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annotate MetaRNASeq.R @ 28:49ce6fbe11de draft

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planemo upload for repository https://github.com/sblanck/smagexp/tree/master/smagexp_tools commit cb90fdea2a64908e301355aef89de403e107764d
author sblanck
date Thu, 21 Jun 2018 04:28:19 -0400
parents cb29ad7d75a5
children 102e4ef8fed2
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1 #!/usr/bin/env Rscript
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2 # setup R error handling to go to stderr
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3 options( show.error.messages=F, error = function () { cat( geterrmessage(), file=stderr() ); q( "no", 1, F ) } )
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5 # we need that to not crash galaxy with an UTF8 error on German LC settings.
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6 loc <- Sys.setlocale("LC_MESSAGES", "en_US.UTF-8")
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8 library("optparse")
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10 ##### Read options
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11 option_list=list(
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12 make_option("--input",type="character",default="NULL",help="list of rdata objects containing eset objects"),
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13 make_option("--result",type="character",default=NULL,help="text file containing result of the meta-analysis"),
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14 make_option("--htmloutput",type="character",default=NULL,help="Output html report"),
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15 make_option("--htmloutputpath",type="character",default="NULL",help="Path of output html report"),
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16 make_option("--htmltemplate",type="character",default=NULL,help="html template)")
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17 );
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19 opt_parser = OptionParser(option_list=option_list);
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20 opt = parse_args(opt_parser);
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22 if(is.null(opt$input)){
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23 print_help(opt_parser)
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24 stop("input required.", call.=FALSE)
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25 }
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27 #loading libraries
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29 suppressPackageStartupMessages(require(affy))
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30 suppressPackageStartupMessages(require(annaffy))
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31 suppressPackageStartupMessages(require(VennDiagram))
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32 suppressPackageStartupMessages(require(GEOquery))
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34 listInput <- trimws( unlist( strsplit(trimws(opt$input), ",") ) )
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36 listfiles=vector()
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37 listfilenames=vector()
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38
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39 for (i in 1:length(listInput))
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40 {
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41 inputFileInfo <- unlist( strsplit( listInput[i], ';' ) )
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42 listfiles=c(listfiles,inputFileInfo[1])
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43 listfilenames=c(listfilenames,inputFileInfo[2])
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44 }
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46 outputfile <- opt$result
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47 result.html = opt$htmloutput
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48 html.files.path=opt$htmloutputpath
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49 result.template=opt$htmltemplate
2
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50
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51 alpha=0.05
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52
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53 #print(comparison)
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54
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55 listData=lapply(listfiles,read.table)
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56 orderData=lapply(listData, function(x) x[order(x[1]), ])
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57 rawpval=lapply(orderData,function(x) x[6])
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58 rawpval=lapply(rawpval, function(x) as.numeric(unlist(x)))
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59
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60 DE=list()
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61 DE=lapply(orderData, function(x) ifelse(x[7]<=0.05,1,0))
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62
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63 FC=list()
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64 FC=lapply(orderData, function(x) x[3])
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65
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66 DE=as.data.frame(DE)
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67 colnames(DE)=listfilenames
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68 FC=as.data.frame(FC)
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69 colnames(FC)=listfilenames
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70 # the comparison must only have two values and the conds must
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71 # be a vector from those values, at least one of each.
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72
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73 #if (length(comparison) != 2) {
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74 # stop("Comparison type must be a tuple: ", cargs[length(cargs) - 8])
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75 #}
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76
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77 sink("/dev/null")
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78 dir.create(html.files.path, recursive=TRUE)
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79 #library(DESeq)
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80 #library(HTSFilter)
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81
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82 #DE=list()
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83 #FC=list()
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84 #i=1
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85
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86 # Open the html output file
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87 #file.conn = file(diag.html, open="w")
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88
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89 #writeLines( c("<html><body>"), file.conn)
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90
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91 # Perform deseq analysis on each study
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92 #for(i in 1:length(listfiles))
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93 #{
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94 # f=listfiles[i]
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95 # fname=listfilenames[i]
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96 # study_name=unlist(strsplit(fname,"[.]"))[1]
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97 # print(paste0("study.name ",study_name))
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98 # d <- read.table(f, sep=" ", header=TRUE, row.names=1)
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99 # conds<-sapply(strsplit(colnames(d),"[.]"),FUN=function(x) x[1])
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100 # if (length(unique(conds)) != 2) {
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101 # warning(as.data.frame(strsplit(colnames(d),"[.]")))
