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1 <tool id="diffexp" name="Voom Rnaseq" version="1.1.0">
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2 <description>
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3 Perform differential expression analysis using pipeline based on the voom
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4 function of the limma bioconductor package. This tool takes a count matrix
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5 (tab separated) as input and produces a HTML report as output.
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6 </description>
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7
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8 <requirements>
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9 <requirement type="R-module" version="3.5.27">edgeR</requirement>
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10 <requirement type="R-module" version="3.18.13">limma</requirement>
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11 </requirements>
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12
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13 <stdio>
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14 <exit_code range="1:" level="fatal" description="Tool exception" />
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15 </stdio>
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16
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17 <command interpreter="Rscript">
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18 diffexp.R $counts
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19
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20 #if $anno.annoOpt=="yes":
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21 $geneanno
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22 #else:
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23 None
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24 #end if
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25
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26 $outFile
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27 $outFile.files_path
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28 "no" <!-- Disabled Rda option -->
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29 $normalisationOption
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30 $weightCond.weightOption
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31 "$contrast"
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32
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33 #if $filterCPM.filterLowCPM=="yes":
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34 $filterCPM.cpmReq
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35 $filterCPM.sampleReq
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36 #else:
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37 0
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38 0
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39 #end if
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40
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41 #if $testOpt.wantOpt=="yes":
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42 "$testOpt.pAdjust"
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43 $testOpt.pVal
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44 $testOpt.lfc
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45 #else:
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46 "BH"
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47 0.05
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48 0
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49 #end if
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50
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51 <!--*Code commented until solution for multiple factors is found*
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52 #for $i, $fct in enumerate($factors):
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53 $fct.factName::$fct.factLevel
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54 #end for
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55 -->
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56 "$factName::$factLevel"
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57
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58 </command>
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59
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60 <inputs>
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61 <param name="counts" type="data" format="tabular" label="Counts Data"/>
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62
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63 <conditional name="anno">
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64 <param name="annoOpt" type="select" label="Use Gene Annotations?"
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65 help="Annotations will be added to table of top differential
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66 expressions to provide descriptions for each gene.">
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67 <option value="no">No</option>
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68 <option value="yes">Yes</option>
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69 </param>
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70
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71 <when value="yes">
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72 <param name="geneanno" type="data" format="tabular"
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73 label="Gene Annotations"/>
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74 </when>
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75 </conditional>
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76
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77 <!--*Code commented until solution for multiple factors is found*
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78 <repeat name="factors" title="Factors" min="1" max="5" default="1">
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79 <param name="factName" type="text" label="Factor Name (No spaces)"
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80 help="Eg. Genotype"/>
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81 <param name="factLevel" type="text" size="100"
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82 label="Factor Levels (No spaces)"
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83 help="Eg. WT,WT,Mut,Mut,WT"/>
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84 </repeat>
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85 -->
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86
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87 <param name="factName" type="text" label="Factor Name"
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88 help="Eg. Genotype."/>
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89 <param name="factLevel" type="text" size="100"
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90 label="Factor Values"
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91 help="Eg. WT,WT,Mut,Mut,WT... NOTE: Please ensure that the same
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92 levels are typed identically when repeated, with all cases
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93 matching."/>
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94
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95 <param name="contrast" type="text" size="30"
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96 label="Contrasts of interest"
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97 help="Eg. Mut-WT,KD-Control."/>
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98
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99 <conditional name="filterCPM">
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100 <param name="filterLowCPM" type="select" label="Filter Low CPM?"
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101 help="Treat genes with very low expression as unexpressed and
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102 filter out to speed up computation.">
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103 <option value="yes" selected="True">Yes</option>
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104 <option value="no">No</option>
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105 </param>
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106
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107 <when value="yes">
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108 <param name="cpmReq" type="float" value="0.5" min="0"
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109 label="Minimum CPM"/>
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110
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111 <param name="sampleReq" type="integer" value="1" min="0"
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112 label="Minimum Samples"
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113 help="Filter out all the genes that do not meet the minimum
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114 CPM in at least this many samples."/>
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115 </when>
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116
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117 <when value="no"/>
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118
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119 </conditional>
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120
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121 <conditional name="weightCond">
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122 <param name="weightOption" type="select" label="Apply sample weights?"
