Mercurial > repos > iuc > limma_voom
comparison limma_voom.R @ 13:d5a940112511 draft
planemo upload for repository https://github.com/galaxyproject/tools-iuc/tree/master/tools/limma_voom commit 8560b34a261fde200bd77dc2e817d55d386ac811
author | iuc |
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date | Sun, 30 Sep 2018 10:51:29 -0400 |
parents | 81796eb60bd0 |
children | 3133e833b3ce |
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12:81796eb60bd0 | 13:d5a940112511 |
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369 stripOutPdf[i] <- makeOut(paste0("stripcharts_", con, ".pdf")) | 369 stripOutPdf[i] <- makeOut(paste0("stripcharts_", con, ".pdf")) |
370 mdvolOutPng[i] <- makeOut(paste0("mdvolplot_", con, ".png")) | 370 mdvolOutPng[i] <- makeOut(paste0("mdvolplot_", con, ".png")) |
371 topOut[i] <- makeOut(paste0(deMethod, "_", con, ".tsv")) | 371 topOut[i] <- makeOut(paste0(deMethod, "_", con, ".tsv")) |
372 glimmaOut[i] <- makeOut(paste0("glimma_", con, "/MD-Plot.html")) | 372 glimmaOut[i] <- makeOut(paste0("glimma_", con, "/MD-Plot.html")) |
373 } | 373 } |
374 filtOut <- makeOut(paste0(deMethod, "_filtcounts.tsv")) | 374 filtOut <- makeOut(paste0(deMethod, "_", "filtcounts")) |
375 normOut <- makeOut(paste0(deMethod, "_normcounts.tsv")) | 375 normOut <- makeOut(paste0(deMethod, "_", "normcounts")) |
376 rdaOut <- makeOut(paste0(deMethod, "_analysis.RData")) | 376 rdaOut <- makeOut(paste0(deMethod, "_analysis.RData")) |
377 sessionOut <- makeOut("session_info.txt") | 377 sessionOut <- makeOut("session_info.txt") |
378 | 378 |
379 # Initialise data for html links and images, data frame with columns Label and | 379 # Initialise data for html links and images, data frame with columns Label and |
380 # Link | 380 # Link |
449 | 449 |
450 if (wantFilt) { | 450 if (wantFilt) { |
451 print("Outputting filtered counts") | 451 print("Outputting filtered counts") |
452 filt_counts <- data.frame(data$genes, data$counts) | 452 filt_counts <- data.frame(data$genes, data$counts) |
453 write.table(filt_counts, file=filtOut, row.names=FALSE, sep="\t", quote=FALSE) | 453 write.table(filt_counts, file=filtOut, row.names=FALSE, sep="\t", quote=FALSE) |
454 linkData <- rbind(linkData, data.frame(Label=paste0(deMethod, "_", "filtcounts.tsv"), Link=paste0(deMethod, "_", "filtcounts.tsv"), stringsAsFactors=FALSE)) | 454 linkData <- rbind(linkData, data.frame(Label=paste0(deMethod, "_", "filtcounts.tsv"), Link=paste0(deMethod, "_", "filtcounts"), stringsAsFactors=FALSE)) |
455 } | 455 } |
456 | 456 |
457 # Plot Density | 457 # Plot Density |
458 if ("d" %in% plots) { | 458 if ("d" %in% plots) { |
459 # PNG | 459 # PNG |
721 plotData <- logCPM | 721 plotData <- logCPM |
722 | 722 |
723 # Save normalised counts (log2cpm) | 723 # Save normalised counts (log2cpm) |
724 if (wantNorm) { | 724 if (wantNorm) { |
725 write.table(logCPM, file=normOut, row.names=TRUE, sep="\t", quote=FALSE) | 725 write.table(logCPM, file=normOut, row.names=TRUE, sep="\t", quote=FALSE) |
726 linkData <- rbind(linkData, c((paste0(deMethod, "_", "normcounts.tsv")), (paste0(deMethod, "_", "normcounts.tsv")))) | 726 linkData <- rbind(linkData, c((paste0(deMethod, "_", "normcounts.tsv")), (paste0(deMethod, "_", "normcounts")))) |
727 } | 727 } |
728 } else { | 728 } else { |
729 # limma-voom approach | 729 # limma-voom approach |
730 voomOutPdf <- makeOut("voomplot.pdf") | 730 voomOutPdf <- makeOut("voomplot.pdf") |
731 voomOutPng <- makeOut("voomplot.png") | 731 voomOutPng <- makeOut("voomplot.png") |
772 | 772 |
773 # Save normalised counts (log2cpm) | 773 # Save normalised counts (log2cpm) |
774 if (wantNorm) { | 774 if (wantNorm) { |
775 norm_counts <- data.frame(vData$genes, vData$E) | 775 norm_counts <- data.frame(vData$genes, vData$E) |
776 write.table(norm_counts, file=normOut, row.names=FALSE, sep="\t", quote=FALSE) | 776 write.table(norm_counts, file=normOut, row.names=FALSE, sep="\t", quote=FALSE) |
777 linkData <- rbind(linkData, c((paste0(deMethod, "_", "normcounts.tsv")), (paste0(deMethod, "_", "normcounts.tsv")))) | 777 linkData <- rbind(linkData, c((paste0(deMethod, "_", "normcounts.tsv")), (paste0(deMethod, "_", "normcounts")))) |
778 } | 778 } |
779 | 779 |
780 # Fit linear model and estimate dispersion with eBayes | 780 # Fit linear model and estimate dispersion with eBayes |
781 voomFit <- contrasts.fit(voomFit, contrasts) | 781 voomFit <- contrasts.fit(voomFit, contrasts) |
782 if (wantRobust) { | 782 if (wantRobust) { |
1050 } | 1050 } |
1051 } | 1051 } |
1052 | 1052 |
1053 cata("<h4>Tables:</h4>\n") | 1053 cata("<h4>Tables:</h4>\n") |
1054 for (i in 1:nrow(linkData)) { | 1054 for (i in 1:nrow(linkData)) { |
1055 if (grepl(".tsv", linkData$Link[i])) { | 1055 if (grepl("counts$", linkData$Link[i])) { |
1056 HtmlLink(linkData$Link[i], linkData$Label[i]) | |
1057 } else if (grepl(".tsv", linkData$Link[i])) { | |
1056 HtmlLink(linkData$Link[i], linkData$Label[i]) | 1058 HtmlLink(linkData$Link[i], linkData$Label[i]) |
1057 } | 1059 } |
1058 } | 1060 } |
1059 | 1061 |
1060 if (wantRda) { | 1062 if (wantRda) { |