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102 # stop("You can only have two columns types: ", paste(conds,collapse=" "))
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103 # }
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104 # if (!identical(sort(comparison), sort(unique(conds)))) {
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105 # stop("Column types must use the two names from Comparison type, and vice versa. Must have at least one of each in the Column types.\nColumn types: ", cargs[2], "\n", "Comparison type: ", cargs[3])
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106 # }
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107 # if (length(d) != length(conds)) {
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108 # stop("Number of total sample columns in counts file must correspond to the columns types field. E.g. if column types is 'kidney,kidney,liver,liver' then number of sample columns in counts file must be 4 as well.")
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109 # }
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110 #
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111 # cds <- newCountDataSet(d, conds)
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112 # cds <- estimateSizeFactors(cds)
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113 #
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114 # cdsBlind <- estimateDispersions( cds, method="blind" )
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115 #
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116 # if (length(conds) != 2) {
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117 # cds <- estimateDispersions( cds )
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118 # norep = FALSE
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119 # }
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120 #
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121 # if (length(conds) == 2) {
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122 # cds <- estimateDispersions( cds, method=method, sharingMode=mod, fitType="parametric" )
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123 # norep = TRUE
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124 # }
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125 #
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126 # filter<-HTSFilter(cds, plot=FALSE)
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127 # cds.filter<-filter$filteredData
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128 # on.index<-which(filter$on==1)
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129 #
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130 # res<-as.data.frame(matrix(NA,nrow=nrow(cds),ncol=ncol(cds)))
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131 # nbT <- nbinomTest(cds.filter, comparison[1], comparison[2])
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132 # colnames(res)<-colnames(nbT)
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133 # res[on.index,]<-nbT
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134 # #write.table(res[order(res$padj), ], file=outputfile, quote=FALSE, row.names=FALSE, sep="\t")
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135 #
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136 #
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137 # temp.pval.plot = file.path( html.files.path, paste("PvalHist",i,".png",sep=""))
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138 # png( temp.pval.plot, width=500, height=500 )
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139 # hist(res$pval, breaks=100, col="skyblue", border="slateblue", main="")
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140 # dev.off()
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141 #
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142 # writeLines( c("<h2>P-value histogram for ",study_name,"</h2>"), file.conn)
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143 # writeLines( c("<img src='PvalHist",i,".png'><br/><br/>"), file.conn)
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144 #
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145 # #on enregistre la p-value
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146 # rawpval[[study_name]]<-res$pval
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147 # DE[[study_name]]<-ifelse(res$padj<=alpha,1,0)
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148 # FC[[study_name]]<-res$log2FoldChange
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149 #
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150 # i=i+1
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151 #}
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152
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153
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154 # combinations
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155 library(metaRNASeq)
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156 library(UpSetR)
2
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157 fishcomb<-fishercomb(rawpval, BHth=alpha)
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158 warning(length(rawpval))
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159
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160 #nrep=c(length(listFiles))
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161
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162
2
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163 invnormcomb<-invnorm(rawpval, nrep=c(8,8), BHth=alpha)
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164 #DE[["fishercomb"]]<-ifelse(fishcomb$adjpval<=alpha,1,0)
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165 #DE[["invnormcomb"]]<-ifelse(invnormcomb$adjpval<=alpha,1,0)
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166
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167 signsFC<-mapply(FC,FUN=function(x) sign(x))
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168 sumsigns<-apply(signsFC,1,sum)
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169 commonsgnFC<-ifelse(abs(sumsigns)==dim(signsFC)[2],sign(sumsigns),0)
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170
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171 DEresults <- data.frame(DE=DE,"DE.fishercomb"=ifelse(fishcomb$adjpval<=alpha,1,0),"DE.invnorm"=ifelse(invnormcomb$adjpval<=alpha,1,0))
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172
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173 unionDE <- unique(c(fishcomb$DEindices,invnormcomb$DEindices))
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174 FC.selecDE <- data.frame(DEresults[unionDE,],FC[unionDE,],signFC=commonsgnFC[unionDE])
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175 keepDE <- FC.selecDE[which(abs(FC.selecDE$signFC)==1),]
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176
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177 fishcomb_de <- rownames(keepDE)[which(keepDE[,"DE.fishercomb"]==1)]
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178 invnorm_de <- rownames(keepDE)[which(keepDE[,"DE.invnorm"]==1)]
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179 indstudy_de = list()
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180 for (i in 1:length(listfiles)) {
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181 currentIndstudy_de = rownames(keepDE)[which(keepDE[,i]==1)]
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182 indstudy_de[[listfilenames[i]]]=currentIndstudy_de
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183 }
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184
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185 IDDIRRfishcomb=IDD.IRR(fishcomb_de,indstudy_de)
