Mercurial > repos > iuc > limma_voom
changeset 21:58c35179ebf0 draft
"planemo upload for repository https://github.com/galaxyproject/tools-iuc/tree/master/tools/limma_voom commit 127882bd6729d92500ce2a7a51eb5f8949a4c2b5"
| author | iuc |
|---|---|
| date | Fri, 04 Jun 2021 20:37:04 +0000 |
| parents | 0921444c832d |
| children | 708348a17fa1 |
| files | limma_voom.R limma_voom.xml |
| diffstat | 2 files changed, 576 insertions(+), 565 deletions(-) [+] |
line wrap: on
line diff
--- a/limma_voom.R Wed May 29 10:31:41 2019 -0400 +++ b/limma_voom.R Fri Jun 04 20:37:04 2021 +0000 @@ -46,24 +46,24 @@ # Modified by: Maria Doyle - Jun 2017, Jan 2018, May 2018 # Record starting time -timeStart <- as.character(Sys.time()) +time_start <- 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) +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) { +sanitise_equation <- function(equation) { equation <- gsub(" *[+] *", "+", equation) equation <- gsub(" *[-] *", "-", equation) equation <- gsub(" *[/] *", "/", equation) @@ -73,33 +73,33 @@ } # Function to sanitise group information -sanitiseGroups <- function(string) { +sanitise_groups <- function(string) { string <- gsub(" *[,] *", ",", string) string <- gsub("^\\s+|\\s+$", "", string) return(string) } # Function to make contrast contain valid R names -sanitiseContrast <- function(string) { - string <- strsplit(string, split="-") +sanitise_contrast <- function(string) { + string <- strsplit(string, split = "-") string <- lapply(string, make.names) - string <- lapply(string, paste, collapse="-") + string <- lapply(string, paste, collapse = "-") return(string) } # Function to change periods to whitespace in a string -unmake.names <- function(string) { - string <- gsub(".", " ", string, fixed=TRUE) +unmake_names <- function(string) { + string <- gsub(".", " ", string, fixed = TRUE) return(string) } # Generate output folder and paths -makeOut <- function(filename) { +make_out <- function(filename) { return(paste0(opt$outPath, "/", filename)) } # Generating design information -pasteListName <- function(string) { +paste_listname <- function(string) { return(paste0("factors$", string)) } @@ -123,33 +123,33 @@ } # Function to write code for html head and title -HtmlHead <- function(title) { +html_head <- function(title) { cata("<head>\n") cata("<title>", title, "</title>\n") cata("</head>\n") } # Function to write code for html links -HtmlLink <- function(address, label=address) { +html_link <- function(address, label = address) { cata("<a href=\"", address, "\" target=\"_blank\">", label, "</a><br />\n") } # Function to write code for html images -HtmlImage <- function(source, label=source, height=500, width=500) { +html_image <- function(source, label = source, height = 500, width = 500) { cata("<img src=\"", source, "\" alt=\"", label, "\" height=\"", height) cata("\" width=\"", width, "\"/>\n") } # Function to write code for html list items -ListItem <- function(...) { +list_item <- function(...) { cata("<li>", ..., "</li>\n") } -TableItem <- function(...) { +table_item <- function(...) { cata("<td>", ..., "</td>\n") } -TableHeadItem <- function(...) { +table_head_item <- function(...) { cata("<th>", ..., "</th>\n") } @@ -158,7 +158,7 @@ ################################################################################ # Collect arguments from command line -args <- commandArgs(trailingOnly=TRUE) +args <- commandArgs(trailingOnly = TRUE) # Get options, using the spec as defined by the enclosed list. # Read the options from the default: commandArgs(TRUE). @@ -190,88 +190,88 @@ "treatOpt", "T", 0, "logical", "plots", "P", 1, "character", "libinfoOpt", "L", 0, "logical"), - byrow=TRUE, ncol=4) + byrow = TRUE, ncol = 4) opt <- getopt(spec) if (is.null(opt$matrixPath) & is.null(opt$filesPath)) { cat("A counts matrix (or a set of counts files) is required.\n") - q(status=1) + q(status = 1) } if (is.null(opt$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$totReq)) { - filtTotCount <- FALSE + filt_totcount <- FALSE } else { - filtTotCount <- TRUE + filt_totcount <- TRUE } if (is.null(opt$rdaOpt)) { - wantRda <- FALSE + want_rda <- FALSE } else { - wantRda <- TRUE + want_rda <- TRUE } if (is.null(opt$annoPath)) { - haveAnno <- FALSE + have_anno <- FALSE } else { - haveAnno <- TRUE + have_anno <- TRUE } if (is.null(opt$filtCounts)) { - wantFilt <- FALSE + want_filt <- FALSE } else { - wantFilt <- TRUE + want_filt <- TRUE } if (is.null(opt$normCounts)) { - wantNorm <- FALSE + want_norm <- FALSE } else { - wantNorm <- TRUE + want_norm <- TRUE } if (is.null(opt$robOpt)) { - wantRobust <- FALSE + want_robust <- FALSE } else { - wantRobust <- TRUE + want_robust <- TRUE } if (is.null(opt$weightOpt)) { - wantWeight <- FALSE + want_weight <- FALSE } else { - wantWeight <- TRUE + want_weight <- TRUE } if (is.null(opt$trend)) { - wantTrend <- FALSE - deMethod <- "limma-voom" + want_trend <- FALSE + de_method <- "limma-voom" } else { - wantTrend <- TRUE - deMethod <- "limma-trend" - priorCount <- opt$trend + want_trend <- TRUE + de_method <- "limma-trend" + prior_count <- opt$trend } if (is.null(opt$treatOpt)) { - wantTreat <- FALSE + want_treat <- FALSE } else { - wantTreat <- TRUE + want_treat <- TRUE } if (is.null(opt$libinfoOpt)) { - wantLibinfo <- FALSE + want_libinfo <- FALSE } else { - wantLibinfo <- TRUE + want_libinfo <- TRUE } @@ -280,65 +280,67 @@ library("rjson") parser <- newJSONParser() parser$addData(opt$filesPath) - factorList <- parser$getObject() - factors <- sapply(factorList, function(x) x[[1]]) - filenamesIn <- unname(unlist(factorList[[1]][[2]])) - sampleTable <- data.frame(sample=basename(filenamesIn), - filename=filenamesIn, - row.names=filenamesIn, - stringsAsFactors=FALSE) - for (factor in factorList) { - factorName <- factor[[1]] - sampleTable[[factorName]] <- character(nrow(sampleTable)) + factor_list <- parser$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] + sampletable[files, factorname] <- lvls[i] } - sampleTable[[factorName]] <- factor(sampleTable[[factorName]], levels=lvls) + sampletable[[factorname]] <- factor(sampletable[[factorname]], levels = lvls) } - rownames(sampleTable) <- sampleTable$sample - rem <- c("sample","filename") - factors <- sampleTable[, !