# HG changeset patch
# User iuc
# Date 1622748858 0
# Node ID 3d89af8a44f05e109b991528d0d49764fdc539c4
# Parent 334ce9b1bac5c18a32aa263fa195bdb541662928
"planemo upload for repository https://github.com/galaxyproject/tools-iuc/tree/master/tools/edger commit 215a0f27f3de87506895ac655f801c40e8c7edbc"
diff -r 334ce9b1bac5 -r 3d89af8a44f0 edger.R
--- a/edger.R Thu Aug 08 06:44:11 2019 -0400
+++ b/edger.R Thu Jun 03 19:34:18 2021 +0000
@@ -7,23 +7,23 @@
# 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
+# 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
+# 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
+# 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
+# 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
+# OUT:
+# MDS Plot
# BCV Plot
# QL Plot
# MD Plot
@@ -37,108 +37,112 @@
# Modified by: Maria Doyle - Oct 2017 (some code taken from the DESeq2 wrapper)
# Record starting time
-timeStart <- as.character(Sys.time())
+time_start <- 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 ) } )
+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)
+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+$", "", equation)
- return(equation)
+sanitise_equation <- function(equation) {
+ equation <- gsub(" *[+] *", "+", equation)
+ equation <- gsub(" *[-] *", "-", equation)
+ equation <- gsub(" *[/] *", "/", equation)
+ equation <- gsub(" *[*] *", "*", equation)
+ equation <- gsub("^\\s+|\\s+$", "", equation)
+ return(equation)
}
# Function to sanitise group information
-sanitiseGroups <- function(string) {
- string <- gsub(" *[,] *", ",", string)
- string <- gsub("^\\s+|\\s+$", "", string)
- return(string)
+sanitise_groups <- function(string) {
+ string <- gsub(" *[,] *", ",", string)
+ string <- gsub("^\\s+|\\s+$", "", string)
+ return(string)
}
# Function to change periods to whitespace in a string
-unmake.names <- function(string) {
- string <- gsub(".", " ", string, fixed=TRUE)
- return(string)
+unmake_names <- function(string) {
+ string <- gsub(".", " ", string, fixed = TRUE)
+ return(string)
}
# Generate output folder and paths
-makeOut <- function(filename) {
- return(paste0(opt$outPath, "/", filename))
+make_out <- function(filename) {
+ return(paste0(out_path, "/", filename))
}
# Generating design information
-pasteListName <- function(string) {
- return(paste0("factors$", string))
+paste_listname <- 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))
+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))
+ file <- file(file, ifelse(append, "a", "w"))
+ on.exit(close(file))
}
- .Internal(cat(list(...), file, sep, fill, labels, append))
+ }
+ .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")
+html_head <- function(title) {
+ cata("\n")
+ cata("", title, "\n")
+ cata("\n")
}
# Function to write code for html links
-HtmlLink <- function(address, label=address) {
- cata("", label, "
\n")
+html_link <- function(address, label = address) {
+ cata("", label, "
\n")
}
# Function to write code for html images
-HtmlImage <- function(source, label=source, height=600, width=600) {
- cata("![\"",](\"",)
\n")
+html_image <- function(source, label = source, height = 600, width = 600) {
+ cata("\n")
}
# Function to write code for html list items
-ListItem <- function(...) {
- cata("", ..., "\n")
+list_item <- function(...) {
+ cata("", ..., "\n")
}
-TableItem <- function(...) {
- cata("", ..., " | \n")
+table_item <- function(...) {
+ cata("", ..., " | \n")
}
-TableHeadItem <- function(...) {
- cata("", ..., " | \n")
+table_head_item <- function(...) {
+ cata("", ..., " | \n")
}
################################################################################
@@ -146,198 +150,205 @@
