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1 <tool id="rgedgeRpaired" name="edgeR paired" version="0.18">
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2 <description>2 level Anova for counts</description>
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3 <command interpreter="python">
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4 rgToolFactory.py --script_path "$runme" --interpreter "Rscript" --tool_name "edgeR"
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5 --output_dir "$html_file.files_path" --output_html "$html_file" --output_tab "$outtab" --make_HTML "yes"
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6 </command>
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7 <inputs>
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8 <param name="input1" type="data" format="tabular" label="Select an input matrix - rows are contigs, columns are counts for each sample"
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9 help="Use the HTSeq based count matrix preparation tool to create these matrices from BAM/SAM files and a GTF file of genomic features"/>
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10 <param name="title" type="text" value="edgeR" size="80" label="Title for job outputs" help="Supply a meaningful name here to remind you what the outputs contain">
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11 <sanitizer invalid_char="">
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12 <valid initial="string.letters,string.digits"><add value="_" /> </valid>
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13 </sanitizer>
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14 </param>
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15 <param name="treatment_name" type="text" value="Treatment" size="50" label="Treatment Name"/>
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16 <param name="Treat_cols" label="Select columns containing treatment." type="data_column" data_ref="input1" numerical="True"
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17 multiple="true" use_header_names="true" size="120" display="checkboxes">
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18 <validator type="no_options" message="Please select at least one column."/>
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19 </param>
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20 <param name="control_name" type="text" value="Control" size="50" label="Control Name"/>
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21 <param name="Control_cols" label="Select columns containing control." type="data_column" data_ref="input1" numerical="True"
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22 multiple="true" use_header_names="true" size="120" display="checkboxes" optional="true">
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23 </param>
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24 <param name="subjectids" type="text" optional="true" size="120"
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25 label="IF SUBJECTS NOT ALL INDEPENDENT! Enter integers to indicate sample pairing for every column in input"
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26 help="Leave blank if no pairing, but eg if data from sample id A99 is in columns 2,4 and id C21 is in 3,5 then enter '1,2,1,2'">
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27 <sanitizer>
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28 <valid initial="string.digits"><add value="," /> </valid>
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29 </sanitizer>
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30 </param>
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31 <param name="fQ" type="float" value="0.3" size="5" label="Non-differential contig count quantile threshold - zero to analyze all non-zero read count contigs"
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32 help="May be a good or a bad idea depending on the biology and the question. EG 0.3 = sparsest 30% of contigs with at least one read are removed before analysis"/>
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33 <param name="useNDF" type="boolean" truevalue="T" checked='false' falsevalue="" size="1" label="Non differential filter - remove contigs below a threshold (1 per million) for half or more samples"
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34 help="May be a good or a bad idea depending on the biology and the question. This was the old default. Quantile based is available as an alternative"/>
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35 <param name="priordf" type="integer" value="20" size="3" label="prior.df for tagwise dispersion - lower value = more emphasis on each tag's variance. Replaces prior.n and prior.df = prior.n * residual.df"
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36 help="Zero = Use edgeR default. Use a small value to 'smooth' small samples. See edgeR docs and note below"/>
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37 <param name="fdrthresh" type="float" value="0.05" size="5" label="P value threshold for FDR filtering for amily wise error rate control"
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38 help="Conventional default value of 0.05 recommended"/>
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39 <param name="fdrtype" type="select" label="FDR (Type II error) control method"
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40 help="Use fdr or bh typically to control for the number of tests in a reliable way">
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41 <option value="fdr" selected="true">fdr</option>
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42 <option value="BH">Benjamini Hochberg</option>
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43 <option value="BY">Benjamini Yukateli</option>
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44 <option value="bonferroni">Bonferroni</option>
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45 <option value="hochberg">Hochberg</option>
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46 <option value="holm">Holm</option>
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47 <option value="hommel">Hommel</option>
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48 <option value="none">no control for multiple tests</option>
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49 </param>
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50 </inputs>
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51 <outputs>
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52 <data format="tabular" name="outtab" label="${title}.xls"/>
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53 <data format="html" name="html_file" label="${title}.html"/>
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54 </outputs>
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55 <stdio>
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56 <exit_code range="4" level="fatal" description="Number of subject ids must match total number of samples in the input matrix" />
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57 </stdio>
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58 <tests>
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59 <test>
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60 <param name='input1' value='test_bams2mx.xls' ftype='tabular' />