(names(sampleTable) %in% rem), drop=FALSE] + rownames(sampletable) <- sampletable$sample + rem <- c("sample", "filename") + factors <- sampletable[, !(names(sampletable) %in% rem), drop = FALSE] #read in count files and create single table - countfiles <- lapply(sampleTable$filename, function(x){read.delim(x, row.names=1)}) + countfiles <- lapply(sampletable$filename, function(x) { + read.delim(x, row.names = 1) + }) counts <- do.call("cbind", countfiles) } else { # Process the single count matrix - counts <- read.table(opt$matrixPath, header=TRUE, sep="\t", strip.white=TRUE, stringsAsFactors=FALSE, check.names=FALSE) + counts <- read.table(opt$matrixPath, header = TRUE, sep = "\t", strip.white = TRUE, stringsAsFactors = FALSE, check.names = FALSE) row.names(counts) <- counts[, 1] - counts <- counts[ , -1] - countsRows <- nrow(counts) + 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) - if(!setequal(factorData[, 1], colnames(counts))) + factordata <- read.table(opt$factFile, header = TRUE, sep = "\t", strip.white = TRUE) + if (!setequal(factordata[, 1], colnames(counts))) stop("Sample IDs in counts and factors files don't match") # order samples as in counts matrix - factorData <- factorData[match(colnames(counts), factorData[, 1]), ] - factors <- factorData[, -1, drop=FALSE] + factordata <- factordata[match(colnames(counts), factordata[, 1]), ] + factors <- factordata[, -1, drop = FALSE] } else { - factors <- unlist(strsplit(opt$factInput, "|", fixed=TRUE)) - factorData <- list() + factors <- unlist(strsplit(opt$factInput, "|", fixed = TRUE)) + factordata <- list() for (fact in factors) { - newFact <- unlist(strsplit(fact, split="::")) - factorData <- rbind(factorData, newFact) + newfact <- unlist(strsplit(fact, split = "::")) + factordata <- rbind(factordata, newfact) } # Factors have the form: FACT_NAME::LEVEL,LEVEL,LEVEL,LEVEL,... The first factor is the Primary Factor. # Set the row names to be the name of the factor and delete first row - row.names(factorData) <- factorData[, 1] - factorData <- factorData[, -1] - factorData <- sapply(factorData, sanitiseGroups) - factorData <- sapply(factorData, strsplit, split=",") + row.names(factordata) <- factordata[, 1] + factordata <- factordata[, -1] + factordata <- sapply(factordata, sanitise_groups) + factordata <- sapply(factordata, strsplit, split = ",") # Transform factor data into data frame of R factor objects - factors <- data.frame(factorData) + factors <- data.frame(factordata) } } # check there are the same number of samples in counts and factors -if(nrow(factors) != ncol(counts)) { +if (nrow(factors) != ncol(counts)) { stop("There are a different number of samples in the counts files and factors") } # make groups valid R names, required for makeContrasts @@ -346,95 +348,95 @@ factors <- data.frame(factors) # if annotation file provided -if (haveAnno) { - geneanno <- read.table(opt$annoPath, header=TRUE, sep="\t", quote= "", strip.white=TRUE, stringsAsFactors=FALSE) +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) +dir.create(opt$outPath, showWarnings = FALSE) # Process contrasts if (is.null(opt$contrastInput)) { - contrastData <- read.table(opt$contrastFile, header=TRUE, sep="\t", quote= "", strip.white=TRUE, stringsAsFactors=FALSE) - contrastData <- contrastData[, 1, drop=TRUE] + contrast_data <- read.table(opt$contrastFile, header = TRUE, sep = "\t", quote = "", strip.white = TRUE, stringsAsFactors = FALSE) + contrast_data <- contrast_data[, 1, drop = TRUE] } else { # Split up contrasts seperated by comma into a vector then sanitise - contrastData <- unlist(strsplit(opt$contrastInput, split=",")) + contrast_data <- unlist(strsplit(opt$contrastInput, split = ",")) } -contrastData <- sanitiseEquation(contrastData) -contrastData <- gsub(" ", ".", contrastData, fixed=TRUE) +contrast_data <- sanitise_equation(contrast_data) +contrast_data <- gsub(" ", ".", contrast_data, fixed = TRUE) # in case input groups start with numbers make the names valid R names, required for makeContrasts cons <- NULL cons_d <- NULL -for (i in contrastData) { +for (i in contrast_data) { # if the contrast is a difference of differences e.g. (A-B)-(X-Y) if (grepl("\\)-\\(", i)) { - i <- unlist(strsplit(i, split="\\)-\\(")) - i <- gsub("\\(|\\)","", i) + i <- unlist(strsplit(i, split = "\\)-\\(")) + i <- gsub("\\(|\\)", "", i) for (j in i) { - j <- sanitiseContrast(j) + j <- sanitise_contrast(j) j <- paste0("(", j, ")") cons_d <- append(cons_d, unlist(j)) } - cons_d <- paste(cons_d, collapse = '-') + cons_d <- paste(cons_d, collapse = "-") cons <- append(cons, unlist(cons_d)) } else { - i <- sanitiseContrast(i) + i <- sanitise_contrast(i) cons <- append(cons, unlist(i)) } } plots <- character() if (!is.null(opt$plots)) { - plots <- unlist(strsplit(opt$plots, split=",")) + 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() -glimmaOut <- character() -for (i in 1:length(cons)) { +den_png <- make_out("densityplots.png") +den_pdf <- make_out("densityplots.pdf") +cpm_pdf <- make_out("cpmplots.pdf") +box_png <- make_out("boxplots.png") +box_pdf <- make_out("boxplots.pdf") +mdsscree_png <- make_out("mdsscree.png") +mdsscree_pdf <- make_out("mdsscree.pdf") +mdsx_pdf <- make_out("mdsplot_extra.pdf") +mdsx_png <- make_out("mdsplot_extra.png") +mdsam_pdf <- make_out("mdplots_samples.pdf") +md_pdf <- character() # Initialise character vector +vol_pdf <- character() +heat_pdf <- character() +strip_pdf <- character() +mdvol_png <- character() +top_out <- character() +glimma_out <- character() +for (i in seq_along(cons)) { con <- cons[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")) - glimmaOut[i] <- makeOut(paste0("glimma_", con, "/MD-Plot.html")) + md_pdf[i] <- make_out(paste0("mdplot_", con, ".pdf")) + vol_pdf[i] <- make_out(paste0("volplot_", con, ".pdf")) + heat_pdf[i] <- make_out(paste0("heatmap_", con, ".pdf")) + strip_pdf[i] <- make_out(paste0("stripcharts_", con, ".pdf")) + mdvol_png[i] <- make_out(paste0("mdvolplot_", con, ".png")) + top_out[i] <- make_out(paste0(de_method, "_", con, ".tsv")) + glimma_out[i] <- make_out(paste0("glimma_", con, "/MD-Plot.html")) } -filtOut <- makeOut(paste0(deMethod, "_", "filtcounts")) -normOut <- makeOut(paste0(deMethod, "_", "normcounts")) -rdaOut <- makeOut(paste0(deMethod, "_analysis.RData")) -sessionOut <- makeOut("session_info.txt") +filt_out <- make_out(paste0(de_method, "_", "filtcounts")) +norm_out <- make_out(paste0(de_method, "_", "normcounts")) +rda_out <- make_out(paste0(de_method, "_analysis.RData")) +session_out <- make_out("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) +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 @@ -444,17 +446,17 @@ print("Extracting counts") 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$genes <- data.frame(GeneID = row.names(counts)) } # Creating naming data samplenames <- colnames(data$counts) -sampleanno <- data.frame("sampleID"=samplenames, factors) +sampleanno <- data.frame("sampleID" = samplenames, factors) row.names(factors) <- samplenames # for "Summary of experimental data" table # Creating colours for the groups @@ -463,129 +465,128 @@ # 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$counts) +prefilter_count <- nrow(data$counts) nsamples <- ncol(data$counts) -if (filtCPM || filtSmpCount || filtTotCount) { +if (filt_cpm || filt_smpcount || filt_totcount) { - if (filtTotCount) { + if (filt_totcount) { keep <- rowSums(data$counts) >= opt$cntReq - } else if (filtSmpCount) { + } else if (filt_smpcount) { keep <- rowSums(data$counts >= opt$cntReq) >= opt$sampleReq - } else if (filtCPM) { - myCPM <- cpm(data$counts) - thresh <- myCPM >= opt$cpmReq - keep <- rowSums(thresh) >= opt$sampleReq + } else if (filt_cpm) { + + keep <- rowSums(cpm(data$counts) >= opt$cpmReq) >= opt$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) + pdf(cpm_pdf, width = 6.5, height = 10) + par(mfrow = c(3, 2)) + for (i in seq_len(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)) + link_name <- "CpmPlots.pdf" + link_addr <- "cpmplots.pdf" + link_data <- rbind(link_data, data.frame(Label = link_name, Link = link_addr, stringsAsFactors = FALSE)) invisible(dev.off()) } } data$counts <- data$counts[keep, ] - data$genes <- data$genes[keep, , drop=FALSE] + data$genes <- data$genes[keep, , drop = FALSE] - if (wantFilt) { + if (want_filt) { print("Outputting filtered counts") - filt_counts <- data.frame(data$genes, data$counts, check.names=FALSE) - write.table(filt_counts, file=filtOut, row.names=FALSE, sep="\t", quote=FALSE) - linkData <- rbind(linkData, data.frame(Label=paste0(deMethod, "_", "filtcounts.tsv"), Link=paste0(deMethod, "_", "filtcounts"), stringsAsFactors=FALSE)) + filt_counts <- data.frame(data$genes, data$counts, check.names = FALSE) + write.table(filt_counts, file = filt_out, row.names = FALSE, sep = "\t", quote = FALSE) + link_data <- rbind(link_data, data.frame(Label = paste0(de_method, "_", "filtcounts.tsv"), Link = paste0(de_method, "_", "filtcounts"), stringsAsFactors = FALSE)) } # Plot Density if ("d" %in% plots) { # PNG - png(denOutPng, width=1000, height=500) - par(mfrow=c(1,2), cex.axis=0.8) + png(den_png, 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){ + 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) + lines(den$x, den$y, col = col.group[i], lwd = 2) } # after filtering - lcpm2 <- cpm(data$counts, log=TRUE) - plot(density(lcpm2[,1]), col=col.group[1], lwd=2, las=2, main="", xlab="") - title(main="Density Plot: Filtered counts", xlab="Log-cpm") - for (i in 2:nsamples){ + lcpm2 <- cpm(data$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) + 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)) + legend("topright", samplenames, text.col = col.group, bty = "n") + img_name <- "Densityplots.png" + img_addr <- "densityplots.png" + image_data <- rbind(image_data, data.frame(Label = img_name, Link = img_addr, 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){ + pdf(den_pdf, 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) + 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){ + 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) + 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)) + legend("topright", samplenames, text.col = col.group, bty = "n") + link_name <- "DensityPlots.pdf" + link_addr <- "densityplots.pdf" + link_data <- rbind(link_data, data.frame(Label = link_name, Link = link_addr, stringsAsFactors = FALSE)) invisible(dev.off()) } } -postFilterCount <- nrow(data$counts) -filteredCount <- preFilterCount-postFilterCount +postfilter_count <- nrow(data$counts) +filtered_count <- prefilter_count - postfilter_count # Generating the DGEList object "y" print("Generating DGEList object") data$samples <- sampleanno -data$samples$lib.size <- colSums(data$counts) +data$samples$lib.size <- colSums(data$counts) # nolint data$samples$norm.factors <- 1 row.names(data$samples) <- colnames(data$counts) y <- new("DGEList", data) print("Generating Design") -factorList <- sapply(names(factors), pasteListName) +factor_list <- sapply(names(factors), paste_listname) formula <- "~0" -for (i in 1:length(factorList)) { - formula <- paste(formula,factorList[i], sep="+") +for (i in seq_along(factor_list)) { + formula <- paste(formula, factor_list[i], sep = "+") } formula <- formula(formula) design <- model.matrix(formula) -for (i in 1:length(factorList)) { - colnames(design) <- gsub(factorList[i], "", colnames(design), fixed=TRUE) +for (i in seq_along(factor_list)) { + colnames(design) <- gsub(factor_list[i], "", colnames(design), fixed = TRUE) } # Calculating normalising factors print("Calculating Normalisation Factors") logcounts <- y #store for plots -y <- calcNormFactors(y, method=opt$normOpt) +y <- calcNormFactors(y, method = opt$normOpt) # Generate contrasts information print("Generating Contrasts") -contrasts <- makeContrasts(contrasts=cons, levels=design) +contrasts <- makeContrasts(contrasts = cons, levels = design) ################################################################################ ### Data Output @@ -593,44 +594,44 @@ # Plot Box plots (before and after normalisation) if (opt$normOpt != "none" & "b" %in% plots) { - png(boxOutPng, width=1000, height=500) - par(mfrow=c(1,2), mar=c(6,4,2,2)+0.1) + png(box_png, width = 1000, height = 500) + par(mfrow = c(1, 2), mar = c(6, 4, 2, 2) + 0.1) labels <- colnames(counts) - lcpm1 <- cpm(y$counts, log=TRUE) - boxplot(lcpm1, las=2, col=col.group, xaxt="n", xlab="") - axis(1, at=seq_along(labels), labels = FALSE) - abline(h=median(lcpm1), col=4) - text(x=seq_along(labels), y=par("usr")[3]-1, srt=45, adj=1, labels=labels, xpd=TRUE) - title(main="Box Plot: Unnormalised counts", ylab="Log-cpm") + lcpm1 <- cpm(y$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") + 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)) + img_name <- "Boxplots.png" + img_addr <- "boxplots.png" + image_data <- rbind(image_data, data.frame(Label = img_name, Link = img_addr, 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)) + pdf(box_pdf, 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") + link_name <- "BoxPlots.pdf" + link_addr <- "boxplots.pdf" + link_data <- rbind(link_data, data.frame(Label = link_name, Link = link_addr, stringsAsFactors = FALSE)) invisible(dev.off()) } @@ -641,11 +642,11 @@ # Scree plot (Variance Explained) code copied from Glimma # get column of matrix -getCols <- function(x, inds) { - x[, inds, drop=FALSE] +get_cols <- function(x, inds) { + x[, inds, drop = FALSE] } -x <- cpm(y, log=TRUE) +x <- cpm(y, log = TRUE) ndim <- nsamples - 1 nprobes <- nrow(x) top <- 500 @@ -655,412 +656,412 @@ if (any(bad)) { warning("Rows containing infinite values have been removed") - x <- x[!bad, , drop=FALSE] + x <- x[!bad, , drop = FALSE] } -dd <- matrix(0, nrow=nsamples, ncol=nsamples, dimnames=list(cn, cn)) +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 ) + dists <- (get_cols(x, i) - get_cols(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)) +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)) +png(mdsscree_png, 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) +img_name <- paste0("MDSPlot_", names(factors)[1], ".png") +img_addr <- "mdsscree.png" +image_data <- rbind(image_data, data.frame(Label = img_name, Link = img_addr, 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)) +pdf(mdsscree_pdf, 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) +link_name <- paste0("MDSPlot_", names(factors)[1], ".pdf") +link_addr <- "mdsscree.pdf" +link_data <- rbind(link_data, data.frame(Label = link_name, Link = link_addr, stringsAsFactors = FALSE)) invisible(dev.off()) # generate Glimma interactive MDS Plot if ("i" %in% plots) { - Glimma::glMDSPlot(y, labels=samplenames, groups=factors[, 1], - folder="glimma_MDS", launch=FALSE) - linkName <- "Glimma_MDSPlot.html" - linkAddr <- "glimma_MDS/MDS-Plot.html" - linkData <- rbind(linkData, c(linkName, linkAddr)) + Glimma::glMDSPlot(y, labels = samplenames, groups = factors[, 1], + folder = "glimma_MDS", launch = FALSE) + link_name <- "Glimma_MDSPlot.html" + link_addr <- "glimma_MDS/MDS-Plot.html" + link_data <- rbind(link_data, c(link_name, link_addr)) } if ("x" %in% plots) { - png(mdsxOutPng, width=1000, height=500) - par(mfrow=c(1, 2)) + png(mdsx_png, 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)) + 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)) + img_name <- paste0("MDSPlot_extra.png") + img_addr <- paste0("mdsplot_extra.png") + image_data <- rbind(image_data, data.frame(Label = img_name, Link = img_addr, stringsAsFactors = FALSE)) invisible(dev.off()) - pdf(mdsxOutPdf, width=14) - par(mfrow=c(1, 2)) + pdf(mdsx_pdf, 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)) + 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)) + link_name <- "MDSPlot_extra.pdf" + link_addr <- "mdsplot_extra.pdf" + link_data <- rbind(link_data, data.frame(Label = link_name, Link = link_addr, 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)) + pdf(mdsam_pdf, width = 6.5, height = 10) + par(mfrow = c(3, 2)) for (i in 1:nsamples) { if (opt$normOpt != "none") { - plotMD(logcounts, column=i, main=paste(colnames(logcounts)[i], "(before)")) - abline(h=0, col="red", lty=2, lwd=2) + plotMD(logcounts, column = i, main = paste(colnames(logcounts)[i], "(before)")) + abline(h = 0, col = "red", lty = 2, lwd = 2) } - plotMD(y, column=i) - abline(h=0, col="red", lty=2, lwd=2) + plotMD(y, column = i) + abline(h = 0, col = "red", lty = 2, lwd = 2) } - linkName <- "MDPlots_Samples.pdf" - linkAddr <- "mdplots_samples.pdf" - linkData <- rbind(linkData, c(linkName, linkAddr)) + link_name <- "MDPlots_Samples.pdf" + link_addr <- "mdplots_samples.pdf" + link_data <- rbind(link_data, c(link_name, link_addr)) invisible(dev.off()) } -if (wantTrend) { +if (want_trend) { # limma-trend approach - logCPM <- cpm(y, log=TRUE, prior.count=opt$trend) - fit <- lmFit(logCPM, design) + 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) + if (want_robust) { + fit <- eBayes(fit, trend = TRUE, robust = TRUE) } else { - fit <- eBayes(fit, trend=TRUE, robust=FALSE) + fit <- eBayes(fit, trend = TRUE, robust = FALSE) } - plotData <- logCPM + plot_data <- 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")))) + if (want_norm) { + write.table(logcpm, file = norm_out, row.names = TRUE, sep = "\t", quote = FALSE) + link_data <- rbind(link_data, c((paste0(de_method, "_", "normcounts.tsv")), (paste0(de_method, "_", "normcounts")))) } } else { # limma-voom approach - if (wantWeight) { - voomWtsOutPdf <- makeOut("voomwtsplot.pdf") - voomWtsOutPng <- makeOut("voomwtsplot.png") + if (want_weight) { + voomwts_pdf <- make_out("voomwtsplot.pdf") + voomwts_png <- make_out("voomwtsplot.png") # Creating voom data object and plot - png(voomWtsOutPng, width=1000, height=500) - vData <- voomWithQualityWeights(y, design=design, plot=TRUE) - imgName <- "VoomWithQualityWeightsPlot.png" - imgAddr <- "voomwtsplot.png" - imageData <- rbind(imageData, c(imgName, imgAddr)) + png(voomwts_png, width = 1000, height = 500) + vdata <- voomWithQualityWeights(y, design = design, plot = TRUE) + img_name <- "VoomWithQualityWeightsPlot.png" + img_addr <- "voomwtsplot.png" + image_data <- rbind(image_data, c(img_name, img_addr)) invisible(dev.off()) - pdf(voomWtsOutPdf, width=14) - vData <- voomWithQualityWeights(y, design=design, plot=TRUE) - linkName <- "VoomWithQualityWeightsPlot.pdf" - linkAddr <- "voomwtsplot.pdf" - linkData <- rbind(linkData, c(linkName, linkAddr)) + pdf(voomwts_pdf, width = 14) + vdata <- voomWithQualityWeights(y, design = design, plot = TRUE) + link_name <- "VoomWithQualityWeightsPlot.pdf" + link_addr <- "voomwtsplot.pdf" + link_data <- rbind(link_data, c(link_name, link_addr)) invisible(dev.off()) # Generating fit data and top table with weights - wts <- vData$weights - voomFit <- lmFit(vData, design, weights=wts) + wts <- vdata$weights + voomfit <- lmFit(vdata, design, weights = wts) } else { - voomOutPdf <- makeOut("voomplot.pdf") - voomOutPng <- makeOut("voomplot.png") + voom_pdf <- make_out("voomplot.pdf") + voom_png <- make_out("voomplot.png") # Creating voom data object and plot - png(voomOutPng, width=500, height=500) - vData <- voom(y, design=design, plot=TRUE) - imgName <- "VoomPlot" - imgAddr <- "voomplot.png" - imageData <- rbind(imageData, c(imgName, imgAddr)) + png(voom_png, width = 500, height = 500) + vdata <- voom(y, design = design, plot = TRUE) + img_name <- "VoomPlot" + img_addr <- "voomplot.png" + image_data <- rbind(image_data, c(img_name, img_addr)) invisible(dev.off()) - pdf(voomOutPdf) - vData <- voom(y, design=design, plot=TRUE) - linkName <- "VoomPlot.pdf" - linkAddr <- "voomplot.pdf" - linkData <- rbind(linkData, c(linkName, linkAddr)) + pdf(voom_pdf) + vdata <- voom(y, design = design, plot = TRUE) + link_name <- "VoomPlot.pdf" + link_addr <- "voomplot.pdf" + link_data <- rbind(link_data, c(link_name, link_addr)) invisible(dev.off()) # Generate voom fit - voomFit <- lmFit(vData, design) + voomfit <- lmFit(vdata, design) } # Save normalised counts (log2cpm) - if (wantNorm) { - norm_counts <- data.frame(vData$genes, vData$E, check.names=FALSE) - write.table(norm_counts, file=normOut, row.names=FALSE, sep="\t", quote=FALSE) - linkData <- rbind(linkData, c((paste0(deMethod, "_", "normcounts.tsv")), (paste0(deMethod, "_", "normcounts")))) + if (want_norm) { + norm_counts <- data.frame(vdata$genes, vdata$E, check.names = FALSE) + write.table(norm_counts, file = norm_out, row.names = FALSE, sep = "\t", quote = FALSE) + link_data <- rbind(link_data, c((paste0(de_method, "_", "normcounts.tsv")), (paste0(de_method, "_", "normcounts")))) } # Fit linear model and estimate dispersion with eBayes - voomFit <- contrasts.fit(voomFit, contrasts) - if (wantRobust) { - fit <- eBayes(voomFit, robust=TRUE) + voomfit <- contrasts.fit(voomfit, contrasts) + if (want_robust) { + fit <- eBayes(voomfit, robust = TRUE) } else { - fit <- eBayes(voomFit, robust=FALSE) + fit <- eBayes(voomfit, robust = FALSE) } - plotData <- vData + plot_data <- vdata } # plot final model mean-variance trend with plotSA -saOutPng <- makeOut("saplot.png") -saOutPdf <- makeOut("saplot.pdf") +sa_png <- make_out("saplot.png") +sa_pdf <- make_out("saplot.pdf") -png(saOutPng, width=500, height=500) -plotSA(fit, main="Final model: Mean-variance trend (SA Plot)") -imgName <- "SAPlot.png" -imgAddr <- "saplot.png" -imageData <- rbind(imageData, c(imgName, imgAddr)) +png(sa_png, width = 500, height = 500) +plotSA(fit, main = "Final model: Mean-variance trend (SA Plot)") +img_name <- "SAPlot.png" +img_addr <- "saplot.png" +image_data <- rbind(image_data, c(img_name, img_addr)) invisible(dev.off()) -pdf(saOutPdf) -plotSA(fit, main="Final model: Mean-variance trend (SA Plot)") -linkName <- "SAPlot.pdf" -linkAddr <- "saplot.pdf" -linkData <- rbind(linkData, c(linkName, linkAddr)) +pdf(sa_pdf) +plotSA(fit, main = "Final model: Mean-variance trend (SA Plot)") +link_name <- "SAPlot.pdf" +link_addr <- "saplot.pdf" +link_data <- rbind(link_data, c(link_name, link_addr)) invisible(dev.off()) # Save library size info -if (wantLibinfo) { +if (want_libinfo) { efflibsize <- round(y$samples$lib.size * y$samples$norm.factors) - libsizeinfo <- cbind(y$samples, EffectiveLibrarySize=efflibsize) - libsizeinfo$lib.size <- round(libsizeinfo$lib.size) - names(libsizeinfo)[names(libsizeinfo)=="sampleID"] <- "SampleID" - names(libsizeinfo)[names(libsizeinfo)=="lib.size"] <- "LibrarySize" - names(libsizeinfo)[names(libsizeinfo)=="norm.factors"] <- "NormalisationFactor" - write.table(libsizeinfo, file="libsizeinfo", row.names=FALSE, sep="\t", quote=FALSE) + libsizeinfo <- cbind(y$samples, EffectiveLibrarySize = efflibsize) + libsizeinfo$lib.size <- round(libsizeinfo$lib.size) # nolint + names(libsizeinfo)[names(libsizeinfo) == "sampleID"] <- "SampleID" + names(libsizeinfo)[names(libsizeinfo) == "lib.size"] <- "LibrarySize" + names(libsizeinfo)[names(libsizeinfo) == "norm.factors"] <- "NormalisationFactor" + write.table(libsizeinfo, file = "libsizeinfo", row.names = FALSE, sep = "\t", quote = FALSE) } print("Generating DE results") -if (wantTreat) { +if (want_treat) { print("Applying TREAT method") - if (wantRobust) { - fit <- treat(fit, lfc=opt$lfcReq, robust=TRUE) + if (want_robust) { + fit <- treat(fit, lfc = opt$lfcReq, robust = TRUE) } else { - fit <- treat(fit, lfc=opt$lfcReq, robust=FALSE) + 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) +status <- decideTests(fit, adjust.method = opt$pAdjOpt, p.value = opt$pValReq, + lfc = opt$lfcReq) +sum_status <- summary(status) -for (i in 1:length(cons)) { +for (i in seq_along(cons)) { con_name <- cons[i] con <- cons[i] con <- gsub("\\(|\\)", "", con) # Collect counts for differential expression - upCount[i] <- sumStatus["Up", i] - downCount[i] <- sumStatus["Down", i] - flatCount[i] <- sumStatus["NotSig", i] + up_count[i] <- sum_status["Up", i] + down_count[i] <- sum_status["Down", i] + flat_count[i] <- sum_status["NotSig", i] # Write top expressions table - if (wantTreat) { - top <- topTreat(fit, coef=i, adjust.method=opt$pAdjOpt, number=Inf, sort.by="P") + if (want_treat) { + top <- topTreat(fit, coef = i, adjust.method = opt$pAdjOpt, number = Inf, sort.by = "P") } else{ - top <- topTable(fit, coef=i, adjust.method=opt$pAdjOpt, number=Inf, sort.by="P") + top <- topTable(fit, coef = i, adjust.method = opt$pAdjOpt, number = Inf, sort.by = "P") } - write.table(top, file=topOut[i], row.names=FALSE, sep="\t", quote=FALSE) - linkName <- paste0(deMethod, "_", con, ".tsv") - linkAddr <- paste0(deMethod, "_", con, ".tsv") - linkData <- rbind(linkData, c(linkName, linkAddr)) + write.table(top, file = top_out[i], row.names = FALSE, sep = "\t", quote = FALSE) + link_name <- paste0(de_method, "_", con, ".tsv") + link_addr <- paste0(de_method, "_", con, ".tsv") + link_data <- rbind(link_data, c(link_name, link_addr)) # Plot MD (log ratios vs mean average) using limma package on weighted - pdf(mdOutPdf[i]) - limma::plotMD(fit, status=status[, i], coef=i, - main=paste("MD Plot:", unmake.names(con)), - hl.col=alpha(c("firebrick", "blue"), 0.4), values=c(1, -1), - xlab="Average Expression", ylab="logFC") - abline(h=0, col="grey", lty=2) - linkName <- paste0("MDPlot_", con, ".pdf") - linkAddr <- paste0("mdplot_", con, ".pdf") - linkData <- rbind(linkData, c(linkName, linkAddr)) + pdf(md_pdf[i]) + limma::plotMD(fit, status = status[, i], coef = i, + main = paste("MD Plot:", unmake_names(con)), + hl.col = alpha(c("firebrick", "blue"), 0.4), values = c(1, -1), + xlab = "Average Expression", ylab = "logFC") + abline(h = 0, col = "grey", lty = 2) + link_name <- paste0("MDPlot_", con, ".pdf") + link_addr <- paste0("mdplot_", con, ".pdf") + link_data <- rbind(link_data, c(link_name, link_addr)) invisible(dev.off()) # Generate Glimma interactive Volcano, MD plot and tables, requires annotation file (assumes gene labels/symbols in 2nd column) - if ("i" %in% plots & haveAnno) { + if ("i" %in% plots & have_anno) { # make gene labels unique to handle NAs geneanno <- y$genes geneanno[, 2] <- make.unique(geneanno[, 2]) - # use the logCPMS for the counts - if (wantTrend) { - cnts <- logCPM + # use the logcpms for the counts + if (want_trend) { + cnts <- logcpm } else{ - cnts <- vData$E + cnts <- vdata$E } # MD plot - Glimma::glMDPlot(fit, coef=i, counts=cnts, anno=geneanno, groups=factors[, 1], - status=status[, i], sample.cols=col.group, - main=paste("MD Plot:", unmake.names(con)), side.main=colnames(y$genes)[2], - folder=paste0("glimma_", unmake.names(con)), launch=FALSE) - linkName <- paste0("Glimma_MDPlot_", con, ".html") - linkAddr <- paste0("glimma_", con, "/MD-Plot.html") - linkData <- rbind(linkData, c(linkName, linkAddr)) + Glimma::glMDPlot(fit, coef = i, counts = cnts, anno = geneanno, groups = factors[, 1], + status = status[, i], sample.cols = col.group, + main = paste("MD Plot:", unmake_names(con)), side.main = colnames(y$genes)[2], + folder = paste0("glimma_", unmake_names(con)), launch = FALSE) + link_name <- paste0("Glimma_MDPlot_", con, ".html") + link_addr <- paste0("glimma_", con, "/MD-Plot.html") + link_data <- rbind(link_data, c(link_name, link_addr)) # Volcano plot - Glimma::glXYPlot(x=fit$coefficients[, i], y=-log10(fit$p.value[, i]), counts=cnts, anno=geneanno, groups=factors[, 1], - status=status[, i], sample.cols=col.group, - main=paste("Volcano Plot:", unmake.names(con)), side.main=colnames(y$genes)[2], - xlab="logFC", ylab="-log10(P-value)", - folder=paste0("glimma_volcano_", unmake.names(con)), launch=FALSE) - linkName <- paste0("Glimma_VolcanoPlot_", con, ".html") - linkAddr <- paste0("glimma_volcano_", con, "/XY-Plot.html") - linkData <- rbind(linkData, c(linkName, linkAddr)) + Glimma::glXYPlot(x = fit$coefficients[, i], y = -log10(fit$p.value[, i]), counts = cnts, anno = geneanno, groups = factors[, 1], + status = status[, i], sample.cols = col.group, + main = paste("Volcano Plot:", unmake_names(con)), side.main = colnames(y$genes)[2], + xlab = "logFC", ylab = "-log10(P-value)", + folder = paste0("glimma_volcano_", unmake_names(con)), launch = FALSE) + link_name <- paste0("Glimma_VolcanoPlot_", con, ".html") + link_addr <- paste0("glimma_volcano_", con, "/XY-Plot.html") + link_data <- rbind(link_data, c(link_name, link_addr)) } # Plot Volcano - pdf(volOutPdf[i]) - if (haveAnno) { + pdf(vol_pdf[i]) + if (have_anno) { # 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)) + limma::volcanoplot(fit, coef = i, + main = paste("Volcano Plot:", unmake_names(con)), + highlight = opt$topgenes, + names = labels) + link_name <- paste0("VolcanoPlot_", con, ".pdf") + link_addr <- paste0("volplot_", con, ".pdf") + link_data <- rbind(link_data, c(link_name, link_addr)) invisible(dev.off()) # PNG of MD and Volcano - png(mdvolOutPng[i], width=1000, height=500) - par(mfrow=c(1, 2), mar=c(5,4,2,2)+0.1, oma=c(0,0,3,0)) + png(mdvol_png[i], width = 1000, height = 500) + par(mfrow = c(1, 2), mar = c(5, 4, 2, 2) + 0.1, oma = c(0, 0, 3, 0)) # MD plot - limma::plotMD(fit, status=status[, i], coef=i, main="MD Plot", - hl.col=alpha(c("firebrick", "blue"), 0.4), values=c(1, -1), - xlab="Average Expression", ylab="logFC") - abline(h=0, col="grey", lty=2) + limma::plotMD(fit, status = status[, i], coef = i, main = "MD Plot", + hl.col = alpha(c("firebrick", "blue"), 0.4), values = c(1, -1), + xlab = "Average Expression", ylab = "logFC") + abline(h = 0, col = "grey", lty = 2) # Volcano - if (haveAnno) { + if (have_anno) { # labels must be in second column currently - limma::volcanoplot(fit, coef=i, main="Volcano Plot", - highlight=opt$topgenes, - names=fit$genes[, 2]) + limma::volcanoplot(fit, coef = i, main = "Volcano Plot", + highlight = opt$topgenes, + names = fit$genes[, 2]) } else { - limma::volcanoplot(fit, coef=i, main="Volcano Plot", - highlight=opt$topgenes, - names=fit$genes$GeneID) + limma::volcanoplot(fit, coef = i, main = "Volcano Plot", + highlight = opt$topgenes, + names = fit$genes$GeneID) } - imgName <- paste0("MDVolPlot_", con) - imgAddr <- paste0("mdvolplot_", con, ".png") - imageData <- rbind(imageData, c(imgName, imgAddr)) - title(paste0("Contrast: ", con_name), outer=TRUE, cex.main=1.5) + img_name <- paste0("MDVolPlot_", con) + img_addr <- paste0("mdvolplot_", con, ".png") + image_data <- rbind(image_data, c(img_name, img_addr)) + title(paste0("Contrast: ", con_name), outer = TRUE, cex.main = 1.5) invisible(dev.off()) if ("h" %in% plots) { # Plot Heatmap topgenes <- rownames(top[1:opt$topgenes, ]) - if (wantTrend) { - topexp <- plotData[topgenes, ] + if (want_trend) { + topexp <- plot_data[topgenes, ] } else { - topexp <- plotData$E[topgenes, ] + topexp <- plot_data$E[topgenes, ] } - pdf(heatOutPdf[i]) - mycol <- colorpanel(1000,"blue","white","red") - if (haveAnno) { + pdf(heat_pdf[i]) + mycol <- colorpanel(1000, "blue", "white", "red") + if (have_anno) { # 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$topgenes, "genes by adj.P.Val"), - trace="none", density.info="none", lhei=c(2,10), margin=c(8, 