, lwd=2)
- }
- linkName <- "MDPlots_Samples.pdf"
- linkAddr <- "mdplots_samples.pdf"
- linkData <- rbind(linkData, c(linkName, linkAddr))
- invisible(dev.off())
-}
-
-
-if (wantTrend) {
- # limma-trend approach
- logCPM <- cpm(y, log=TRUE, prior.count=opt$trend)
- fit <- lmFit(logCPM, design)
- fit$genes <- y$genes
- fit <- contrasts.fit(fit, contrasts)
- if (wantRobust) {
- fit <- eBayes(fit, trend=TRUE, robust=TRUE)
- } else {
- fit <- eBayes(fit, trend=TRUE, robust=FALSE)
- }
- # plot fit with plotSA
- saOutPng <- makeOut("saplot.png")
- saOutPdf <- makeOut("saplot.pdf")
-
- png(saOutPng, width=500, height=500)
- plotSA(fit, main="SA Plot")
- imgName <- "SAPlot.png"
- imgAddr <- "saplot.png"
- imageData <- rbind(imageData, c(imgName, imgAddr))
- invisible(dev.off())
-
- pdf(saOutPdf, width=14)
- plotSA(fit, main="SA Plot")
- linkName <- "SAPlot.pdf"
- linkAddr <- "saplot.pdf"
- linkData <- rbind(linkData, c(linkName, linkAddr))
- invisible(dev.off())
-
- plotData <- logCPM
-
- # Save normalised counts (log2cpm)
- if (wantNorm) {
- write.table(logCPM, file=normOut, row.names=TRUE, sep="\t", quote=FALSE)
- linkData <- rbind(linkData, c((paste0(deMethod, "_", "normcounts.tsv")), (paste0(deMethod, "_", "normcounts.tsv"))))
- }
-} else {
- # limma-voom approach
- voomOutPdf <- makeOut("voomplot.pdf")
- voomOutPng <- makeOut("voomplot.png")
-
- if (wantWeight) {
- # Creating voom data object and plot
- png(voomOutPng, width=1000, height=500)
- vData <- voomWithQualityWeights(y, design=design, plot=TRUE)
- imgName <- "VoomPlot.png"
- imgAddr <- "voomplot.png"
- imageData <- rbind(imageData, c(imgName, imgAddr))
- invisible(dev.off())
-
- pdf(voomOutPdf, width=14)
- vData <- voomWithQualityWeights(y, design=design, plot=TRUE)
- linkName <- "VoomPlot.pdf"
- linkAddr <- "voomplot.pdf"
- linkData <- rbind(linkData, c(linkName, linkAddr))
- invisible(dev.off())
-
- # Generating fit data and top table with weights
- wts <- vData$weights
- voomFit <- lmFit(vData, design, weights=wts)
-
- } else {
- # Creating voom data object and plot
- png(voomOutPng, width=500, height=500)
- vData <- voom(y, design=design, plot=TRUE)
- imgName <- "VoomPlot"
- imgAddr <- "voomplot.png"
- imageData <- rbind(imageData, c(imgName, imgAddr))
- invisible(dev.off())
-
- pdf(voomOutPdf)
- vData <- voom(y, design=design, plot=TRUE)
- linkName <- "VoomPlot.pdf"
- linkAddr <- "voomplot.pdf"
- linkData <- rbind(linkData, c(linkName, linkAddr))
- invisible(dev.off())
-
- # Generate voom fit
- voomFit <- lmFit(vData, design)
- }
-
- # Save normalised counts (log2cpm)
- if (wantNorm) {
- norm_counts <- data.frame(vData$genes, vData$E)
- write.table(norm_counts, file=normOut, row.names=FALSE, sep="\t", quote=FALSE)
- linkData <- rbind(linkData, c((paste0(deMethod, "_", "normcounts.tsv")), (paste0(deMethod, "_", "normcounts.tsv"))))
- }
-
- # Fit linear model and estimate dispersion with eBayes
- voomFit <- contrasts.fit(voomFit, contrasts)
- if (wantRobust) {
- fit <- eBayes(voomFit, robust=TRUE)
- } else {
- fit <- eBayes(voomFit, robust=FALSE)
- }
- plotData <- vData
-}
-
-
-print("Generating DE results")
-
-if (wantTreat) {
- print("Applying TREAT method")
- if (wantRobust) {
- fit <- treat(fit, lfc=opt$lfcReq, robust=TRUE)
- } else {
- fit <- treat(fit, lfc=opt$lfcReq, robust=FALSE)