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123 display="radio" help="Apply weights if outliers are present.">
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124
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125 <option value="no">No</option>
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126 <option value="yes">Yes</option>
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127
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128 </param>
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129 </conditional>
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130
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131 <param name="normalisationOption" type="select"
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132 label="Normalisation Method">
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133
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134 <option value="TMM">TMM</option>
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135 <option value="RLE">RLE</option>
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136 <option value="upperquartile">Upperquartile</option>
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137 <option value="none">None (Don't normalise)</option>
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138
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139 </param>
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140
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141 <conditional name="testOpt">
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142 <param name="wantOpt" type="select" label="Use Advanced Testing Options?"
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143 help="Enable choices for p-value adjustment method, p-value threshold
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144 and log2-fold-change threshold.">
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145 <option value="no" selected="True">No</option>
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146 <option value="yes">Yes</option>
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147 </param>
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148
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149 <when value="yes">
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150 <param name="pAdjust" type="select" label="P-Value Adjustment Method.">
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151 <option value="BH">Benjamini and Hochberg (1995)</option>
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152 <option value="BY">Benjamini and Yekutieli (2001)</option>
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153 <option value="holm">Holm (1979)</option>
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154 <option value="none">None</option>
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155 </param>
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156
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157 <param name="pVal" type="float" value="0.05" min="0" max="1"
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158 label="Adjusted Threshold"
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159 help="Genes below this threshold are considered significant and
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160 highlighted in the MA plot. If either BH(1995) or
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161 BY(2001) were selected then this value is a
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162 false-discovery-rate control. If Holm(1979) was selected
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163 then this is an adjusted p-value for family-wise error
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164 rate."/>
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165
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166 <param name="lfc" type="float" value="0" min="0"
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167 label="Minimum log2-fold-change Required"
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168 help="Genes above this threshold and below the p-value
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169 threshold are considered significant and highlighted
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170 in the MA plot."/>
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171 </when>
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172
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173 <when value="no"/>
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174
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175 </conditional>
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176
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177 <!-- <conditional name="wantRda">
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178 <param name="rdaOption" type="select" label="Output RData?"
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179 display="radio"
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180 help="Output all the data R used to construct the plots,
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181 can be loaded into R.">
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182
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183 <option value="no">No</option>
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184 <option value="yes">Yes</option>
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185
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186 </param>
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187 </conditional> -->
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188 </inputs>
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189
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190 <outputs>
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191 <data format="html" name="outFile" label="Voom Output"/>
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192 </outputs>
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193
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194
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195 <help>
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196 .. class:: infomark
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197
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198 **What it does**
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199
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200 Given a matrix of counts and optional information about the genes, this tool
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201 produces plots and tables useful in the analysis of differential gene
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202 expression.
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203
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204 .. class:: warningmark
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205
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206 This tool is dependent on the R packages limma_ and edgeR_ as a part of the
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207 bioconductor project. Please ensure that these packages are installed on the
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208 server running this tool.
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209
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210 -----
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211
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212 **Counts Data:**
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213 A matrix of expression level with rows corresponding to particular genes
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214 and columns corresponding to the feature count in particular samples.
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215 Values must be tab separated and there must be a row for the sample/column
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216 labels and a column for the row/gene labels.
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217
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218 Example::
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219
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220 "GeneID" "Smpl1" "Smpl2" "Smpl3" "Smpl4" "Smpl5"
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221 "27395" 1699 1528 1463 1441 1495
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222 "18777" 1905 1744 1345 1291 1346
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223 "15037" 6 8 4 5 5
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224 "21399" 2099 1974 1574 1519 1654
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225 "58175" 356 312 347 361 346
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226 "10866" 2528 2438 1762 1942 2027
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227 "12421" 2182 2005 1786 1799 1858
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228 "24069" 3 4 2 3 3
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229 "31926" 1337 1380 1004 1102 1000
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230 "71096" 0 0 2 1 6
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231 "59014" 1466 1426 1296 1097 1175
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232 ...
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233
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234 **Gene Annotations:**
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235 Optional input for gene annotations, this can contain more
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236 information about the genes than just an ID number. The annotations will
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237 be avaiable in the top differential expression table.