6), labRow=labels, cexRow=0.7, srtCol=45, - col=mycol, ColSideColors=col.group) - linkName <- paste0("Heatmap_", con, ".pdf") - linkAddr <- paste0("heatmap_", con, ".pdf") - linkData <- rbind(linkData, c(linkName, linkAddr)) + heatmap.2(topexp, scale = "row", Colv = FALSE, Rowv = FALSE, dendrogram = "none", + main = paste("Contrast:", unmake_names(con), "\nTop", opt$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) + link_name <- paste0("Heatmap_", con, ".pdf") + link_addr <- paste0("heatmap_", con, ".pdf") + link_data <- rbind(link_data, c(link_name, link_addr)) 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) + pdf(strip_pdf[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)) { + for (j in seq_along(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)) + if (want_trend) { + stripchart(plot_data[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$E[topgenes[j], ] ~ factors[, 1], vertical=TRUE, las=2, pch=16, cex=0.8, cex.lab=0.8, col=cols, - method="jitter", ylab="Normalised log2 expression", main=paste0(labels[j], "\nlogFC=", lfc, ", adj.P.Val=", pval)) + stripchart(plot_data$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)) + link_name <- paste0("Stripcharts_", con, ".pdf") + link_addr <- paste0("stripcharts_", con, ".pdf") + link_data <- rbind(link_data, c(link_name, link_addr)) invisible(dev.off()) } } -sigDiff <- data.frame(Up=upCount, Flat=flatCount, Down=downCount) -row.names(sigDiff) <- cons +sig_diff <- data.frame(Up = up_count, Flat = flat_count, Down = down_count) +row.names(sig_diff) <- cons # Save relevant items as rda object -if (wantRda) { +if (want_rda) { print("Saving RData") - if (wantWeight) { - save(counts, data, y, status, plotData, labels, factors, wts, fit, top, contrastData, contrasts, design, - file=rdaOut, ascii=TRUE) + if (want_weight) { + save(counts, data, y, status, plot_data, labels, factors, wts, fit, top, contrast_data, contrasts, design, + file = rda_out, ascii = TRUE) } else { - save(counts, data, y, status, plotData, labels, factors, fit, top, contrastData, contrasts, design, - file=rdaOut, ascii=TRUE) + save(counts, data, y, status, plot_data, labels, factors, fit, top, contrast_data, contrasts, design, + file = rda_out, ascii = TRUE) } - linkData <- rbind(linkData, c((paste0(deMethod, "_analysis.RData")), (paste0(deMethod, "_analysis.RData")))) + link_data <- rbind(link_data, c((paste0(de_method, "_analysis.RData")), (paste0(de_method, "_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("<html>\n") @@ -1068,11 +1069,11 @@ cata("<h3>Limma Analysis Output:</h3>\n") cata("Links to PDF copies of plots are in 'Plots' section below <br />\n") -for (i in 1:nrow(imageData)) { - if (grepl("density|box|mds|mdvol|wts", imageData$Link[i])) { - HtmlImage(imageData$Link[i], imageData$Label[i], width=1000) +for (i in seq_len(nrow(image_data))) { + if (grepl("density|box|mds|mdvol|wts", image_data$Link[i])) { + html_image(image_data$Link[i], image_data$Label[i], width = 1000) } else { - HtmlImage(imageData$Link[i], imageData$Label[i]) + html_image(image_data$Link[i], image_data$Label[i]) } } @@ -1080,16 +1081,16 @@ cata("<table border=\"1\" cellpadding=\"4\">\n") cata("<tr>\n") -TableItem() -for (i in colnames(sigDiff)) { - TableHeadItem(i) +table_item() +for (i in colnames(sig_diff)) { + table_head_item(i) } cata("</tr>\n") -for (i in 1:nrow(sigDiff)) { +for (i in seq_len(nrow(sig_diff))) { cata("<tr>\n") - TableHeadItem(unmake.names(row.names(sigDiff)[i])) - for (j in 1:ncol(sigDiff)) { - TableItem(as.character(sigDiff[i, j])) + 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("</tr>\n") } @@ -1097,59 +1098,59 @@ cata("<h4>Plots:</h4>\n") #PDFs -for (i in 1:nrow(linkData)) { - if (grepl(".pdf", linkData$Link[i]) & grepl("density|cpm|boxplot|mds|mdplots|voom|saplot", linkData$Link[i])) { - HtmlLink(linkData$Link[i], linkData$Label[i]) +for (i in seq_len(nrow(link_data))) { + if (grepl(".pdf", link_data$Link[i]) & grepl("density|cpm|boxplot|mds|mdplots|voom|saplot", link_data$Link[i])) { + html_link(link_data$Link[i], link_data$Label[i]) } } -for (i in 1:nrow(linkData)) { - if (grepl("mdplot_", linkData$Link[i])) { - HtmlLink(linkData$Link[i], linkData$Label[i]) +for (i in seq_len(nrow(link_data))) { + if (grepl("mdplot_", link_data$Link[i])) { + html_link(link_data$Link[i], link_data$Label[i]) } } -for (i in 1:nrow(linkData)) { - if (grepl("volplot", linkData$Link[i])) { - HtmlLink(linkData$Link[i], linkData$Label[i]) +for (i in seq_len(nrow(link_data))) { + if (grepl("volplot", link_data$Link[i])) { + html_link(link_data$Link[i], link_data$Label[i]) } } -for (i in 1:nrow(linkData)) { - if (grepl("heatmap", linkData$Link[i])) { - HtmlLink(linkData$Link[i], linkData$Label[i]) +for (i in seq_len(nrow(link_data))) { + if (grepl("heatmap", link_data$Link[i])) { + html_link(link_data$Link[i], link_data$Label[i]) } } -for (i in 1:nrow(linkData)) { - if (grepl("stripcharts", linkData$Link[i])) { - HtmlLink(linkData$Link[i], linkData$Label[i]) +for (i in seq_len(nrow(link_data))) { + if (grepl("stripcharts", link_data$Link[i])) { + html_link(link_data$Link[i], link_data$Label[i]) } } cata("<h4>Tables:</h4>\n") -for (i in 1:nrow(linkData)) { - if (grepl("counts$", linkData$Link[i])) { - HtmlLink(linkData$Link[i], linkData$Label[i]) - } else if (grepl(".tsv", linkData$Link[i])) { - HtmlLink(linkData$Link[i], linkData$Label[i]) +for (i in seq_len(nrow(link_data))) { + if (grepl("counts$", link_data$Link[i])) { + html_link(link_data$Link[i], link_data$Label[i]) + } else if (grepl(".tsv", link_data$Link[i])) { + html_link(link_data$Link[i], link_data$Label[i]) } } -if (wantRda) { +if (want_rda) { cata("<h4>R Data Object:</h4>\n") - for (i in 1:nrow(linkData)) { - if (grepl(".RData", linkData$Link[i])) { - HtmlLink(linkData$Link[i], linkData$Label[i]) + for (i in seq_len(nrow(link_data))) { + if (grepl(".RData", link_data$Link[i])) { + html_link(link_data$Link[i], link_data$Label[i]) } } } if ("i" %in% plots) { cata("<h4>Glimma Interactive Results:</h4>\n") - for (i in 1:nrow(linkData)) { - if (grepl("glimma", linkData$Link[i])) { - HtmlLink(linkData$Link[i], linkData$Label[i]) + for (i in seq_len(nrow(link_data))) { + if (grepl("glimma", link_data$Link[i])) { + html_link(link_data$Link[i], link_data$Label[i]) } } } @@ -1162,66 +1163,66 @@ cata("<h4>Additional Information</h4>\n") cata("<ul>\n") -if (filtCPM || filtSmpCount || filtTotCount) { - if (filtCPM) { - tempStr <- paste("Genes without more than", opt$cpmReq, +if (filt_cpm || filt_smpcount || filt_totcount) { + if (filt_cpm) { + temp_str <- paste("Genes without more than", opt$cpmReq, "CPM in at least", opt$sampleReq, "samples are insignificant", "and filtered out.") - } else if (filtSmpCount) { - tempStr <- paste("Genes without more than", opt$cntReq, + } 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 (filtTotCount) { - tempStr <- paste("Genes without more than", opt$cntReq, + } else if (filt_totcount) { + temp_str <- paste("Genes without more than", opt$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, + 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.") - ListItem(tempStr) + list_item(temp_str) } -ListItem(opt$normOpt, " was the method used to normalise library sizes.") -if (wantTrend) { - ListItem("The limma-trend method was used.") +list_item(opt$normOpt, " was the method used to normalise library sizes.") +if (want_trend) { + list_item("The limma-trend method was used.") } else { - ListItem("The limma-voom method was used.") + list_item("The limma-voom method was used.") } -if (wantWeight) { - ListItem("Weights were applied to samples.") +if (want_weight) { + list_item("Weights were applied to samples.") } else { - ListItem("Weights were not applied to samples.") + list_item("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 (want_treat) { + list_item(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).") +if (want_robust) { + if (want_treat) { + list_item("TREAT was used with robust settings (robust = TRUE).") } else { - ListItem("eBayes was used with robust settings (robust=TRUE).") + list_item("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 ", +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$lfcReq, ".") - ListItem(tempStr) - } else if (opt$pAdjOpt=="holm") { - tempStr <- paste0("MD Plot highlighted genes are significant at adjusted ", - "p-value of ", opt$pValReq," by the Holm(1979) ", + list_item(temp_str) + } else if (opt$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, ".") - ListItem(tempStr) + list_item(temp_str) } } else { - tempStr <- paste0("MD Plot highlighted genes are significant at p-value ", - "of ", opt$pValReq," and exhibit log2-fold-change of at ", + temp_str <- paste0("MD Plot highlighted genes are significant at p-value ", + "of ", opt$pValReq, " and exhibit log2-fold-change of at ", "least ", opt$lfcReq, ".") - ListItem(tempStr) + list_item(temp_str) } cata("</ul>\n") @@ -1231,21 +1232,21 @@ cata("<table border=\"1\" cellpadding=\"3\">\n") cata("<tr>\n") -TableHeadItem("SampleID") -TableHeadItem(names(factors)[1]," (Primary Factor)") +table_head_item("SampleID") +table_head_item(names(factors)[1], " (Primary Factor)") if (ncol(factors) > 1) { for (i in names(factors)[2:length(names(factors))]) { - TableHeadItem(i) + table_head_item(i) } cata("</tr>\n") } -for (i in 1:nrow(factors)) { +for (i in seq_len(nrow(factors))) { cata("<tr>\n") - TableHeadItem(row.names(factors)[i]) - for (j in 1:ncol(factors)) { - TableItem(as.character(unmake.names(factors[i, j]))) + table_head_item(row.names(factors)[i]) + for (j in seq_len(ncol(factors))) { + table_item(as.character(unmake_names(factors[i, j]))) } cata("</tr>\n") } @@ -1313,37 +1314,37 @@ cata("<h4>limma</h4>\n") cata(cit[3], "\n") cata("<ol>\n") -ListItem(cit[4]) -ListItem(cit[10]) -ListItem(cit[11]) +list_item(cit[4]) +list_item(cit[10]) +list_item(cit[11]) cata("</ol>\n") cata("<h4>edgeR</h4>\n") cata(cit[5], "\n") cata("<ol>\n") -ListItem(cit[6]) -ListItem(cit[7]) -ListItem(cit[8]) -ListItem(cit[9]) +list_item(cit[6]) +list_item(cit[7]) +list_item(cit[8]) +list_item(cit[9]) cata("</ol>\n") cata("<p>Please report problems or suggestions to: su.s@wehi.edu.au</p>\n") -for (i in 1:nrow(linkData)) { - if (grepl("session_info", linkData$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("<table border=\"0\">\n") cata("<tr>\n") -TableItem("Task started at:"); TableItem(timeStart) +table_item("Task started at:"); table_item(time_start) cata("</tr>\n") cata("<tr>\n") -TableItem("Task ended at:"); TableItem(timeEnd) +table_item("Task ended at:"); table_item(time_end) cata("</tr>\n") cata("<tr>\n") -TableItem("Task run time:"); TableItem(timeTaken) +table_item("Task run time:"); table_item(time_taken) cata("<tr>\n") cata("</table>\n")
--- a/limma_voom.xml Wed May 29 10:31:41 2019 -0400 +++ b/limma_voom.xml Fri Jun 04 20:37:04 2021 +0000 @@ -1,17 +1,27 @@ -<tool id="limma_voom" name="limma" version="3.38.3+galaxy3"> +<tool id="limma_voom" name="limma" version="3.48.0"> <description> Perform differential expression with limma-voom or limma-trend </description> + <xrefs> + <xref type="bio.tools">limma</xref> + </xrefs> + <edam_topics> + <edam_topic>topic_3308</edam_topic> + </edam_topics> + <edam_operations> + <edam_operation>operation_3563</edam_operation> + <edam_operation>operation_3223</edam_operation> + </edam_operations> <requirements> - <requirement type="package" version="3.38.3">bioconductor-limma</requirement> - <requirement type="package" version="3.24.1">bioconductor-edger</requirement> - <requirement type="package" version="1.4.30">r-statmod</requirement> - <requirement type="package" version="1.0.0">r-scales</requirement> + <requirement type="package" version="3.48.0">bioconductor-limma</requirement> + <requirement type="package" version="3.34.0">bioconductor-edger</requirement> + <requirement type="package" version="1.4.36">r-statmod</requirement> + <requirement type="package" version="1.1.1">r-scales</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="3.0.1">r-gplots</requirement> - <requirement type="package" version="1.10.0">bioconductor-glimma</requirement> + <requirement type="package" version="1.20.3">r-getopt</requirement> + <requirement type="package" version="3.1.1">r-gplots</requirement> + <requirement type="package" version="2.2.0">bioconductor-glimma</requirement> </requirements> <version_command><![CDATA[