################################################################################
# Collect arguments from command line
-args <- commandArgs(trailingOnly=TRUE)
+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)
+ "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$filesPath)) {
- cat("A counts matrix (or a set of counts files) is required.\n")
- q(status=1)
+ cat("A counts matrix (or a set of counts files) is required.\n")
+ q(status = 1)
}
if (is.null(opt$cpmReq)) {
- filtCPM <- FALSE
+ filt_cpm <- FALSE
} else {
- filtCPM <- TRUE
+ filt_cpm <- TRUE
}
if (is.null(opt$cntReq) || is.null(opt$sampleReq)) {
- filtSmpCount <- FALSE
+ filt_smpcount <- FALSE
} else {
- filtSmpCount <- TRUE
+ filt_smpcount <- TRUE
}
if (is.null(opt$totReq)) {
- filtTotCount <- FALSE
+ filt_totcount <- FALSE
} else {
- filtTotCount <- TRUE
+ filt_totcount <- TRUE
}
if (is.null(opt$lrtOpt)) {
- wantLRT <- FALSE
+ want_lrt <- FALSE
} else {
- wantLRT <- TRUE
+ want_lrt <- TRUE
}
if (is.null(opt$rdaOpt)) {
- wantRda <- FALSE
+ want_rda <- FALSE
} else {
- wantRda <- TRUE
+ want_rda <- TRUE
}
if (is.null(opt$annoPath)) {
- haveAnno <- FALSE
+ have_anno <- FALSE
} else {
- haveAnno <- TRUE
+ have_anno <- TRUE
}
if (is.null(opt$normCounts)) {
- wantNorm <- FALSE
-} else {
- wantNorm <- TRUE
+ want_norm <- FALSE
+} else {
+ want_norm <- TRUE
}
if (is.null(opt$robOpt)) {
- wantRobust <- FALSE
+ want_robust <- FALSE
} else {
- wantRobust <- TRUE
+ want_robust <- 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)
+ # Process the separate count files (adapted from DESeq2 wrapper)
+ library("rjson")
+ parser <- newJSONParser()
+ parser$addData(opt$filesPath)
+ factor_list <- parser$getObject()
+ factors <- sapply(factor_list, function(x) x[[1]])
+ filenames_in <- unname(unlist(factor_list[[1]][[2]]))
+ sampletable <- data.frame(
+ sample = basename(filenames_in),
+ filename = filenames_in,
+ row.names = filenames_in,
+ stringsAsFactors = FALSE
+ )
+ for (factor in factor_list) {
+ factorname <- factor[[1]]
+ sampletable[[factorname]] <- character(nrow(sampletable))
+ lvls <- sapply(factor[[2]], function(x) names(x))
+ for (i in seq_along(factor[[2]])) {
+ files <- factor[[2]][[i]][[1]]
+ sampletable[files, factorname] <- lvls[i]
}
- rownames(sampleTable) <- sampleTable$sample
- rem <- c("sample","filename")
- factors <- sampleTable[, !(names(sampleTable) %in% rem), drop=FALSE]
-
- #read in count files and create single table
- countfiles <- lapply(sampleTable$filename, function(x){read.delim(x, row.names=1)})
- counts <- do.call("cbind", countfiles)
-
+ sampletable[[factorname]] <- factor(sampletable[[factorname]], levels = lvls)
+ }
+ rownames(sampletable) <- sampletable$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", strip.white=TRUE, stringsAsFactors=FALSE)
- row.names(counts) <- counts[, 1]
- counts <- counts[ , -1]
- countsRows <- nrow(counts)
+ # Process the single count matrix
+ counts <- read.table(opt$matrixPath, header = TRUE, sep = "\t", strip.white = TRUE, stringsAsFactors = FALSE)
+ row.names(counts) <- counts[, 1]
+ counts <- counts[, -1]
+ countsrows <- nrow(counts)
- # Process factors
- if (is.null(opt$factInput)) {
- factorData <- read.table(opt$factFile, header=TRUE, sep="\t", strip.white=TRUE)
- # check samples names match
- if(!any(factorData[, 1] %in% colnames(counts)))
- stop("Sample IDs in factors file and count matrix don't match")
- # order samples as in counts matrix
- factorData <- factorData[match(colnames(counts), factorData[, 1]), ]
- factors <- factorData[, -1, drop=FALSE]
- } else {
- factors <- unlist(strsplit(opt$factInput, "|", fixed=TRUE))
- 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.
