# HG changeset patch
# User iuc
# Date 1546684353 18000
# Node ID 4730985c816ff0707cce117d437035351dd746bd
# Parent  d79ed3ec25fe162ec1cd68e800fd07de5f73a379
planemo upload for repository https://github.com/galaxyproject/tools-iuc/tree/master/tools/edger commit 230fc767e2e402ee460440afab0e348f2ccab179

diff -r d79ed3ec25fe -r 4730985c816f edger.R
--- a/edger.R	Sun May 06 13:38:41 2018 -0400
+++ b/edger.R	Sat Jan 05 05:32:33 2019 -0500
@@ -268,7 +268,9 @@
 
     # Process factors
     if (is.null(opt$factInput)) {
-            factorData <- read.table(opt$factFile, header=TRUE, sep="\t")
+            factorData <- read.table(opt$factFile, header=TRUE, sep="\t", strip.white=TRUE)
+            # order samples as in counts matrix
+            factorData <- factorData[match(colnames(counts), factorData[, 1]), ]
             factors <- factorData[, -1, drop=FALSE]
     }  else {
             factors <- unlist(strsplit(opt$factInput, "|", fixed=TRUE))
diff -r d79ed3ec25fe -r 4730985c816f edger.xml
--- a/edger.xml	Sun May 06 13:38:41 2018 -0400
+++ b/edger.xml	Sat Jan 05 05:32:33 2019 -0500
@@ -1,16 +1,16 @@
-<tool id="edger" name="edgeR" version="3.20.7.2">
+<tool id="edger" name="edgeR" version="3.22.5">
     <description>
         Perform differential expression of count data
     </description>
 
     <requirements>
-        <requirement type="package" version="3.20.7">bioconductor-edger</requirement>
-        <requirement type="package" version="3.34.9">bioconductor-limma</requirement>
-        <requirement type="package" version="0.2.15">r-rjson</requirement>
-        <requirement type="package" version="1.20.0">r-getopt</requirement>
+        <requirement type="package" version="3.22.5">bioconductor-edger</requirement>
+        <requirement type="package" version="3.36.5">bioconductor-limma</requirement>
+        <requirement type="package" version="0.2.20">r-rjson</requirement>
+        <requirement type="package" version="1.20.2">r-getopt</requirement>
         <requirement type="package" version="1.4.30">r-statmod</requirement>
         <!-- required for alpha function used with plotMD -->
-        <requirement type="package" version="0.5.0">r-scales</requirement>
+        <requirement type="package" version="1.0.0">r-scales</requirement>
     </requirements>
 
     <version_command><![CDATA[
@@ -351,7 +351,11 @@
                     <has_text text="RData" />
                 </assert_contents>
             </output>
-            <output name="rscript" value="out_rscript.txt"/>
+            <output name="rscript">
+                <assert_contents>
+                    <has_text_matching expression="Task run time" />
+                </assert_contents>
+            </output>
         </test>
         <!-- Ensure secondary factors work -->
         <test>
@@ -378,7 +382,7 @@
                </element>
             </output_collection>
         </test>
-        <!-- Ensure factors file input works -->
+        <!-- Ensure factors file with unordered samples works -->
         <test>
             <param name="format" value="matrix" />
             <param name="ffile" value="yes" />
@@ -659,26 +663,6 @@
     11305      2528    2438    2493    1762     1942     2027
     ========== ======= ======= ======= ======== ======== ========
 
-**Factor Information:**
-Enter factor names and groups in the tool form, or provide a tab-separated file that has the samples in the same order as listed in the columns of the counts matrix. The second column should contain the primary factor levels (e.g. WT, Mut) with optional additional columns for any secondary factors.
-
-Example:
-
-    ========== ============ =========
-    **Sample** **Genotype** **Batch**
-    ---------- ------------ ---------
-    WT1        WT           b1
-    WT2        WT           b2
-    WT3        WT           b3
-    Mut1       Mut          b1
-    Mut2       Mut          b2
-    Mut3       Mut          b3
-    ========== ============ =========
-
-*Factor Name:* The name of the experimental factor being investigated e.g. Genotype, Treatment. One factor must be entered and spaces must not be used. Optionally, additional factors can be included, these are variables that might influence your experiment e.g. Batch, Gender, Subject. If additional factors are entered, edgeR will fit an additive linear model.