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61 <param name='treatment_name' value='case' />
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62 <param name='title' value='edgeRtest' />
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63 <param name='fdrtype' value='fdr' />
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64 <param name='priordf' value="0" />
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65 <param name='fdrthresh' value="0.05" />
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66 <param name='control_name' value='control' />
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67 <param name='Treat_cols' value='3,4,5,9' />
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68 <param name='Control_cols' value='2,6,7,8' />
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69 <output name='outtab' file='edgeRtest1out.xls' ftype='tabular' compare='diff' />
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70 <output name='html_file' file='edgeRtest1out.html' ftype='html' compare='diff' lines_diff='20' />
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71 </test>
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72 </tests>
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73
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74 <configfiles>
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75 <configfile name="runme">
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76 <![CDATA[
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77 #
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78 # edgeR.Rscript
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79 # updated npv 2011 for R 2.14.0 and edgeR 2.4.0 by ross
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80 # Performs DGE on a count table containing n replicates of two conditions
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81 #
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82 # Parameters
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83 #
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84 # 1 - Output Dir
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85
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86 # Original edgeR code by: S.Lunke and A.Kaspi
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87 reallybig = log10(.Machine\$double.xmax)
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88 reallysmall = log10(.Machine\$double.xmin)
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89 library('stringr')
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90 library('gplots')
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91 library('DESeq')
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92 library('edgeR')
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93 hmap2 = function(cmat,nsamp=100,outpdfname='heatmap2.pdf', TName='Treatment',group=NA,myTitle='title goes here')
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94 {
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95 # Perform clustering for significant pvalues after controlling FWER
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96 samples = colnames(cmat)
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97 gu = unique(group)
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98 if (length(gu) == 2) {
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99 col.map = function(g) {if (g==gu[1]) "#FF0000" else "#0000FF"}
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100 pcols = unlist(lapply(group,col.map))
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101 } else {
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102 colours = rainbow(length(gu),start=0,end=4/6)
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103 pcols = colours[match(group,gu)] }
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104 gn = rownames(cmat)
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105 dm = cmat[(! is.na(gn)),]
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106 # remove unlabelled hm rows
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107 nprobes = nrow(dm)
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108 # sub = paste('Showing',nprobes,'contigs ranked for evidence of differential abundance')
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109 if (nprobes > nsamp) {
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110 dm =dm[1:nsamp,]
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111 #sub = paste('Showing',nsamp,'contigs ranked for evidence for differential abundance out of',nprobes,'total')
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112 }
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113 newcolnames = substr(colnames(dm),1,20)
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114 colnames(dm) = newcolnames
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115 pdf(outpdfname)
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116 heatmap.2(dm,main=myTitle,ColSideColors=pcols,col=topo.colors(100),dendrogram="col",key=T,density.info='none',
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117 Rowv=F,scale='row',trace='none',margins=c(8,8),cexRow=0.4,cexCol=0.5)
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118 dev.off()
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119 }
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120
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121 hmap = function(cmat,nmeans=4,outpdfname="heatMap.pdf",nsamp=250,TName='Treatment',group=NA,myTitle="Title goes here")
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122 {
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123 # for 2 groups only was
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124 #col.map = function(g) {if (g==TName) "#FF0000" else "#0000FF"}
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125 #pcols = unlist(lapply(group,col.map))
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126 gu = unique(group)
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127 colours = rainbow(length(gu),start=0.3,end=0.6)
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128 pcols = colours[match(group,gu)]
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129 nrows = nrow(cmat)
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130 mtitle = paste(myTitle,'Heatmap: n contigs =',nrows)
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131 if (nrows > nsamp) {
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132 cmat = cmat[c(1:nsamp),]
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133 mtitle = paste('Heatmap: Top ',nsamp,' DE contigs (of ',nrows,')',sep='')
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134 }
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135 newcolnames = substr(colnames(cmat),1,20)
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136 colnames(cmat) = newcolnames
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137 pdf(outpdfname)
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138 heatmap(cmat,scale='row',main=mtitle,cexRow=0.3,cexCol=0.4,Rowv=NA,ColSideColors=pcols)
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139 dev.off()
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140 }
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141
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142 qqPlot = function(descr='Title',pvector, ...)