+ # Process factors
+ if (is.null(opt$factInput)) {
+ factordata <- read.table(opt$factFile, header = TRUE, sep = "\t", strip.white = TRUE)
+ # check samples names match
+ if (!any(factordata[, 1] %in% colnames(counts))) {
+ stop("Sample IDs in factors file and count matrix don't match")
+ }
+ # order samples as in counts matrix
+ factordata <- factordata[match(colnames(counts), factordata[, 1]), ]
+ factors <- factordata[, -1, drop = FALSE]
+ } else {
+ factors <- unlist(strsplit(opt$factInput, "|", fixed = TRUE))
+ factordata <- list()
+ for (fact in factors) {
+ newfact <- unlist(strsplit(fact, split = "::"))
+ factordata <- rbind(factordata, newfact)
+ } # Factors have the form: FACT_NAME::LEVEL,LEVEL,LEVEL,LEVEL,... The first factor is the Primary Factor.
- # Set the row names to be the name of the factor and delete first row
- row.names(factorData) <- factorData[, 1]
- factorData <- factorData[, -1]
- factorData <- sapply(factorData, sanitiseGroups)
- factorData <- sapply(factorData, strsplit, split=",")
- factorData <- sapply(factorData, make.names)
- # Transform factor data into data frame of R factor objects
- factors <- data.frame(factorData)
- }
+ # Set the row names to be the name of the factor and delete first row
+ row.names(factordata) <- factordata[, 1]
+ factordata <- factordata[, -1]
+ factordata <- sapply(factordata, sanitise_groups)
+ factordata <- sapply(factordata, strsplit, split = ",")
+ factordata <- sapply(factordata, make.names)
+ # Transform factor data into data frame of R factor objects
+ factors <- data.frame(factordata)
+ }
}
- # if annotation file provided
-if (haveAnno) {
- geneanno <- read.table(opt$annoPath, header=TRUE, sep="\t", quote= "", strip.white=TRUE, stringsAsFactors=FALSE)
+# if annotation file provided
+if (have_anno) {
+ geneanno <- read.table(opt$annoPath, header = TRUE, sep = "\t", quote = "", strip.white = TRUE, stringsAsFactors = FALSE)
}
-#Create output directory
-dir.create(opt$outPath, showWarnings=FALSE)
+# Create output directory
+out_path <- opt$outPath
+dir.create(out_path, showWarnings = FALSE)
# Split up contrasts separated by comma into a vector then sanitise
-contrastData <- unlist(strsplit(opt$contrastData, split=","))
-contrastData <- sanitiseEquation(contrastData)
-contrastData <- gsub(" ", ".", contrastData, fixed=TRUE)
+contrast_data <- unlist(strsplit(opt$contrastData, split = ","))
+contrast_data <- sanitise_equation(contrast_data)
+contrast_data <- gsub(" ", ".", contrast_data, 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"))
+bcv_pdf <- make_out("bcvplot.pdf")
+bcv_png <- make_out("bcvplot.png")
+ql_pdf <- make_out("qlplot.pdf")
+ql_png <- make_out("qlplot.png")
+mds_pdf <- character() # Initialise character vector
+mds_png <- character()
+for (i in seq_len(ncol(factors))) {
+ mds_pdf[i] <- make_out(paste0("mdsplot_", names(factors)[i], ".pdf"))
+ mds_png[i] <- make_out(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")
+md_pdf <- character()
+md_png <- character()
+top_out <- character()
+for (i in seq_along(contrast_data)) {
+ md_pdf[i] <- make_out(paste0("mdplot_", contrast_data[i], ".pdf"))
+ md_png[i] <- make_out(paste0("mdplot_", contrast_data[i], ".png"))
+ top_out[i] <- make_out(paste0("edgeR_", contrast_data[i], ".tsv"))
+} # Save output paths for each contrast as vectors
+norm_out <- make_out("edgeR_normcounts.tsv")
+rda_out <- make_out("edgeR_analysis.RData")
+session_out <- make_out("session_info.txt")