-
-*Groups:* The names of the groups for the factor. These must be entered in the same order as the samples (to which the groups correspond) are listed in the columns of the counts matrix. Spaces must not be used and if entered into the tool form above, the values should be separated by commas.
-
 
 **Gene Annotations:**
 Optional input for gene annotations, this can contain more
@@ -698,6 +682,27 @@
     11305        Abca2       ATP-binding cassette, sub-family A (ABC1), member 2
     ==========  ==========  ===================================================
 
+**Factor Information:**
+Enter factor names and groups in the tool form, or provide a tab-separated file that has the names of the samples in the first column and one header row. The sample names must be the same as the names in the columns of the count matrix. The second column should contain the primary factor levels (e.g. WT, Mut) with optional additional columns for any secondary factors.
+
+Example:
+
+    ========== ============ =========
+    **Sample** **Genotype** **Batch**
+    ---------- ------------ ---------
+    WT1        WT           b1
+    WT2        WT           b2
+    WT3        WT           b3
+    Mut1       Mut          b1
+    Mut2       Mut          b2
+    Mut3       Mut          b3
+    ========== ============ =========
+
+*Factor Name:* The name of the experimental factor being investigated e.g. Genotype, Treatment. One factor must be entered and spaces must not be used. Optionally, additional factors can be included, these are variables that might influence your experiment e.g. Batch, Gender, Subject. If additional factors are entered, an additive linear model will be used.
+
+*Groups:* The names of the groups for the factor. These must be entered in the same order as the samples (to which the groups correspond) are listed in the columns of the counts matrix. Spaces must not be used and if entered into the tool form above, the values should be separated by commas.
+
+
 **Contrasts of Interest:**
 The contrasts you wish to make between levels.
 A common contrast would be a simple difference between two levels: "Mut-WT"
diff -r d79ed3ec25fe -r 4730985c816f test-data/factorinfo.txt
--- a/test-data/factorinfo.txt	Sun May 06 13:38:41 2018 -0400
+++ b/test-data/factorinfo.txt	Sat Jan 05 05:32:33 2019 -0500
@@ -1,7 +1,7 @@
 Sample	Genotype	Batch
-Mut1	Mut	b1
+WT3	WT	b3
 Mut2	Mut	b2
 Mut3	Mut	b3
 WT1	WT	b1
 WT2	WT	b2
-WT3	WT	b3
+Mut1	Mut	b1
diff -r d79ed3ec25fe -r 4730985c816f test-data/out_rscript.txt
--- a/test-data/out_rscript.txt	Sun May 06 13:38:41 2018 -0400
+++ /dev/null	Thu Jan 01 00:00:00 1970 +0000
@@ -1,748 +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 
-#       lrtOpt", "t", 0, "logical"          -String specifying whether to perform LRT test instead 
-#
-# OUT: 
-#       MDS Plot 
-#       BCV Plot
-#       QL Plot
-#       MD Plot
-#       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 - Oct 2017 (some code taken from the DESeq2 wrapper)
-
-# Record starting time
-timeStart <- as.character(Sys.time())
-
-# setup R error handling to go to stderr
-options( show.error.messages=F, error = function () { cat( geterrmessage(), file=stderr() ); q( "no", 1, F ) } )
-
-# we need that to not crash galaxy with an UTF8 error on German LC settings.