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143 # stolen from https://gist.github.com/703512
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144 {
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145 o = -log10(sort(pvector,decreasing=F))
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146 e = -log10( 1:length(o)/length(o) )
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147 o[o==-Inf] = reallysmall
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148 o[o==Inf] = reallybig
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149 pdfname = paste(gsub(" ","", descr , fixed=TRUE),'pval_qq.pdf',sep='_')
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150 maint = paste(descr,'QQ Plot')
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151 pdf(pdfname)
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152 plot(e,o,pch=19,cex=1, main=maint, ...,
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153 xlab=expression(Expected~~-log[10](italic(p))),
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154 ylab=expression(Observed~~-log[10](italic(p))),
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155 xlim=c(0,max(e)), ylim=c(0,max(o)))
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156 lines(e,e,col="red")
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157 grid(col = "lightgray", lty = "dotted")
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158 dev.off()
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159 }
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160
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161 smearPlot = function(DGEList,deTags, outSmear, outMain)
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162 {
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163 pdf(outSmear)
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164 plotSmear(DGEList,de.tags=deTags,main=outMain)
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165 grid(col="blue")
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166 dev.off()
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167 }
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168
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169 boxPlot = function(rawrs,cleanrs,maint,myTitle)
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170 { #
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171 nc = ncol(rawrs)
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172 for (i in c(1:nc)) {rawrs[(rawrs[,i] < 0),i] = NA}
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173 fullnames = colnames(rawrs)
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174 newcolnames = substr(colnames(rawrs),1,20)
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175 colnames(rawrs) = newcolnames
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176 newcolnames = substr(colnames(cleanrs),1,20)
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177 colnames(cleanrs) = newcolnames
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178 pdfname = paste(gsub(" ","", myTitle , fixed=TRUE),"sampleBoxplot.pdf",sep='_')
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179 defpar = par(no.readonly=T)
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180 pdf(pdfname,height=6,width=8)
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181 #par(mfrow=c(1,2)) # 1 rows 2 col
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182 l = layout(matrix(c(1,2),1,2,byrow=T))
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183 print.noquote('raw contig counts by sample:')
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184 print.noquote(summary(rawrs))
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185 print.noquote('normalised contig counts by sample:')
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186 print.noquote(summary(cleanrs))
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187 boxplot(rawrs,varwidth=T,notch=T,ylab='log contig count',col="maroon",las=3,cex.axis=0.35,main=paste('Raw:',maint))
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188 grid(col="blue")
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189 boxplot(cleanrs,varwidth=T,notch=T,ylab='log contig count',col="maroon",las=3,cex.axis=0.35,main=paste('After ',maint))
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190 grid(col="blue")
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191 dev.off()
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192 pdfname = paste(gsub(" ","", myTitle , fixed=TRUE),"samplehistplot.pdf",sep='_')
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193 nc = ncol(rawrs)
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194 print.noquote(paste('Using ncol rawrs=',nc))
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195 ncroot = round(sqrt(nc))
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196 if (ncroot*ncroot < nc) { ncroot = ncroot + 1 }
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197 m = c()
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198 for (i in c(1:nc)) {
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199 rhist = hist(rawrs[,i],breaks=100,plot=F)
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200 m = append(m,max(rhist\$counts))
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201 }
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202 ymax = max(m)
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203 pdf(pdfname)
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204 par(mfrow=c(ncroot,ncroot))
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205 for (i in c(1:nc)) {
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206 hist(rawrs[,i], main=paste("Contig logcount",i), xlab='log raw count', col="maroon",
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207 breaks=100,sub=fullnames[i],cex=0.8,ylim=c(0,ymax))
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208 }
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209 dev.off()