- }
-}
-
-status = decideTests(fit, adjust.method=opt$pAdjOpt, p.value=opt$pValReq,
- lfc=opt$lfcReq)
-sumStatus <- summary(status)
-
-for (i in 1:length(contrastData)) {
- con <- contrastData[i]
- con <- gsub("\\(|\\)", "", con)
- # Collect counts for differential expression
- upCount[i] <- sumStatus["Up", i]
- downCount[i] <- sumStatus["Down", i]
- flatCount[i] <- sumStatus["NotSig", i]
-
- # Write top expressions table
- if (wantTreat) {
- top <- topTreat(fit, coef=i, number=Inf, sort.by="P")
- } else{
- top <- topTable(fit, coef=i, number=Inf, sort.by="P")
- }
- write.table(top, file=topOut[i], row.names=FALSE, sep="\t", quote=FALSE)
- linkName <- paste0(deMethod, "_", con, ".tsv")
- linkAddr <- paste0(deMethod, "_", con, ".tsv")
- linkData <- rbind(linkData, c(linkName, linkAddr))
-
- # Plot MD (log ratios vs mean average) using limma package on weighted
- pdf(mdOutPdf[i])
- limma::plotMD(fit, status=status[, i], coef=i,
- main=paste("MD Plot:", unmake.names(con)),
- hl.col=alpha(c("firebrick", "blue"), 0.4), values=c(1, -1),
- xlab="Average Expression", ylab="logFC")
- abline(h=0, col="grey", lty=2)
- linkName <- paste0("MDPlot_", con, ".pdf")
- linkAddr <- paste0("mdplot_", con, ".pdf")
- linkData <- rbind(linkData, c(linkName, linkAddr))
- invisible(dev.off())
-
- # Plot Volcano
- pdf(volOutPdf[i])
- if (haveAnno) {
- # labels must be in second column currently
- labels <- fit$genes[, 2]
- } else {
- labels <- fit$genes$GeneID
- }
- limma::volcanoplot(fit, coef=i,
- main=paste("Volcano Plot:", unmake.names(con)),
- highlight=opt$topgenes,
- names=labels)
- linkName <- paste0("VolcanoPlot_", con, ".pdf")
- linkAddr <- paste0("volplot_", con, ".pdf")
- linkData <- rbind(linkData, c(linkName, linkAddr))
- invisible(dev.off())
-
- # PNG of MD and Volcano
- png(mdvolOutPng[i], width=1000, height=500)
- par(mfrow=c(1, 2), mar=c(5,4,2,2)+0.1, oma=c(0,0,3,0))
-
- # MD plot
- limma::plotMD(fit, status=status[, i], coef=i, main="MD Plot",
- hl.col=alpha(c("firebrick", "blue"), 0.4), values=c(1, -1),
- xlab="Average Expression", ylab="logFC")
- abline(h=0, col="grey", lty=2)
-
- # Volcano
- if (haveAnno) {
- # labels must be in second column currently
- limma::volcanoplot(fit, coef=i, main="Volcano Plot",
- highlight=opt$topgenes,
- names=fit$genes[, 2])
- } else {
- limma::volcanoplot(fit, coef=i, main="Volcano Plot",
- highlight=opt$topgenes,
- names=fit$genes$GeneID)
- }
-
- imgName <- paste0("MDVolPlot_", con)
- imgAddr <- paste0("mdvolplot_", con, ".png")
- imageData <- rbind(imageData, c(imgName, imgAddr))
- title(paste0("Contrast: ", unmake.names(con)), outer=TRUE, cex.main=1.5)
- invisible(dev.off())
-
- if ("h" %in% plots) {
- # Plot Heatmap
- topgenes <- rownames(top[1:opt$topgenes, ])
- if (wantTrend) {
- topexp <- plotData[topgenes, ]
- } else {
- topexp <- plotData$E[topgenes, ]
- }
- pdf(heatOutPdf[i])
- mycol <- colorpanel(1000,"blue","white","red")
- if (haveAnno) {
- # labels must be in second column currently
- labels <- top[topgenes, 2]
- } else {
- labels <- rownames(topexp)
- }
- heatmap.2(topexp, scale="row", Colv=FALSE, Rowv=FALSE, dendrogram="none",
- main=paste("Contrast:", unmake.names(con), "\nTop", opt$topgenes, "genes by adj.P.Val"),
- trace="none", density.info="none", lhei=c(2,10), margin=c(8, 6), labRow=labels, cexRow=0.7, srtCol=45,