-# Initialise data for html links and images, data frame with columns Label and
+# 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)
+link_data <- data.frame(Label = character(), Link = character(), stringsAsFactors = FALSE)
+image_data <- data.frame(Label = character(), Link = character(), stringsAsFactors = FALSE)
# Initialise vectors for storage of up/down/neutral regulated counts
-upCount <- numeric()
-downCount <- numeric()
-flatCount <- numeric()
+up_count <- numeric()
+down_count <- numeric()
+flat_count <- numeric()
################################################################################
### Data Processing
@@ -346,74 +357,71 @@
# Extract counts and annotation data
data <- list()
data$counts <- counts
-if (haveAnno) {
+if (have_anno) {
# order annotation by genes in counts (assumes gene ids are in 1st column of geneanno)
- annoord <- geneanno[match(row.names(counts), geneanno[,1]), ]
+ annoord <- geneanno[match(row.names(counts), geneanno[, 1]), ]
data$genes <- annoord
} else {
- data$genes <- data.frame(GeneID=row.names(counts))
+ 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) {
+prefilter_count <- nrow(data$counts)
- if (filtTotCount) {
- keep <- rowSums(data$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$sampleReq
- }
+if (filt_cpm || filt_smpcount || filt_totcount) {
+ if (filt_totcount) {
+ keep <- rowSums(data$counts) >= opt$cntReq
+ } else if (filt_smpcount) {
+ keep <- rowSums(data$counts >= opt$cntReq) >= opt$sampleReq
+ } else if (filt_cpm) {
+ keep <- rowSums(cpm(data$counts) >= opt$cpmReq) >= opt$sampleReq
+ }
- data$counts <- data$counts[keep, ]
- data$genes <- data$genes[keep, , drop=FALSE]
+ data$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$counts)
-data <- new("DGEList", data)
+postfilter_count <- nrow(data$counts)
+filtered_count <- prefilter_count - postfilter_count
# Name rows of factors according to their sample
row.names(factors) <- names(data$counts)
-factorList <- sapply(names(factors), pasteListName)
+factor_list <- sapply(names(factors), paste_listname)
-formula <- "~0"
-for (i in 1:length(factorList)) {
- formula <- paste(formula, factorList[i], sep="+")
+# Generating the DGEList object "data"
+samplenames <- colnames(data$counts)
+genes <- data$genes
+data <- DGEList(data$counts)
+colnames(data) <- samplenames
+data$samples <- factors
+data$genes <- genes
+
+
+
+formula <- "~0"
+for (i in seq_along(factor_list)) {
+ formula <- paste(formula, factor_list[i], sep = "+")
}
formula <- formula(formula)
design <- model.matrix(formula)
-for (i in 1:length(factorList)) {
- colnames(design) <- gsub(factorList[i], "", colnames(design), fixed=TRUE)
+for (i in seq_along(factor_list)) {
+ colnames(design) <- gsub(factor_list[i], "", colnames(design), fixed = TRUE)
}
# Calculating normalising factor, estimating dispersion
-data <- calcNormFactors(data, method=opt$normOpt)
+data <- calcNormFactors(data, method = opt$normOpt)
-if (wantRobust) {
- data <- estimateDisp(data, design=design, robust=TRUE)
+if (want_robust) {
+ data <- estimateDisp(data, design = design, robust = TRUE)
} else {
- data <- estimateDisp(data, design=design)
+ data <- estimateDisp(data, design = design)
}
# Generate contrasts information
-contrasts <- makeContrasts(contrasts=contrastData, levels=design)
+contrasts <- makeContrasts(contrasts = contrast_data, levels = design)
################################################################################
### Data Output
@@ -423,173 +431,178 @@
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)