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210 par(defpar)
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211
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212 }
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213
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214 cumPlot = function(rawrs,cleanrs,maint,myTitle)
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215 { # updated to use ecdf
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216 pdfname = paste(gsub(" ","", myTitle , fixed=TRUE),"RowsumCum.pdf",sep='_')
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217 defpar = par(no.readonly=T)
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218 pdf(pdfname)
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219 par(mfrow=c(2,1))
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220 lrs = log(rawrs,10)
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221 lim = max(lrs)
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222 hist(lrs,breaks=100,main=paste('Before:',maint),xlab="# Reads (log)",
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223 ylab="Count",col="maroon",sub=myTitle, xlim=c(0,lim),las=1)
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224 grid(col="blue")
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225 lrs = log(cleanrs,10)
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226 hist(lrs,breaks=100,main=paste('After:',maint),xlab="# Reads (log)",
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227 ylab="Count",col="maroon",sub=myTitle,xlim=c(0,lim),las=1)
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228 grid(col="blue")
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229 dev.off()
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230 par(defpar)
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231 }
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232
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233 cumPlot1 = function(rawrs,cleanrs,maint,myTitle)
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234 { # updated to use ecdf
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235 pdfname = paste(gsub(" ","", myTitle , fixed=TRUE),"RowsumCum.pdf",sep='_')
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236 pdf(pdfname)
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237 par(mfrow=c(2,1))
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238 lastx = max(rawrs)
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239 rawe = knots(ecdf(rawrs))
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240 cleane = knots(ecdf(cleanrs))
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241 cy = 1:length(cleane)/length(cleane)
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242 ry = 1:length(rawe)/length(rawe)
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243 plot(rawe,ry,type='l',main=paste('Before',maint),xlab="Log Contig Total Reads",
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244 ylab="Cumulative proportion",col="maroon",log='x',xlim=c(1,lastx),sub=myTitle)
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245 grid(col="blue")
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246 plot(cleane,cy,type='l',main=paste('After',maint),xlab="Log Contig Total Reads",
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247 ylab="Cumulative proportion",col="maroon",log='x',xlim=c(1,lastx),sub=myTitle)
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248 grid(col="blue")
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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
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254 edgeIt = function (Count_Matrix,group,outputfilename,fdrtype='fdr',priordf=5,fdrthresh=0.05,outputdir='.',
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255 myTitle='edgeR',libSize=c(),useNDF="T",filterquantile=0.2,subjects=c()) {
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256
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257 # Error handling
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258 if (length(unique(group))!=2){
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259 print("Number of conditions identified in experiment does not equal 2")
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260 q()
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261 }
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262 require(edgeR)
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263 mt = paste(unlist(strsplit(myTitle,'_')),collapse=" ")
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264 allN = nrow(Count_Matrix)
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265 nscut = round(ncol(Count_Matrix)/2)
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266 colTotmillionreads = colSums(Count_Matrix)/1e6
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267 rawrs = rowSums(Count_Matrix)
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268 nonzerod = Count_Matrix[(rawrs > 0),] # remove all zero count genes
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269 nzN = nrow(nonzerod)
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270 nzrs = rowSums(nonzerod)
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271 zN = allN - nzN
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272 print('# Quantiles for non-zero row counts:',quote=F)
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273 print(quantile(nzrs,probs=seq(0,1,0.1)),quote=F)
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274 if (useNDF == "T")
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275 {
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276 gt1rpin3 = rowSums(Count_Matrix/expandAsMatrix(colTotmillionreads,dim(Count_Matrix)) >= 1) >= nscut
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277 lo = colSums(Count_Matrix[!gt1rpin3,])
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278 workCM = Count_Matrix[gt1rpin3,]
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279 cleanrs = rowSums(workCM)