-loc <- Sys.setlocale("LC_MESSAGES", "en_US.UTF-8")
-
-# 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)
-
-################################################################################
-### Function Delcaration
-################################################################################
-# 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("<head>\n")
-    cata("<title>", title, "</title>\n")
-    cata("</head>\n")
-}
-
-# Function to write code for html links
-HtmlLink <- function(address, label=address) {
-    cata("<a href=\"", address, "\" target=\"_blank\">", label, "</a><br />\n")
-}
-
-# Function to write code for html images
-HtmlImage <- function(source, label=source, height=600, width=600) {
-    cata("<img src=\"", source, "\" alt=\"", label, "\" height=\"", height)
-    cata("\" width=\"", width, "\"/>\n")
-}
-
-# Function to write code for html list items
-ListItem <- function(...) {
-    cata("<li>", ..., "</li>\n")
-}
-
-TableItem <- function(...) {
-    cata("<td>", ..., "</td>\n")
-}
-
-TableHeadItem <- function(...) {
-    cata("<th>", ..., "</th>\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",
-    "lrtOpt", "t", 0, "logical"),
-    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$totReq)) {
-    filtTotCount <- FALSE
-} else {
-    filtTotCount <- TRUE
-}
-
-if (is.null(opt$lrtOpt)) {
-    wantLRT <- FALSE
-} else {
-    wantLRT <- 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$filesPath)) {
-    # Process the separate count files (adapted from DESeq2 wrapper)
-    library("rjson")
-    parser <- newJSONParser()
-    parser$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 separated by comma into a vector then sanitise
-contrastData <- unlist(strsplit(opt$contrastData, split=","))
-contrastData <- sanitiseEquation(contrastData)
-contrastData <- gsub(" ", ".", contrastData, fixed=TRUE)
-
-bcvOutPdf <- makeOut("bcvplot.pdf")
-bcvOutPng <- makeOut("bcvplot.png")
-qlOutPdf <- makeOut("qlplot.pdf")
-qlOutPng <- makeOut("qlplot.png")
-mdsOutPdf <- character()   # Initialise character vector
-mdsOutPng <- character()
-for (i in 1:ncol(factors)) {
-    mdsOutPdf[i] <- makeOut(paste0("mdsplot_", names(factors)[i], ".pdf"))
-    mdsOutPng[i] <- makeOut(paste0("mdsplot_", names(factors)[i], ".png"))
-}
-mdOutPdf <- character()
-mdOutPng <- character()
-topOut <- character()
-for (i in 1:length(contrastData)) {
-    mdOutPdf[i] <- makeOut(paste0("mdplot_", contrastData[i], ".pdf"))
-    mdOutPng[i] <- makeOut(paste0("mdplot_", contrastData[i], ".png"))
-    topOut[i] <- makeOut(paste0("edgeR_", contrastData[i], ".tsv"))
-}   # Save output paths for each contrast as vectors
-normOut <- makeOut("edgeR_normcounts.tsv")
-rdaOut <- makeOut("edgeR_analysis.RData")
-sessionOut <- makeOut("session_info.txt")
-
-# Initialise data for html links and images, data frame with columns Label and 
-# Link
-linkData <- data.frame(Label=character(), Link=character(), stringsAsFactors=FALSE)
-imageData <- data.frame(Label=character(), Link=character(), stringsAsFactors=FALSE)
-
-# Initialise vectors for storage of up/down/neutral regulated counts
-upCount <- numeric()
-downCount <- numeric()
-flatCount <- numeric()
-
-################################################################################
-### Data Processing
-################################################################################
-
-# Extract counts and annotation data
-data <- list()
-data$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))
-}
-
-# 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$counts)