- col=mycol, ColSideColors=col.group)
- linkName <- paste0("Heatmap_", con, ".pdf")
- linkAddr <- paste0("heatmap_", con, ".pdf")
- linkData <- rbind(linkData, c(linkName, linkAddr))
- invisible(dev.off())
- }
-
- if ("s" %in% plots) {
- # Plot Stripcharts of top genes
- pdf(stripOutPdf[i], title=paste("Contrast:", unmake.names(con)))
- par(mfrow = c(3,2), cex.main=0.8, cex.axis=0.8)
- cols <- unique(col.group)
-
- for (j in 1:length(topgenes)) {
- lfc <- round(top[topgenes[j], "logFC"], 2)
- pval <- round(top[topgenes[j], "adj.P.Val"], 5)
- if (wantTrend) {
- stripchart(plotData[topgenes[j], ] ~ factors[, 1], vertical=TRUE, las=2, pch=16, cex=0.8, cex.lab=0.8, col=cols,
- method="jitter", ylab="Normalised log2 expression", main=paste0(labels[j], "\nlogFC=", lfc, ", adj.P.Val=", pval))
- } else {
- stripchart(plotData$E[topgenes[j], ] ~ factors[, 1], vertical=TRUE, las=2, pch=16, cex=0.8, cex.lab=0.8, col=cols,
- method="jitter", ylab="Normalised log2 expression", main=paste0(labels[j], "\nlogFC=", lfc, ", adj.P.Val=", pval))
- }
- }
- linkName <- paste0("Stripcharts_", con, ".pdf")
- linkAddr <- paste0("stripcharts_", con, ".pdf")
- linkData <- rbind(linkData, c(linkName, linkAddr))
- invisible(dev.off())
- }
-}
-sigDiff <- data.frame(Up=upCount, Flat=flatCount, Down=downCount)
-row.names(sigDiff) <- contrastData
-
-# Save relevant items as rda object
-if (wantRda) {
- print("Saving RData")
- if (wantWeight) {
- save(counts, data, y, status, plotData, labels, factors, wts, fit, top, contrastData, contrasts, design,
- file=rdaOut, ascii=TRUE)
- } else {
- save(counts, data, y, status, plotData, labels, factors, fit, top, contrastData, contrasts, design,
- file=rdaOut, ascii=TRUE)
- }
- linkData <- rbind(linkData, c((paste0(deMethod, "_analysis.RData")), (paste0(deMethod, "_analysis.RData"))))
-}
-
-# Record session info
-writeLines(capture.output(sessionInfo()), sessionOut)
-linkData <- rbind(linkData, c("Session Info", "session_info.txt"))
-
-# Record ending time and calculate total run time
-timeEnd <- as.character(Sys.time())
-timeTaken <- capture.output(round(difftime(timeEnd,timeStart), digits=3))
-timeTaken <- gsub("Time difference of ", "", timeTaken, fixed=TRUE)
-################################################################################
-### HTML Generation
-################################################################################
-
-# Clear file
-cat("", file=opt$htmlPath)
-
-cata("\n")
-
-cata("\n")
-cata("Limma Analysis Output:
\n")
-cata("Links to PDF copies of plots are in 'Plots' section below
\n")
-
-for (i in 1:nrow(imageData)) {
- if (grepl("density|box|mds|mdvol", imageData$Link[i])) {
- HtmlImage(imageData$Link[i], imageData$Label[i], width=1000)
- } else if (wantWeight) {
- HtmlImage(imageData$Link[i], imageData$Label[i], width=1000)
- } else {
- HtmlImage(imageData$Link[i], imageData$Label[i])
- }
-}
-
-cata("Differential Expression Counts:
\n")
-
-cata("\n")
-cata("\n")
-TableItem()
-for (i in colnames(sigDiff)) {
- TableHeadItem(i)
-}
-cata("
\n")
-for (i in 1:nrow(sigDiff)) {
- cata("\n")
- TableHeadItem(unmake.names(row.names(sigDiff)[i]))
- for (j in 1:ncol(sigDiff)) {
- TableItem(as.character(sigDiff[i, j]))
- }
- cata("
\n")
-}
-cata("
")
-
-cata("Plots:
\n")
-#PDFs