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280 cleanN = length(cleanrs)
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281 meth = paste( "After removing",length(lo),"contigs with fewer than ",nscut," sample read counts >= 1 per million, there are",sep="")
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282 print(paste("Read",allN,"contigs. Removed",zN,"contigs with no reads.",meth,cleanN,"contigs"),quote=F)
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283 maint = paste('Filter >=1/million reads in >=',nscut,'samples')
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284 }
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285 else {
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286 useme = (nzrs > quantile(nzrs,filterquantile))
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287 workCM = nonzerod[useme,]
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288 lo = colSums(nonzerod[!useme,])
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289 cleanrs = rowSums(workCM)
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290 cleanN = length(cleanrs)
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291 meth = paste("After filtering at count quantile =",filterquantile,", there are",sep="")
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292 print(paste('Read',allN,"contigs. Removed",zN,"with no reads.",meth,cleanN,"contigs"),quote=F)
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293 maint = paste('Filter below',filterquantile,'quantile')
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294 }
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295 cumPlot(rawrs=rawrs,cleanrs=cleanrs,maint=maint,myTitle=myTitle)
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296 allgenes <- rownames(workCM)
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297 print(paste("# Total low count contigs per sample = ",paste(lo,collapse=',')),quote=F)
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298 rsums = rowSums(workCM)
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299 TName=unique(group)[1]
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300 CName=unique(group)[2]
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301 # Setup DGEList object
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302 DGEList = DGEList(counts=workCM, group = group)
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303 if (length(subjects) == 0)
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304 {
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305 doDESEQ = T
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306 mydesign = model.matrix(~group)
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307 }
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308 else {
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309 doDESEQ = F
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310 subjf = factor(subjects)
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311 mydesign = model.matrix(~subjf+group) # we block on subject so make group last to simplify finding it
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312 }
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313 print.noquote(paste('Using samples:',paste(colnames(workCM),collapse=',')))
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314 print.noquote('Using design matrix:')
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315 print.noquote(mydesign)
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316 DGEList = estimateGLMCommonDisp(DGEList,mydesign)
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317 comdisp = DGEList\$common.dispersion
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318 DGEList = estimateGLMTrendedDisp(DGEList,mydesign)
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|
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319 if (priordf > 0) {
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320 print.noquote(paste("prior.df =",priordf))
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321 DGEList = estimateGLMTagwiseDisp(DGEList,mydesign,prior.df = priordf)
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|
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322 } else {
|
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323 DGEList = estimateGLMTagwiseDisp(DGEList,mydesign)
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|
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324 }
|
|
|
325 DGLM = glmFit(DGEList,design=mydesign)
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326 efflib = DGEList\$samples\$lib.size*DGEList\$samples\$norm.factors
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327 normData = (1e+06*DGEList\$counts/efflib)
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328 co = length(colnames(mydesign))
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329 DE = glmLRT(DGLM,coef=co) # always last one - subject is first if needed
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330 uoutput = cbind(
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331 Name=as.character(rownames(DGEList\$counts)),
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332 DE\$table,
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333 adj.p.value=p.adjust(DE\$table\$PValue, method=fdrtype),
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334 Dispersion=DGEList\$tagwise.dispersion,totreads=rsums,normData,
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335 DGEList\$counts
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336 )
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337 soutput = uoutput[order(DE\$table\$PValue),] # sorted into p value order - for quick toptable
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338 goodness = gof(DGLM, pcutoff=fdrthresh)
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339 if (sum(goodness\$outlier) > 0) {
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340 print.noquote('GLM outliers:')