+png(mds_png, width = 600, height = 600)
+plotMDS(data, labels = labels, col = as.numeric(factors[, 1]), cex = 0.8, main = paste("MDS Plot:", names(factors)[1]))
+img_name <- paste0("MDS Plot_", names(factors)[1], ".png")
+img_addr <- paste0("mdsplot_", names(factors)[1], ".png")
+image_data[1, ] <- c(img_name, img_addr)
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)
+pdf(mds_pdf)
+plotMDS(data, labels = labels, col = as.numeric(factors[, 1]), cex = 0.8, main = paste("MDS Plot:", names(factors)[1]))
+link_name <- paste0("MDS Plot_", names(factors)[1], ".pdf")
+link_addr <- paste0("mdsplot_", names(factors)[1], ".pdf")
+link_data[1, ] <- c(link_name, link_addr)
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())
+ for (i in 2:ncol(factors)) {
+ png(mds_png[i], width = 600, height = 600)
+ plotMDS(data, labels = labels, col = as.numeric(factors[, i]), cex = 0.8, main = paste("MDS Plot:", names(factors)[i]))
+ img_name <- paste0("MDS Plot_", names(factors)[i], ".png")
+ img_addr <- paste0("mdsplot_", names(factors)[i], ".png")
+ image_data <- rbind(image_data, c(img_name, img_addr))
+ 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())
- }
+ pdf(mds_pdf[i])
+ plotMDS(data, labels = labels, col = as.numeric(factors[, i]), cex = 0.8, main = paste("MDS Plot:", names(factors)[i]))
+ link_name <- paste0("MDS Plot_", names(factors)[i], ".pdf")
+ link_addr <- paste0("mdsplot_", names(factors)[i], ".pdf")
+ link_data <- rbind(link_data, c(link_name, link_addr))
+ 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))
+png(bcv_png, width = 600, height = 600)
+plotBCV(data, main = "BCV Plot")
+img_name <- "BCV Plot"
+img_addr <- "bcvplot.png"
+image_data <- rbind(image_data, c(img_name, img_addr))
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))
+pdf(bcv_pdf)
+plotBCV(data, main = "BCV Plot")
+link_name <- paste0("BCV Plot.pdf")
+link_addr <- paste0("bcvplot.pdf")
+link_data <- rbind(link_data, c(link_name, link_addr))
invisible(dev.off())
# Generate fit
-if (wantLRT) {
-
- fit <- glmFit(data, design)
-
+if (want_lrt) {
+ fit <- glmFit(data, design)
} else {
-
- if (wantRobust) {
- fit <- glmQLFit(data, design, robust=TRUE)
- } else {
- fit <- glmQLFit(data, design)
- }
+ if (want_robust) {
+ 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())
+ # Plot QL dispersions
+ png(ql_png, width = 600, height = 600)
+ plotQLDisp(fit, main = "QL Plot")
+ img_name <- "QL Plot"
+ img_addr <- "qlplot.png"
+ image_data <- rbind(image_data, c(img_name, img_addr))
+ 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())
+ pdf(ql_pdf)
+ plotQLDisp(fit, main = "QL Plot")
+ link_name <- "QL Plot.pdf"
+ link_addr <- "qlplot.pdf"
+ link_data <- rbind(link_data, c(link_name, link_addr))
+ invisible(dev.off())
}
- # Save normalised counts (log2cpm)
-if (wantNorm) {
- normalisedCounts <- cpm(data, normalized.lib.sizes=TRUE, log=TRUE)
- normalisedCounts <- data.frame(data$genes, normalisedCounts)
- write.table (normalisedCounts, file=normOut, row.names=FALSE, sep="\t", quote=FALSE)
- linkData <- rbind(linkData, c("edgeR_normcounts.tsv", "edgeR_normcounts.tsv"))
+# Save normalised counts (log2cpm)
+if (want_norm) {
+ normalised_counts <- cpm(data, normalized.lib.sizes = TRUE, log = TRUE)
+ normalised_counts <- data.frame(data$genes, normalised_counts)
+ write.table(normalised_counts, file = norm_out, row.names = FALSE, sep = "\t", quote = FALSE)
+ link_data <- rbind(link_data, 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])