-for (i in 1:nrow(linkData)) {
- if (grepl("density|cpm|boxplot|mds|mdplots|voomplot|saplot", linkData$Link[i])) {
- HtmlLink(linkData$Link[i], linkData$Label[i])
- }
-}
-
-for (i in 1:nrow(linkData)) {
- if (grepl("mdplot_", linkData$Link[i])) {
- HtmlLink(linkData$Link[i], linkData$Label[i])
- }
-}
-
-for (i in 1:nrow(linkData)) {
- if (grepl("volplot", linkData$Link[i])) {
- HtmlLink(linkData$Link[i], linkData$Label[i])
- }
-}
-
-for (i in 1:nrow(linkData)) {
- if (grepl("heatmap", linkData$Link[i])) {
- HtmlLink(linkData$Link[i], linkData$Label[i])
- }
-}
-
-for (i in 1:nrow(linkData)) {
- if (grepl("stripcharts", linkData$Link[i])) {
- HtmlLink(linkData$Link[i], linkData$Label[i])
- }
-}
-
-cata("Tables:
\n")
-for (i in 1:nrow(linkData)) {
- if (grepl(".tsv", linkData$Link[i])) {
- HtmlLink(linkData$Link[i], linkData$Label[i])
- }
-}
-
-if (wantRda) {
- cata("R Data Object:
\n")
- for (i in 1:nrow(linkData)) {
- if (grepl(".RData", linkData$Link[i])) {
- HtmlLink(linkData$Link[i], linkData$Label[i])
- }
- }
-}
-
-cata("Alt-click links to download file.
\n")
-cata("Click floppy disc icon associated history item to download ")
-cata("all files.
\n")
-cata(".tsv files can be viewed in Excel or any spreadsheet program.
\n")
-
-cata("Additional Information
\n")
-cata("\n")
-
-if (filtCPM || filtSmpCount || filtTotCount) {
- if (filtCPM) {
- tempStr <- paste("Genes without more than", opt$cpmReq,
- "CPM in at least", opt$sampleReq, "samples are insignificant",
- "and filtered out.")
- } else if (filtSmpCount) {
- tempStr <- paste("Genes without more than", opt$cntReq,
- "counts in at least", opt$sampleReq, "samples are insignificant",
- "and filtered out.")
- } else if (filtTotCount) {
- tempStr <- paste("Genes without more than", opt$cntReq,
- "counts, after summing counts for all samples, are insignificant",
- "and filtered out.")
- }
-
- ListItem(tempStr)
- filterProp <- round(filteredCount/preFilterCount*100, digits=2)
- tempStr <- paste0(filteredCount, " of ", preFilterCount," (", filterProp,
- "%) genes were filtered out for low expression.")
- ListItem(tempStr)
-}
-ListItem(opt$normOpt, " was the method used to normalise library sizes.")
-if (wantTrend) {
- ListItem("The limma-trend method was used.")
-} else {
- ListItem("The limma-voom method was used.")
-}
-if (wantWeight) {
- ListItem("Weights were applied to samples.")
-} else {
- ListItem("Weights were not applied to samples.")
-}
-if (wantTreat) {
- ListItem(paste("Testing significance relative to a fold-change threshold (TREAT) was performed using a threshold of log2 =", opt$lfcReq, "at FDR of", opt$pValReq, "."))
-}
-if (wantRobust) {
- if (wantTreat) {
- ListItem("TREAT was used with robust settings (robust=TRUE).")
- } else {
- ListItem("eBayes was used with robust settings (robust=TRUE).")
- }
-}
-if (opt$pAdjOpt!="none") {
- if (opt$pAdjOpt=="BH" || opt$pAdjOpt=="BY") {
- tempStr <- paste0("MD Plot highlighted genes are significant at FDR ",
- "of ", opt$pValReq," and exhibit log2-fold-change of at ",
- "least ", opt$lfcReq, ".")
- ListItem(tempStr)
- } else if (opt$pAdjOpt=="holm") {
- tempStr <- paste0("MD Plot highlighted genes are significant at adjusted ",
- "p-value of ", opt$pValReq," by the Holm(1979) ",
- "method, and exhibit log2-fold-change of at least ",
- opt$lfcReq, ".")