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341 print(paste(rownames(DGLM)[(goodness\$outlier != 0)],collapse=','),quote=F)
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342 z = limma::zscoreGamma(goodness\$gof.statistic, shape=goodness\$df/2, scale=2)
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343 pdf(paste(mt,"GoodnessofFit.pdf",sep='_'))
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344 qq = qqnorm(z, panel.first=grid(), main="tagwise dispersion")
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345 abline(0,1,lwd=3)
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346 points(qq\$x[goodness\$outlier],qq\$y[goodness\$outlier], pch=16, col="dodgerblue")
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347 dev.off()
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348 } else { print('No GLM fit outlier genes found\n')}
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349 estpriorn = getPriorN(DGEList)
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350 print(paste("Common Dispersion =",comdisp,"CV = ",sqrt(comdisp),"getPriorN = ",estpriorn),quote=F)
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351 efflib = DGEList\$samples\$lib.size*DGEList\$samples\$norm.factors
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352 normData = (1e+06*DGEList\$counts/efflib)
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353 uniqueg = unique(group)
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354 # Plot MDS
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355 sample_colors = match(group,levels(group))
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356 pdf(paste(mt,"MDSplot.pdf",sep='_'))
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|
357 sampleTypes = levels(group)
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|
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358 plotMDS.DGEList(DGEList,main=paste("MDS Plot for",myTitle),cex=0.5,col=sample_colors,pch=sample_colors)
|
|
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359 legend(x="topleft", legend = sampleTypes,col=c(1:length(sampleTypes)), pch=19)
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360 grid(col="blue")
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|
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361 dev.off()
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362 colnames(normData) = paste( colnames(normData),'N',sep="_")
|
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363 print(paste('Raw sample read totals',paste(colSums(nonzerod,na.rm=T),collapse=',')))
|
|
|
364 nzd = data.frame(log(nonzerod + 1e-2,10))
|
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365 boxPlot(rawrs=nzd,cleanrs=log(normData,10),maint='TMM Normalisation',myTitle=myTitle)
|
|
|
366 if (doDESEQ)
|
|
|
367 {
|
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368 # DESeq
|
|
|
369 deSeqDatcount <- newCountDataSet(workCM, group)
|
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|
370 deSeqDatsizefac <- estimateSizeFactors(deSeqDatcount)
|
|
|
371 deSeqDatdisp <- estimateDispersions(deSeqDatsizefac)
|
|
|
372 rDESeq <- nbinomTest(deSeqDatdisp, levels(group)[1], levels(group)[2])
|
|
|
373 rDESeq <- rDESeq[order(rDESeq\$pval), ]
|
|
|
374 write.table(rDESeq,paste(mt,'DESeq_TopTable.xls',sep='_'), quote=FALSE, sep="\t",row.names=F)
|
|
|
375 topresults.DESeq <- rDESeq[which(rDESeq\$padj < fdrthresh), ]
|
|
|
376 DESeqcountsindex <- which(allgenes %in% topresults.DESeq\$id)
|
|
|
377 DESeqcounts <- rep(0, length(allgenes))
|
|
|
378 DESeqcounts[DESeqcountsindex] <- 1
|
|
|
379 }
|
|
|
380 DGEList = calcNormFactors(DGEList)
|
|
|
381 norm.factor = DGEList\$samples\$norm.factors
|
|
|
382 pdf(paste(mt,"voomplot.pdf",sep='_'))
|
|
|
383 dat.voomed <- voom(DGEList, mydesign, plot = TRUE, lib.size = colSums(workCM) * norm.factor)
|
|
|
384 dev.off()
|
|
|
385 # Use limma to fit data
|
|
|
386 fit <- lmFit(dat.voomed, mydesign)
|
|
|
387 fit <- eBayes(fit)
|
|
|
388 rvoom <- topTable(fit, coef = length(colnames(mydesign)), adj = "BH", n = Inf)
|
|
|
389 write.table(rvoom,paste(mt,'VOOM_topTable.xls',sep='_'), quote=FALSE, sep="\t",row.names=F)
|
|
|
390 # Use an FDR cutoff to find interesting samples for edgeR, DESeq and voom/limma
|
|
|
391 topresults.voom <- rvoom[which(rvoom\$adj.P.Val < fdrthresh), ]
|
|
|
392 topresults.edgeR <- soutput[which(soutput\$adj.p.value < fdrthresh), ]
|
|
|
393 # Create venn diagram of hits
|
|
|
394 edgeRcountsindex <- which(allgenes %in% rownames(topresults.edgeR))
|
|
|
395 voomcountsindex <- which(allgenes %in% topresults.voom\$ID)
|
|
|
396 edgeRcounts <- rep(0, length(allgenes))
|
|
|
397 edgeRcounts[edgeRcountsindex] <- 1
|
|
|
398 voomcounts <- rep(0, length(allgenes))
|
|
|
399 voomcounts[voomcountsindex] <- 1
|
|
|
400 if (doDESEQ) {
|
|
|
401 vennmain = paste(mt,'Voom,edgeR and DESeq overlap at FDR=',fdrthresh)
|
|
|
402 counts.dataframe <- data.frame(edgeRcounts = edgeRcounts, DESeqcounts = DESeqcounts,
|
|
|
403 voomcounts = voomcounts, row.names = allgenes)
|
|
|
404 } else {
|
|
|
405 vennmain = paste(mt,'Voom and edgeR overlap at FDR=',fdrthresh)
|
|
|
406 counts.dataframe <- data.frame(edgeRcounts = edgeRcounts, voomcounts = voomcounts, row.names = allgenes)
|
|
|
407 }
|
|
|
408 counts.venn <- vennCounts(counts.dataframe)
|
|
|
409 vennf = paste(mt,'venn.pdf',sep='_')
|
|
|
410 pdf(vennf)
|
|
|
411 vennDiagram(counts.venn,main=vennmain,col="maroon")
|
|
|
412 dev.off()
|
|
|
413 #Prepare our output file
|
|
|
414 nreads = soutput\$totreads # ordered same way
|
|
|
415 print('# writing output',quote=F)
|
|
|
416 write.table(soutput,outputfilename, quote=FALSE, sep="\t",row.names=F)
|
|
|
417 rn = uoutput\$Name
|
|
|
418 reg = "^chr([0-9]+):([0-9]+)-([0-9]+)"
|
|
|
419 org="hg19"
|
|
|
420 genecards="<a href='http://www.genecards.org/index.php?path=/Search/keyword/"
|
|
|
421 ucsc = paste("<a href='http://genome.ucsc.edu/cgi-bin/hgTracks?db=",org,sep='')
|
|
|