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186 IDDIRRinvnorm=IDD.IRR(invnorm_de,indstudy_de)
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187
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188 #conflits<-data.frame(ID=listData[[1]][rownames(DEresults),1],Fishercomb=DEresults[["DE.fishercomb"]],Invnormcomb=DEresults[["DE.invnorm"]],sign=commonsgnFC)
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189 conflits<-data.frame(ID=listData[[1]][rownames(DEresults),1],DE=DEresults,FC=FC,signFC=commonsgnFC)
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190 #write DE outputfile
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191 write.table(conflits, outputfile,sep="\t",,row.names=FALSE)
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192 library(VennDiagram)
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193 DE_num=apply(keepDE[,1:(length(listfiles)+2)], 2, FUN=function(x) which(x==1))
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194 #DE_num=apply(DEresults, 2, FUN=function(x) which(x==1))
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195 if (length(listfiles<=2)) {
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196 title="VENN DIAGRAM"
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197 width=500
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198 venn.plot<-venn.diagram(x=as.list(DE_num),filename=NULL, col="black", fill=1:length(DE_num)+1,alpha=0.6)
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199 temp.venn.plot = file.path( html.files.path, paste("venn.png"))
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200 png(temp.venn.plot,width=width,height=500)
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201 grid.draw(venn.plot)
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202 dev.off()
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203 } else {
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204 title="UPSETR DIAGRAM"
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205 width=1000
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206 png(temp.venn.plot,width=width,height=500)
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207 upset(fromList(as.list(DE_num)), order.by = "freq")
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208 dev.off()
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209
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210 }
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211
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212
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213 library(jsonlite)
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214 matrixConflits=as.matrix(conflits)
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215 datajson=toJSON(matrixConflits,pretty = TRUE)
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216 summaryFishcombjson=toJSON(as.matrix(t(IDDIRRfishcomb)),pretty = TRUE)
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217 summaryinvnormjson=toJSON(as.matrix(t(IDDIRRinvnorm)),pretty = TRUE)
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218
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219
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220 #vennsplit=strsplit(result.venn,split="/")[[1]]
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221 #venn=paste0("./",vennsplit[length(vennsplit)])
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222
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223
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224 vennFilename="venn.png"
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225 vennFile=file.path(html.files.path,vennFilename)
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226 htmlfile=readChar(result.template, file.info(result.template)$size)
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227 htmlfile=gsub(x=htmlfile,pattern = "###DATAJSON###",replacement = datajson, fixed = TRUE)
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228 htmlfile=gsub(x=htmlfile,pattern = "###FISHSUMMARYJSON###",replacement = summaryFishcombjson, fixed = TRUE)
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229 htmlfile=gsub(x=htmlfile,pattern = "###INVSUMMARYJSON###",replacement = summaryinvnormjson, fixed = TRUE)
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230 htmlfile=gsub(x=htmlfile,pattern = "###VENN###",replacement = vennFilename, fixed = TRUE)
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231 htmlfile=gsub(x=htmlfile,pattern = "###WIDTH##",replacement = as.character(width), fixed = TRUE)
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232 htmlfile=gsub(x=htmlfile,pattern = "###TITLE##",replacement = as.character(width), fixed = TRUE)
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233 write(htmlfile,result.html)
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234
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235 #library(VennDiagram)
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236 #flog.threshold(ERROR)
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237 #
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238 ##venn.plot<-venn.diagram(x = c(res[c(1:(length(res)-3))],meta=list(res$Meta)),filename = v, col = "black", fill = c(1:(length(res)-2)), margin=0.05, alpha = 0.6,imagetype = "png")
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239 #dir.create(result.path, showWarnings = TRUE, recursive = FALSE)
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240 #
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241 #showVenn<-function(liste,file)
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242 #{
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243 # venn.plot<-venn.diagram(x = liste,
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244 # filename = vennFilename, col = "black",
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245 # fill = 1:length(liste)+1,
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246 # margin=0.05, alpha = 0.6,imagetype = "png")
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247 ## png(file);
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248 ## grid.draw(venn.plot);
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249 ## dev.off();
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250 #
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251 #}
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252 #
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253 #l=list()
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254 #for(i in 1:length(esets))
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255 #{
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256 # l[[paste("study",i,sep="")]]<-res[[i]]
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257 #}
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258 #l[["Meta"]]=res[[length(res)-1]]
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259 #showVenn(l,vennFile)
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260 #file.copy(vennFilename,result.path)
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261
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262
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263 #writeLines( c("<h2>Venn Plot</h2>"), file.conn)
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264 #writeLines( c("<img src='venn.png'><br/><br/>"), file.conn)
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265 #writeLines( c("</body></html>"), file.conn)
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266 #close(file.conn)
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267 #print("passe6")
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268 #sink(NULL)