-
-if (filtCPM || filtSmpCount || filtTotCount) {
-
-    if (filtTotCount) {
-        keep <- rowSums(data$counts) >= opt$cntReq
-    } else if (filtSmpCount) {
-        keep <- rowSums(data$counts >= opt$cntReq) >= opt$sampleReq
-    } else if (filtCPM) {
-        keep <- rowSums(cpm(data$counts) >= opt$cpmReq) >= opt$sampleReq
-    }
-
-    data$counts <- data$counts[keep, ]
-    data$genes <- data$genes[keep, , drop=FALSE]
-}
-
-postFilterCount <- nrow(data$counts)
-filteredCount <- preFilterCount-postFilterCount
-
-# Creating naming data
-samplenames <- colnames(data$counts)
-sampleanno <- data.frame("sampleID"=samplenames, factors)
-
-
-# Generating the DGEList object "data"
-data$samples <- sampleanno
-data$samples$lib.size <- colSums(data$counts)
-data$samples$norm.factors <- 1
-row.names(data$samples) <- colnames(data$counts)
-data <- new("DGEList", data)
-
-# Name rows of factors according to their sample
-row.names(factors) <- names(data$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 factor, estimating dispersion
-data <- calcNormFactors(data, method=opt$normOpt)
-
-if (wantRobust) {
-    data <- estimateDisp(data, design=design, robust=TRUE)
-} else {
-    data <- estimateDisp(data, design=design)
-}
-
-# Generate contrasts information
-contrasts <- makeContrasts(contrasts=contrastData, levels=design)
-
-################################################################################
-### Data Output
-################################################################################
-
-# Plot MDS
-labels <- names(counts)
-
-# MDS plot
-png(mdsOutPng, width=600, height=600)
-plotMDS(data, labels=labels, col=as.numeric(factors[, 1]), cex=0.8, main=paste("MDS Plot:", names(factors)[1]))
-imgName <- paste0("MDS Plot_", names(factors)[1], ".png")
-imgAddr <- paste0("mdsplot_", names(factors)[1], ".png")
-imageData[1, ] <- c(imgName, imgAddr)
-invisible(dev.off())
-
-pdf(mdsOutPdf)
-plotMDS(data, labels=labels, col=as.numeric(factors[, 1]), cex=0.8, main=paste("MDS Plot:", names(factors)[1]))
-linkName <- paste0("MDS Plot_", names(factors)[1], ".pdf")
-linkAddr <- paste0("mdsplot_", names(factors)[1], ".pdf")
-linkData[1, ] <- c(linkName, linkAddr)
-invisible(dev.off())
-
-# If additional factors create additional MDS plots coloured by factor
-if (ncol(factors) > 1) {
-    for (i in 2:ncol(factors)) {
-        png(mdsOutPng[i], width=600, height=600)
-        plotMDS(data, labels=labels, col=as.numeric(factors[, i]), cex=0.8, main=paste("MDS Plot:", names(factors)[i]))
-        imgName <- paste0("MDS Plot_", names(factors)[i], ".png")
-        imgAddr <- paste0("mdsplot_", names(factors)[i], ".png")
-        imageData <- rbind(imageData, c(imgName, imgAddr))
-        invisible(dev.off())
-
-        pdf(mdsOutPdf[i])
-        plotMDS(data, labels=labels, col=as.numeric(factors[, i]), cex=0.8, main=paste("MDS Plot:", names(factors)[i]))
-        linkName <- paste0("MDS Plot_", names(factors)[i], ".pdf")
-        linkAddr <- paste0("mdsplot_", names(factors)[i], ".pdf")
-        linkData <- rbind(linkData, c(linkName, linkAddr))
-        invisible(dev.off())
-    }
-}
-
-# BCV Plot
-png(bcvOutPng, width=600, height=600)
-plotBCV(data, main="BCV Plot")
-imgName <- "BCV Plot"
-imgAddr <- "bcvplot.png"
-imageData <- rbind(imageData, c(imgName, imgAddr))
-invisible(dev.off())
-
-pdf(bcvOutPdf)
-plotBCV(data, main="BCV Plot")
-linkName <- paste0("BCV Plot.pdf")
-linkAddr <- paste0("bcvplot.pdf")
-linkData <- rbind(linkData, c(linkName, linkAddr))
-invisible(dev.off())
-
-# Generate fit