422 testreg = str_match(rn,reg)
|
|
|
423 nreads = uoutput\$totreads # ordered same way
|
|
|
424 if (sum(!is.na(testreg[,1]))/length(testreg[,1]) > 0.8) # is ucsc style string
|
|
|
425 {
|
|
|
426 urls = paste(ucsc,'&position=chr',testreg[,2],':',testreg[,3],"-",testreg[,4],"'>",rn,'</a>',sep='')
|
|
|
427 } else {
|
|
|
428 urls = paste(genecards,rn,"'></a>",rn,'</a>',sep="")
|
|
|
429 }
|
|
|
430 print.noquote('# urls')
|
|
|
431 cat(head(urls))
|
|
|
432 tt = uoutput
|
|
|
433 cat("# Top tags\n")
|
|
|
434 tt = cbind(tt,ntotreads=nreads,URL=urls) # add to end so table isn't laid out strangely
|
|
|
435 tt = tt[order(DE\$table\$PValue),]
|
|
|
436 options(width = 500)
|
|
|
437 print.noquote(tt[1:50,])
|
|
|
438 pdf(paste(mt,"BCV_vs_abundance.pdf",sep='_'))
|
|
|
439 plotBCV(DGEList, cex=0.3, main="Biological CV vs abundance")
|
|
|
440 dev.off()
|
|
|
441 # Plot MAplot
|
|
|
442 deTags = rownames(uoutput[uoutput\$adj.p.value < fdrthresh,])
|
|
|
443 nsig = length(deTags)
|
|
|
444 print(paste('#',nsig,'tags significant at adj p=',fdrthresh),quote=F)
|
|
|
445 print('# deTags',quote=F)
|
|
|
446 print(head(deTags))
|
|
|
447 dg = DGEList[order(DE\$table\$PValue),]
|
|
|
448 #normData = (1e+06 * dg\$counts/expandAsMatrix(dg\$samples\$lib.size, dim(dg)))
|
|
|
449 efflib = dg\$samples\$lib.size*dg\$samples\$norm.factors
|
|
|
450 normData = (1e+06*dg\$counts/efflib)
|
|
|
451 outpdfname=paste(mt,"heatmap.pdf",sep='_')
|
|
|
452 hmap2(normData,nsamp=100,TName=TName,group=group,outpdfname=outpdfname,myTitle=myTitle)
|
|
|
453 outSmear = paste(mt,"Smearplot.pdf",sep='_')
|
|
|
454 outMain = paste("Smear Plot for ",TName,' Vs ',CName,' (FDR@',fdrthresh,' N = ',nsig,')',sep='')
|
|
|
455 smearPlot(DGEList=DGEList,deTags=deTags, outSmear=outSmear, outMain = outMain)
|
|
|
456 qqPlot(descr=myTitle,pvector=DE\$table\$PValue)
|
|
|
457 if (doDESEQ) {
|
|
|
458 cat("# DESeq top 50\n")
|
|
|
459 print(rDESeq[1:50,])
|
|
|
460 }
|
|
|
461 cat("# VOOM top 50\n")
|
|
|
462 print(rvoom[1:50,])
|
|
|
463 # need a design matrix and glm to use this
|
|
|
464 goodness = gof(DGLM, pcutoff=fdrthresh)
|
|
|
465 nout = sum(goodness\$outlier)
|
|
|
466 if (nout > 0) {
|
|
|
467 print.noquote(paste('Found',nout,'Goodness of fit outliers'))
|
|
|
468 rownames(DGLM)[goodness\$outlier]
|
|
|
469 z = limma::zscoreGamma(goodness\$gof.statistic, shape=goodness\$df/2, scale=2)
|
|
|
470 pdf(paste(mt,"GoodnessofFit.pdf",sep='_'))
|
|
|
471 qq = qqnorm(z, panel.first=grid(), main="tagwise dispersion")
|
|
|
472 abline(0,1,lwd=3)
|
|
|
473 points(qq\$x[goodness\$outlier],qq\$y[goodness\$outlier], pch=16, col="dodgerblue")
|
|
|
474 dev.off()
|
|
|
475 }
|
|
|
476 #Return our main table
|
|
|
477 uoutput
|
|
|
478
|
|
|
479 } #Done
|
|
|
480 sink(stdout(),append=T,type="message")
|
|
|
481 options(width=512)
|
|
|
482 Out_Dir = "$html_file.files_path"
|
|
|
483 Input = "$input1"
|
|
|
484 TreatmentName = "$treatment_name"
|
|
|
485 TreatmentCols = "$Treat_cols"
|
|
|
486 ControlName = "$control_name"
|
|
|
487 ControlCols= "$Control_cols"
|
|
|
488 outputfilename = "$outtab"
|
|
|
489 fdrtype = "$fdrtype"
|
|
|
490 priordf = $priordf
|
|
|
491 fdrthresh = $fdrthresh
|
|
|
492 useNDF = "$useNDF"
|
|
|
493 fQ = $fQ # non-differential centile cutoff
|
|
|
494 myTitle = "$title"
|
|
|
495 subjects = c($subjectids)
|
|
|
496 nsubj = length(subjects)
|
|
|
497 #Set our columns
|
|
|
498 TCols = as.numeric(strsplit(TreatmentCols,",")[[1]])-1
|
|
|
499 CCols = as.numeric(strsplit(ControlCols,",")[[1]])-1
|
|
|
500 cat('# got TCols=')
|
|
|
501 cat(TCols)
|
|
|
502 cat('; CCols=')
|
|
|
503 cat(CCols)
|
|
|
504 cat('\n')
|
|
|
505 useCols = c(TCols,CCols)
|
|
|
506 # Create output dir if non existent
|
|
|
507 if (file.exists(Out_Dir) == F) dir.create(Out_Dir)
|
|
|
508
|
|
|
509 Count_Matrix = read.table(Input,header=T,row.names=1,sep='\t') #Load tab file assume header
|
|
|
510 snames = colnames(Count_Matrix)
|
|
|
511 nsamples = length(snames)
|
|
|
512 if (nsubj > 0 & nsubj != nsamples) {
|
|
|
513 options("show.error.messages"=T)
|
|
|
514 mess = paste('Fatal error: Supplied subject id list',paste(subjects,collapse=','),'has length',nsubj,'but there are',nsamples,'samples',paste(snames,collapse=','))
|
|
|
515 write(mess, stderr())
|
|
|
516 #print(mess)
|
|
|
517 quit(save="no",status=4)
|
|
|
518 }
|
|
|
519
|
|
|
520 Count_Matrix = Count_Matrix[,useCols] # reorder columns
|
|
|
521 if (length(subjects) != 0) {subjects = subjects[useCols]}
|
|
|
522 rn = rownames(Count_Matrix)
|
|
|
523 islib = rn %in% c('librarySize','NotInBedRegions')
|
|
|
524 LibSizes = Count_Matrix[subset(rn,islib),][1] # take first
|
|
|
525 Count_Matrix = Count_Matrix[subset(rn,! islib),]
|
|
|
526 group = c(rep(TreatmentName,length(TCols)), rep(ControlName,length(CCols)) ) #Build a group descriptor
|
|
|
527 group = factor(group, levels=c(ControlName,TreatmentName))
|
|
|
528 colnames(Count_Matrix) = paste(group,colnames(Count_Matrix),sep="_") #Relable columns
|
|
|
529 results = edgeIt(Count_Matrix=Count_Matrix,group=group,outputfilename=outputfilename,fdrtype=fdrtype,priordf=priordf,fdrthresh=fdrthresh,
|
|
|
530 outputdir=Out_Dir,myTitle=myTitle,libSize=c(),useNDF=useNDF,filterquantile=fQ,subjects=subjects)
|
|
|
531 #Run the main function
|
|
|
532 # for the log
|
|
|
533 sessionInfo()
|
|
|
534 ]]>
|
|
|
535 </configfile>
|
|
|
536 </configfiles>
|
|
|
537 <help>
|
|
|
538 **What it does**
|
|
|
539
|
|
|
540 Performs digital gene expression analysis between a treatment and control on a count matrix.
|
|
|
541 Optionally adds a term for subject if not all samples are independent or if some other factor needs to be blocked in the design.
|
|
|
542
|
|
|
543 **Input**
|
|
|
544
|
|
|
545 A matrix consisting of non-negative integers. The matrix must have a unique header row identifiying the samples, and a unique set of row names
|
|
|
546 as the first column. Typically the row names are gene symbols or probe id's for downstream use in GSEA and other methods.
