Mercurial > repos > theo.collard > ballgown_wrapper

comparison ballgown.R @ 6:c637c3bf6781 draft

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Uploaded
author theo.collard
date Wed, 26 Apr 2017 08:45:30 -0400
parents 940701e8bc59
children 4290f0f3d908
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5:5e80570024ed 6:c637c3bf6781
1 #!/usr/bin/Rscript
2
3 # Enabling commands line arguments. Using optparse which allows to use options.
4 # ----------------------------------------------------------------------------------------
5
6 suppressMessages(library(optparse, warn.conflicts = FALSE))
7 opt_list=list(
8 make_option(c("-d", "--directory"), type="character", default=NULL, help="directory containing the samples", metavar="character"),
9 make_option(c("-p", "--phendat"), type="character", default=NULL, help="phenotype data(must be a .csv file)", metavar="character"),
10 make_option(c("-t","--outputtranscript"), type="character", default="output_transcript.csv", help="output_transcript.csv: contains the transcripts of the expirements", metavar="character"),
11 make_option(c("-g","--outputgenes"), type="character", default="output_genes.csv", help="output_genes.csv: contains the genes of the expirements", metavar="character"),
12 make_option(c("-e","--texpression"), type="double", default="0.5", help="transcripts expression filter", metavar="character"),
13 make_option(c("--bgout"), type="character", default="", help="save the ballgown object created in the process", metavar="character")
14 )
15 opt_parser=OptionParser(option_list=opt_list)
16 opt=parse_args(opt_parser)
17
18 # Loading required libraries. suppressMessages() remove all noisy attachement messages
19 # ----------------------------------------------------------------------------------------
20
21 suppressMessages(library(ballgown, warn.conflicts = FALSE))
22 suppressMessages(library(genefilter, warn.conflicts = FALSE))
23 suppressMessages(library(dplyr, warn.conflicts = FALSE))
24
25 # Setup for the tool with some bases variables.
26 # ----------------------------------------------------------------------------------------
27
28
29 filtstr = opt$texpression
30 pdat = 2
31 phendata = read.csv(opt$phendat)
32 setwd(opt$dir)
33
34 # Checking if the pdata file has the right samples names.
35 # ----------------------------------------------------------------------------------------
36
37 if (all(phendata$ids == list.files(".")) != TRUE)
38 {
39 stop("Your phenotype data table does not match the samples names. ")
40 }
41
42 # Creation of the ballgown object based on data
43 # ----------------------------------------------------------------------------------------
44 bgi = ballgown(dataDir= "." , samplePattern="", pData = phendata, verbose = FALSE)
45
46 # Filter the genes with an expression superior to the input filter
47 # ----------------------------------------------------------------------------------------
48 bgi_filt= subset(bgi, paste("rowVars(texpr(bgi)) >",filtstr), genomesubset = TRUE)
49
50 # Creating the variables containing the transcripts and the genes and sorting them through the arrange() command.
51 # Checking if there's one or more adjust variables in the phenotype data file
52 # ----------------------------------------------------------------------------------------
53
54 if (ncol(pData(bgi))<=3) {
55 results_transcripts=stattest(bgi_filt,feature = "transcript", covariate = colnames(pData(bgi))[pdat], adjustvars = colnames(pData(bgi)[pdat+1]), getFC = TRUE, meas = "FPKM")
56 results_genes=stattest(bgi_filt,feature = "gene", covariate = colnames(pData(bgi))[pdat], adjustvars = colnames(pData(bgi)[pdat+1]), getFC = TRUE, meas = "FPKM")
57 } else {
58 results_transcripts=stattest(bgi_filt,feature = "transcript", covariate = colnames(pData(bgi))[pdat], adjustvars = c(colnames(pData(bgi)[pdat+1:ncol(pData(bgi))])), getFC = TRUE, meas = "FPKM")
59 results_genes=stattest(bgi_filt,feature = "gene", covariate = colnames(pData(bgi))[pdat], adjustvars = c(colnames(pData(bgi)[pdat+1:ncol(pData(bgi))])), getFC = TRUE, meas = "FPKM")
60 }
61
62 results_transcripts = data.frame(geneNames=ballgown::geneNames(bgi_filt), geneIDs=ballgown::geneIDs(bgi_filt), results_transcripts)
63 results_transcripts = arrange(results_transcripts,pval)
64 results_genes = arrange(results_genes,pval)
65
66 # Main output of the wrapper, two .csv files containing the genes and transcripts with their qvalue and pvalue
67 #This part also output the data of the ballgown object created in the process and save it in a R data file
68 # ----------------------------------------------------------------------------------------
69 write.csv(results_transcripts, opt$outputtranscript, row.names = FALSE)
70 write.csv(results_genes, opt$outputgenes, row.names = FALSE)
71 if (opt$bgout != ""){
72 save(bgi, file=opt$bgout)
73 }