-if (wantLRT) {
-    
-    fit <- glmFit(data, design)
-    
-} else {
-    
-    if (wantRobust) {
-        fit <- glmQLFit(data, design, robust=TRUE)
-    } else {
-        fit <- glmQLFit(data, design)
-    }
-
-    # Plot QL dispersions
-    png(qlOutPng, width=600, height=600)
-    plotQLDisp(fit, main="QL Plot")
-    imgName <- "QL Plot"
-    imgAddr <- "qlplot.png"
-    imageData <- rbind(imageData, c(imgName, imgAddr))
-    invisible(dev.off())
-
-    pdf(qlOutPdf)
-    plotQLDisp(fit, main="QL Plot")
-    linkName <- "QL Plot.pdf"
-    linkAddr <- "qlplot.pdf"
-    linkData <- rbind(linkData, c(linkName, linkAddr))
-    invisible(dev.off())
-}
-
- # Save normalised counts (log2cpm)
-if (wantNorm) { 
-        normalisedCounts <- cpm(data, normalized.lib.sizes=TRUE, log=TRUE) 
-        normalisedCounts <- data.frame(data$genes, normalisedCounts)
-        write.table (normalisedCounts, file=normOut, row.names=FALSE, sep="\t", quote=FALSE)
-        linkData <- rbind(linkData, c("edgeR_normcounts.tsv", "edgeR_normcounts.tsv"))
-}
-
-
-for (i in 1:length(contrastData)) {
-    if (wantLRT) {
-        res <- glmLRT(fit, contrast=contrasts[, i])
-    } else {
-        res <- glmQLFTest(fit, contrast=contrasts[, i])
-    }
-
-    status = decideTestsDGE(res, adjust.method=opt$pAdjOpt, p.value=opt$pValReq,
-                                             lfc=opt$lfcReq)
-    sumStatus <- summary(status)
-
-    # Collect counts for differential expression
-    upCount[i] <- sumStatus["Up", ]
-    downCount[i] <- sumStatus["Down", ]
-    flatCount[i] <- sumStatus["NotSig", ]
-                                             
-    # Write top expressions table
-    top <- topTags(res, n=Inf, sort.by="PValue")
-    write.table(top, file=topOut[i], row.names=FALSE, sep="\t", quote=FALSE)
-    
-    linkName <- paste0("edgeR_", contrastData[i], ".tsv")
-    linkAddr <- paste0("edgeR_", contrastData[i], ".tsv")
-    linkData <- rbind(linkData, c(linkName, linkAddr))
-    
-    # Plot MD (log ratios vs mean difference) using limma package
-    pdf(mdOutPdf[i])
-    limma::plotMD(res, status=status,
-                                main=paste("MD Plot:", unmake.names(contrastData[i])), 
-                                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("MD Plot_", contrastData[i], ".pdf")
-    linkAddr <- paste0("mdplot_", contrastData[i], ".pdf")
-    linkData <- rbind(linkData, c(linkName, linkAddr))
-    invisible(dev.off())
-    
-    png(mdOutPng[i], height=600, width=600)
-    limma::plotMD(res, status=status,
-                                main=paste("MD Plot:", unmake.names(contrastData[i])), 
-                                hl.col=alpha(c("firebrick", "blue"), 0.4), values=c(1, -1),
-                                xlab="Average Expression", ylab="logFC")
-    
-    abline(h=0, col="grey", lty=2)
-    
-    imgName <- paste0("MD Plot_", contrastData[i], ".png")
-    imgAddr <- paste0("mdplot_", contrastData[i], ".png")
-    imageData <- rbind(imageData, c(imgName, imgAddr))
-    invisible(dev.off())
-}
-sigDiff <- data.frame(Up=upCount, Flat=flatCount, Down=downCount)
-row.names(sigDiff) <- contrastData
-
-# Save relevant items as rda object
-if (wantRda) {
-    if (wantNorm) {
-        save(counts, data, status, normalisedCounts, labels, factors, fit, res, top, contrasts, design,
-                 file=rdaOut, ascii=TRUE)
-    } else {
-        save(counts, data, status, labels, factors, fit, res, top, contrasts, design,
-                 file=rdaOut, ascii=TRUE)
-    }