|
|
|
547
|
|
|
548 If you have (eg) paired samples and wish to include a term in the GLM to account for some other factor (subject in the case of paired samples),
|
|
|
549 put a comma separated list of indicators for every sample (whether modelled or not!) indicating (eg) the subject number or
|
|
|
550 A list of integers, one for each subject or an empty string if samples are all independent.
|
|
|
551 If not empty, there must be exactly as many integers in the supplied integer list as there are columns (samples) in the count matrix.
|
|
|
552 Integers for samples that are not in the analysis *must* be present in the string as filler even if not used.
|
|
|
553
|
|
|
554 So if you have 2 pairs out of 6 samples, you need to put in unique integers for the unpaired ones
|
|
|
555 eg if you had 6 samples with the first two independent but the second and third pairs each being from independent subjects. you might use
|
|
|
556 8,9,1,1,2,2
|
|
|
557 as subject IDs to indicate two paired samples from the same subject in columns 3/4 and 5/6
|
|
|
558
|
|
|
559 **Output**
|
|
|
560
|
|
|
561 A matrix which consists the original data and relative expression levels and some helpful plots
|
|
|
562
|
|
|
563 **Note on edgeR versions**
|
|
|
564
|
|
|
565 The edgeR authors made a small cosmetic change in the name of one important variable (from p.value to PValue)
|
|
|
566 breaking this and all other code that assumed the old name for this variable,
|
|
|
567 between edgeR2.4.4 and 2.4.6 (the version for R 2.14 as at the time of writing).
|
|
|
568 This means that all code using edgeR is sensitive to the version. I think this was a very unwise thing
|
|
|
569 to do because it wasted hours of my time to track down and will similarly cost other edgeR users dearly
|
|
|
570 when their old scripts break. This tool currently now works with 2.4.6.
|
|
|
571
|
|
|
572 **Note on prior.N**
|
|
|
573
|
|
|
574 http://seqanswers.com/forums/showthread.php?t=5591 says:
|
|
|
575
|
|
|
576 *prior.n*
|
|
|
577
|
|
|
578 The value for prior.n determines the amount of smoothing of tagwise dispersions towards the common dispersion.
|
|
|
579 You can think of it as like a "weight" for the common value. (It is actually the weight for the common likelihood
|
|
|
580 in the weighted likelihood equation). The larger the value for prior.n, the more smoothing, i.e. the closer your
|
|
|
581 tagwise dispersion estimates will be to the common dispersion. If you use a prior.n of 1, then that gives the
|
|
|
582 common likelihood the weight of one observation.
|
|
|
583
|
|
|
584 In answer to your question, it is a good thing to squeeze the tagwise dispersions towards a common value,
|
|
|
585 or else you will be using very unreliable estimates of the dispersion. I would not recommend using the value that
|
|
|
586 you obtained from estimateSmoothing()---this is far too small and would result in virtually no moderation
|
|
|
587 (squeezing) of the tagwise dispersions. How many samples do you have in your experiment?
|
|
|
588 What is the experimental design? If you have few samples (less than 6) then I would suggest a prior.n of at least 10.
|
|
|
589 If you have more samples, then the tagwise dispersion estimates will be more reliable,
|
|
|
590 so you could consider using a smaller prior.n, although I would hesitate to use a prior.n less than 5.
|
|
|
591
|
|
|
592
|
|
|
593 From Bioconductor Digest, Vol 118, Issue 5, Gordon writes:
|
|
|
594
|
|
|
595 Dear Dorota,
|
|
|
596
|
|
|
597 The important settings are prior.df and trend.
|
|
|
598
|
|
|
599 prior.n and prior.df are related through prior.df = prior.n * residual.df,
|
|
|
600 and your experiment has residual.df = 36 - 12 = 24. So the old setting of
|
|
|
601 prior.n=10 is equivalent for your data to prior.df = 240, a very large
|
|
|
602 value. Going the other way, the new setting of prior.df=10 is equivalent
|
|
|
603 to prior.n=10/24.
|
|
|
604
|
|
|
605 To recover old results with the current software you would use
|
|
|
606
|
|
|
607 estimateTagwiseDisp(object, prior.df=240, trend="none")
|
|
|
608
|
|
|
609 To get the new default from old software you would use
|
|
|
610
|
|
|
611 estimateTagwiseDisp(object, prior.n=10/24, trend=TRUE)
|
|
|
612
|
|
|
613 Actually the old trend method is equivalent to trend="loess" in the new
|
|
|
614 software. You should use plotBCV(object) to see whether a trend is
|
|
|
615 required.
|
|
|
616
|
|
|
617 Note you could also use
|
|
|
618
|
|
|
619 prior.n = getPriorN(object, prior.df=10)
|
|
|
620
|
|
|
621 to map between prior.df and prior.n.
|
|
|
622
|
|
|
623 </help>
|
|
|
624
|
|
|
625 </tool>
|
|
|
626
|
|
|
627
|