- }
+for (i in seq_along(contrast_data)) {
+ if (want_lrt) {
+ 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$lfcReq
+ )
+ sum_status <- summary(status)
- status = decideTestsDGE(res, adjust.method=opt$pAdjOpt, p.value=opt$pValReq,
- lfc=opt$lfcReq)
- sumStatus <- summary(status)
+ # Collect counts for differential expression
+ up_count[i] <- sum_status["Up", ]
+ down_count[i] <- sum_status["Down", ]
+ flat_count[i] <- sum_status["NotSig", ]
+
+ # Write top expressions table
+ top <- topTags(res, adjust.method = opt$pAdjOpt, n = Inf, sort.by = "PValue")
+ write.table(top, file = top_out[i], row.names = FALSE, sep = "\t", quote = FALSE)
+
+ link_name <- paste0("edgeR_", contrast_data[i], ".tsv")
+ link_addr <- paste0("edgeR_", contrast_data[i], ".tsv")
+ link_data <- rbind(link_data, c(link_name, link_addr))
- # Collect counts for differential expression
- upCount[i] <- sumStatus["Up", ]
- downCount[i] <- sumStatus["Down", ]
- flatCount[i] <- sumStatus["NotSig", ]
-
- # Write top expressions table
- top <- topTags(res, adjust.method=opt$pAdjOpt, 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())
+ # Plot MD (log ratios vs mean difference) using limma package
+ pdf(md_pdf[i])
+ limma::plotMD(res,
+ status = status,
+ main = paste("MD Plot:", unmake_names(contrast_data[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)
+
+ link_name <- paste0("MD Plot_", contrast_data[i], ".pdf")
+ link_addr <- paste0("mdplot_", contrast_data[i], ".pdf")
+ link_data <- rbind(link_data, c(link_name, link_addr))
+ invisible(dev.off())
+
+ png(md_png[i], height = 600, width = 600)
+ limma::plotMD(res,
+ status = status,
+ main = paste("MD Plot:", unmake_names(contrast_data[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)
+
+ img_name <- paste0("MD Plot_", contrast_data[i], ".png")
+ img_addr <- paste0("mdplot_", contrast_data[i], ".png")
+ image_data <- rbind(image_data, c(img_name, img_addr))
+ invisible(dev.off())
}
-sigDiff <- data.frame(Up=upCount, Flat=flatCount, Down=downCount)
-row.names(sigDiff) <- contrastData
+sig_diff <- data.frame(Up = up_count, Flat = flat_count, Down = down_count)
+row.names(sig_diff) <- contrast_data
# 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"))
+if (want_rda) {
+ if (want_norm) {
+ save(counts, data, status, normalised_counts, labels, factors, fit, res, top, contrasts, design,
+ file = rda_out, ascii = TRUE
+ )
+ } else {
+ save(counts, data, status, labels, factors, fit, res, top, contrasts, design,
+ file = rda_out, ascii = TRUE
+ )
+ }
+ link_data <- rbind(link_data, c("edgeR_analysis.RData", "edgeR_analysis.RData"))
}
# Record session info
-writeLines(capture.output(sessionInfo()), sessionOut)
-linkData <- rbind(linkData, c("Session Info", "session_info.txt"))
+writeLines(capture.output(sessionInfo()), session_out)
+link_data <- rbind(link_data, 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)
+time_end <- as.character(Sys.time())
+time_taken <- capture.output(round(difftime(time_end, time_start), digits = 3))
+time_taken <- gsub("Time difference of ", "", time_taken, fixed = TRUE)
################################################################################
### HTML Generation
################################################################################
# Clear file
-cat("", file=opt$htmlPath)
+cat("", file = opt$htmlPath)
cata("\n")
@@ -597,52 +610,52 @@
cata("edgeR Analysis Output:
\n")
cata("Links to PDF copies of plots are in 'Plots' section below.