-    linkData <- rbind(linkData, c("edgeR_analysis.RData", "edgeR_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("<html>\n")
-
-cata("<body>\n")
-cata("<h3>edgeR Analysis Output:</h3>\n")
-cata("Links to PDF copies of plots are in 'Plots' section below.<br />\n")
-
-HtmlImage(imageData$Link[1], imageData$Label[1])
-
-for (i in 2:nrow(imageData)) {
-    HtmlImage(imageData$Link[i], imageData$Label[i])
-}
-
-cata("<h4>Differential Expression Counts:</h4>\n")
-
-cata("<table border=\"1\" cellpadding=\"4\">\n")
-cata("<tr>\n")
-TableItem()
-for (i in colnames(sigDiff)) {
-    TableHeadItem(i)
-}
-cata("</tr>\n")
-for (i in 1:nrow(sigDiff)) {
-    cata("<tr>\n")
-    TableHeadItem(unmake.names(row.names(sigDiff)[i]))
-    for (j in 1:ncol(sigDiff)) {
-        TableItem(as.character(sigDiff[i, j]))
-    }
-    cata("</tr>\n")
-}
-cata("</table>")
-
-cata("<h4>Plots:</h4>\n")
-for (i in 1:nrow(linkData)) {
-    if (grepl(".pdf", linkData$Link[i])) {
-        HtmlLink(linkData$Link[i], linkData$Label[i])
-    }
-}
-
-cata("<h4>Tables:</h4>\n")
-for (i in 1:nrow(linkData)) {
-    if (grepl(".tsv", linkData$Link[i])) {
-        HtmlLink(linkData$Link[i], linkData$Label[i])
-    }
-}
-
-if (wantRda) {
-    cata("<h4>R Data Objects:</h4>\n")
-    for (i in 1:nrow(linkData)) {
-        if (grepl(".RData", linkData$Link[i])) {
-            HtmlLink(linkData$Link[i], linkData$Label[i])
-        }
-    }
-}
-
-cata("<p>Alt-click links to download file.</p>\n")
-cata("<p>Click floppy disc icon associated history item to download ")
-cata("all files.</p>\n")
-cata("<p>.tsv files can be viewed in Excel or any spreadsheet program.</p>\n")
-
-cata("<h4>Additional Information</h4>\n")
-cata("<ul>\n")
-
-if (filtCPM || filtSmpCount || filtTotCount) {
-    if (filtCPM) {
-    tempStr <- paste("Genes without more than", opt$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$normOpt, " was the method used to normalise library sizes.")
-if (wantLRT) {
-    ListItem("The edgeR likelihood ratio test was used.")
-} else {
-    if (wantRobust) {
-        ListItem("The edgeR quasi-likelihood test was used with robust settings (robust=TRUE with estimateDisp and glmQLFit).")
-    } else {
-            ListItem("The edgeR quasi-likelihood test was used.")
-    }
-}
-if (opt$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$lfcReq, ".")
-    ListItem(tempStr)
-}
-cata("</ul>\n")
-
-cata("<h4>Summary of experimental data:</h4>\n")
-
-cata("<p>*CHECK THAT SAMPLES ARE ASSOCIATED WITH CORRECT GROUP(S)*</p>\n")
-
-cata("<table border=\"1\" cellpadding=\"3\">\n")
-cata("<tr>\n")
-TableHeadItem("SampleID")
-TableHeadItem(names(factors)[1], " (Primary Factor)")
-
-    if (ncol(factors) > 1) {
-        for (i in names(factors)[2:length(names(factors))]) {
-            TableHeadItem(i)
-        }
-        cata("</tr>\n")
-    }
-
-for (i in 1:nrow(factors)) {
-    cata("<tr>\n")
-    TableHeadItem(row.names(factors)[i])
-    for (j in 1:ncol(factors)) {
-        TableItem(as.character(unmake.names(factors[i, j])))
-    }
-    cata("</tr>\n")
-}
-cata("</table>")
-
-for (i in 1:nrow(linkData)) {
-    if (grepl("session_info", linkData$Link[i])) {
-        HtmlLink(linkData$Link[i], linkData$Label[i])
-    }
-}
-
-cata("<table border=\"0\">\n")
-cata("<tr>\n")
-TableItem("Task started at:"); TableItem(timeStart)
-cata("</tr>\n")
-cata("<tr>\n")
-TableItem("Task ended at:"); TableItem(timeEnd)
-cata("</tr>\n")
-cata("<tr>\n")
-TableItem("Task run time:"); TableItem(timeTaken)
-cata("<tr>\n")
-cata("</table>\n")
-
-cata("</body>\n")
-cata("</html>")