- ListItem(tempStr)
- }
- } else {
- tempStr <- paste0("MD Plot highlighted genes are significant at p-value ",
- "of ", opt$pValReq," and exhibit log2-fold-change of at ",
- "least ", opt$lfcReq, ".")
- ListItem(tempStr)
-}
-cata("
\n")
-
-cata("Summary of experimental data:
\n")
-
-cata("*CHECK THAT SAMPLES ARE ASSOCIATED WITH CORRECT GROUP(S)*
\n")
-
-cata("\n")
-cata("\n")
-TableHeadItem("SampleID")
-TableHeadItem(names(factors)[1]," (Primary Factor)")
-
-if (ncol(factors) > 1) {
- for (i in names(factors)[2:length(names(factors))]) {
- TableHeadItem(i)
- }
- cata("
\n")
-}
-
-for (i in 1:nrow(factors)) {
- cata("\n")
- TableHeadItem(row.names(factors)[i])
- for (j in 1:ncol(factors)) {
- TableItem(as.character(unmake.names(factors[i, j])))
- }
- cata("
\n")
-}
-cata("
")
-
-cit <- character()
-link <- character()
-link[1] <- paste0("", "limma User's Guide", ".")
-
-link[2] <- paste0("", "edgeR User's Guide", "")
-
-cit[1] <- paste("Please cite the following paper for this tool:")
-
-cit[2] <- paste("Liu R, Holik AZ, Su S, Jansz N, Chen K, Leong HS, Blewitt ME,",
- "Asselin-Labat ML, Smyth GK, Ritchie ME (2015). Why weight? ",
- "Modelling sample and observational level variability improves power ",
- "in RNA-seq analyses. Nucleic Acids Research, 43(15), e97.")
-
-cit[3] <- paste("Please cite the paper below for the limma software itself.",
- "Please also try to cite the appropriate methodology articles",
- "that describe the statistical methods implemented in limma,",
- "depending on which limma functions you are using. The",
- "methodology articles are listed in Section 2.1 of the",
- link[1],
- "Cite no. 3 only if sample weights were used.")
-cit[4] <- paste("Smyth GK (2005). Limma: linear models for microarray data.",
- "In: 'Bioinformatics and Computational Biology Solutions using",
- "R and Bioconductor'. R. Gentleman, V. Carey, S. doit,.",
- "Irizarry, W. Huber (eds), Springer, New York, pages 397-420.")
-cit[5] <- paste("Please cite the first paper for the software itself and the",
- "other papers for the various original statistical methods",
- "implemented in edgeR. See Section 1.2 in the", link[2],
- "for more detail.")
-cit[6] <- paste("Robinson MD, McCarthy DJ and Smyth GK (2010). edgeR: a",
- "Bioconductor package for differential expression analysis",
- "of digital gene expression data. Bioinformatics 26, 139-140")
-cit[7] <- paste("Robinson MD and Smyth GK (2007). Moderated statistical tests",
- "for assessing differences in tag abundance. Bioinformatics",
- "23, 2881-2887")
-cit[8] <- paste("Robinson MD and Smyth GK (2008). Small-sample estimation of",
- "negative binomial dispersion, with applications to SAGE data.",
- "Biostatistics, 9, 321-332")
-cit[9] <- paste("McCarthy DJ, Chen Y and Smyth GK (2012). Differential",
- "expression analysis of multifactor RNA-Seq experiments with",
- "respect to biological variation. Nucleic Acids Research 40,",
- "4288-4297")
-cit[10] <- paste("Law CW, Chen Y, Shi W, and Smyth GK (2014). Voom:",
- "precision weights unlock linear model analysis tools for",
- "RNA-seq read counts. Genome Biology 15, R29.")
-cit[11] <- paste("Ritchie ME, Diyagama D, Neilson J, van Laar R,",
- "Dobrovic A, Holloway A and Smyth GK (2006).",
- "Empirical array quality weights for microarray data.",
- "BMC Bioinformatics 7, Article 261.")
-cata("Citations
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-cata("
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-cata("Please report problems or suggestions to: su.s@wehi.edu.au
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-
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-TableItem("Task run time:"); TableItem(timeTaken)
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