\n")
-HtmlImage(imageData$Link[1], imageData$Label[1])
+html_image(image_data$Link[1], image_data$Label[1])
-for (i in 2:nrow(imageData)) {
- HtmlImage(imageData$Link[i], imageData$Label[i])
+for (i in 2:nrow(image_data)) {
+ html_image(image_data$Link[i], image_data$Label[i])
}
cata("Differential Expression Counts:
\n")
cata("\n")
cata("\n")
-TableItem()
-for (i in colnames(sigDiff)) {
- TableHeadItem(i)
+table_item()
+for (i in colnames(sig_diff)) {
+ table_head_item(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")
+for (i in seq_len(nrow(sig_diff))) {
+ cata("\n")
+ table_head_item(unmake_names(row.names(sig_diff)[i]))
+ for (j in seq_len(ncol(sig_diff))) {
+ table_item(as.character(sig_diff[i, j]))
+ }
+ cata("
\n")
}
cata("
")
cata("Plots:
\n")
-for (i in 1:nrow(linkData)) {
- if (grepl(".pdf", linkData$Link[i])) {
- HtmlLink(linkData$Link[i], linkData$Label[i])
- }
+for (i in seq_len(nrow(link_data))) {
+ if (grepl(".pdf", link_data$Link[i])) {
+ html_link(link_data$Link[i], link_data$Label[i])
+ }
}
cata("Tables:
\n")
-for (i in 1:nrow(linkData)) {
- if (grepl(".tsv", linkData$Link[i])) {
- HtmlLink(linkData$Link[i], linkData$Label[i])
- }
+for (i in seq_len(nrow(link_data))) {
+ if (grepl(".tsv", link_data$Link[i])) {
+ html_link(link_data$Link[i], link_data$Label[i])
+ }
}
-if (wantRda) {
- cata("R Data Objects:
\n")
- for (i in 1:nrow(linkData)) {
- if (grepl(".RData", linkData$Link[i])) {
- HtmlLink(linkData$Link[i], linkData$Label[i])
- }
+if (want_rda) {
+ cata("R Data Objects:
\n")
+ for (i in seq_len(nrow(link_data))) {
+ if (grepl(".RData", link_data$Link[i])) {
+ html_link(link_data$Link[i], link_data$Label[i])
}
+ }
}
cata("Alt-click links to download file.
\n")
@@ -653,55 +666,69 @@
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$cntReq,
- "counts, after summing counts for all samples, are insignificant",
- "and filtered out.")
- }
+if (filt_cpm || filt_smpcount || filt_totcount) {
+ if (filt_cpm) {
+ temp_str <- paste(
+ "Genes without more than", opt$cpmReq,
+ "CPM in at least", opt$sampleReq, "samples are insignificant",
+ "and filtered out."
+ )
+ } else if (filt_smpcount) {
+ temp_str <- paste(
+ "Genes without more than", opt$cntReq,
+ "counts in at least", opt$sampleReq, "samples are insignificant",
+ "and filtered out."
+ )
+ } else if (filt_totcount) {
+ temp_str <- paste(
+ "Genes without more than", opt$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)
+ list_item(temp_str)
+ filter_prop <- round(filtered_count / prefilter_count * 100, digits = 2)
+ temp_str <- paste0(
+ filtered_count, " of ", prefilter_count, " (", filter_prop,
+ "%) genes were filtered out for low expression."
+ )
+ list_item(temp_str)
}
-ListItem(opt$normOpt, " was the method used to normalise library sizes.")
-if (wantLRT) {
- ListItem("The edgeR likelihood ratio test was used.")
+list_item(opt$normOpt, " was the method used to normalise library sizes.")
+if (want_lrt) {
+ list_item("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 (want_robust) {
+ list_item("The edgeR quasi-likelihood test was used with robust settings (robust=TRUE with estimateDisp and glmQLFit).")
+ } else {
+ list_item("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)
- }
+if (opt$pAdjOpt != "none") {
+ if (opt$pAdjOpt == "BH" || opt$pAdjOpt == "BY") {
+ temp_str <- paste0(
+ "MD-Plot highlighted genes are significant at FDR ",
+ "of ", opt$pValReq, " and exhibit log2-fold-change of at ",
+ "least ", opt$lfcReq, "."
+ )
+ list_item(temp_str)
+ } else if (opt$pAdjOpt == "holm") {
+ temp_str <- 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, "."
+ )
+ list_item(temp_str)
+ }
} 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)
+ temp_str <- paste0(
+ "MD-Plot highlighted genes are significant at p-value ",
+ "of ", opt$pValReq, " and exhibit log2-fold-change of at ",
+ "least ", opt$lfcReq, "."
+ )
+ list_item(temp_str)
}
cata("
\n")
@@ -711,41 +738,44 @@
cata("\n")
cata("\n")
-TableHeadItem("SampleID")
-TableHeadItem(names(factors)[1], " (Primary Factor)")
+table_head_item("SampleID")
+table_head_item(names(factors)[1], " (Primary Factor)")
- if (ncol(factors) > 1) {
- for (i in names(factors)[2:length(names(factors))]) {
- TableHeadItem(i)
- }
- cata("
\n")
- }
+if (ncol(factors) > 1) {
+ for (i in names(factors)[2:length(names(factors))]) {
+ table_head_item(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")
+for (i in seq_len(nrow((factors)))) {
+ cata("\n")
+ table_head_item(row.names(factors)[i])
+ for (j in seq_len(ncol(factors))) {
+ table_item(as.character(unmake_names(factors[i, j])))
+ }
+ cata("
\n")
}
cata("
")
-for (i in 1:nrow(linkData)) {
- if (grepl("session_info", linkData$Link[i])) {
- HtmlLink(linkData$Link[i], linkData$Label[i])
- }
+for (i in seq_len(nrow(link_data))) {
+ if (grepl("session_info", link_data$Link[i])) {
+ html_link(link_data$Link[i], link_data$Label[i])
+ }
}
cata("\n")
cata("\n")
-TableItem("Task started at:"); TableItem(timeStart)
+table_item("Task started at:")
+table_item(time_start)
cata("
\n")
cata("\n")
-TableItem("Task ended at:"); TableItem(timeEnd)
+table_item("Task ended at:")
+table_item(time_end)
cata("
\n")
cata("\n")
-TableItem("Task run time:"); TableItem(timeTaken)
+table_item("Task run time:")
+table_item(time_taken)
cata("
\n")
cata("
\n")
diff -r 334ce9b1bac5 -r 3d89af8a44f0 edger.xml
--- a/edger.xml Thu Aug 08 06:44:11 2019 -0400
+++ b/edger.xml Thu Jun 03 19:34:18 2021 +0000
@@ -1,16 +1,26 @@
-
+
Perform differential expression of count data
+
+ edger
+
+
+ topic_3308
+
+
+ operation_3563
+ operation_3223
+
- bioconductor-edger
- bioconductor-limma
+ bioconductor-edger
+ bioconductor-limma
r-rjson
- r-getopt
- r-statmod
+ r-getopt
+ r-statmod
- r-scales
+ r-scales