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238
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239 Example::
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240
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241 "GeneID" "Length" "EntrezID" "Symbols" "GeneName" "Chr"
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242 "11287" "11287" 4681 "11287" "Pzp" "pregnancy zone protein" "6"
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243 "11298" "11298" 1455 "11298" "Aanat" "arylalkylamine N-acetyltransferase" "11"
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244 "11302" "11302" 5743 "11302" "Aatk" "apoptosis-associated tyrosine kinase" "11"
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245 "11303" "11303" 10260 "11303" "Abca1" "ATP-binding cassette, sub-family A (ABC1), member 1" "4"
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246 "11304" "11304" 7248 "11304" "Abca4" "ATP-binding cassette, sub-family A (ABC1), member 4" "3"
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247 "11305" "11305" 8061 "11305" "Abca2" "ATP-binding cassette, sub-family A (ABC1), member 2" "2"
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248 ...
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249
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250 **Factor Name:**
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251 The name of the factor being investigated. This tool currently assumes
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252 that only one factor is of interest.
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253
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254 **Factor Levels:**
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255 The levels of the factor of interest, this must be entered in the same
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256 order as the samples to which the levels correspond as listed in the
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257 columns of the counts matrix.
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258
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259 The values should be seperated by commas, and spaces must not be used.
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260
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261 **Contrasts of Interest:**
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262 The contrasts you wish to make between levels.
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263
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264 Common contrasts would be a simple difference between two levels: "Mut-WT"
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265 represents the difference between the mutant and wild type genotypes.
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266
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267 The values should be seperated by commas and spaces must not be used.
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268
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269 **Filter Low CPM:**
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270 Option to ignore the genes that do not show significant levels of
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271 expression, this filtering is dependent on two criteria:
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272
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273 * **Minimum CPM:** This is the counts per million that a gene must have in at
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274 least some specified number of samples.
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275
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276 * **Minumum Samples:** This is the number of samples in which the CPM
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277 requirement must be met in order for that gene to be acknowledged.
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278
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279 Only genes that exhibit a CPM greater than the required amount in at least the
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280 number of samples specified will be used for analysis. Care should be taken to
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281 ensure that the sample requirement is appropriate. In the case of an experiment
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282 with two experimental groups each with two members, if there is a change from
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283 insignificant cpm to significant cpm but the sample requirement is set to 3,
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284 then this will cause that gene to fail the criteria. When in doubt simply do not
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285 filter.
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286
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287
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288 **Normalisation Method:**
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289 Option for using different methods to rescale the raw library
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290 size. For more information, see calcNormFactor section in the edgeR_ user's
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291 manual.
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292
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293 **Apply Sample Weights:**
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294 Option to downweight outlier samples such that their information is still
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295 used in the statistical analysis but their impact is reduced. Use this
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296 whenever significant outliers are present. The MDS plotting tool in this package
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297 is useful for identifying outliers
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298
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299 **Use Advanced Testing Options?:**
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300 By default error rate for multiple testing is controlled using Benjamini and
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301 Hochberg's false discovery rate control at a threshold value of 0.05. However
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302 there are options to change this to custom values.
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303
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304 * **P-Value Adjustment Method:**
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305 Change the multiple testing control method, the options are BH(1995) and
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306 BY(2001) which are both false discovery rate controls. There is also
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307 Holm(1979) which is a method for family-wise error rate control.
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308
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309 * **Adjusted Threshold:**
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310 Set the threshold for the resulting value of the multiple testing control
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311 method. Only observations whose statistic falls below this value is
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312 considered significant, thus highlighted in the MA plot.
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313
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314 * **Minimum log2-fold-change Required:**
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315 In addition to meeting the requirement for the adjusted statistic for
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316 multiple testing, the observation must have an absolute log2-fold-change
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317 greater than this threshold to be considered significant, thus highlighted
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318 in the MA plot.
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319
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320 -----
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321
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322 **Citations:**
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323
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324 .. class:: infomark
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325
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326 limma
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327
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328 Please cite the paper below for the limma software itself. Please also try
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329 to cite the appropriate methodology articles that describe the statistical
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330 methods implemented in limma, depending on which limma functions you are
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331 using. The methodology articles are listed in Section 2.1 of the limma
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332 User's Guide.
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333
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334 * Smyth, GK (2005). Limma: linear models for microarray data. In:
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335 'Bioinformatics and Computational Biology Solutions using R and
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336 Bioconductor'. R. Gentleman, V. Carey, S. Dudoit, R. Irizarry,
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337 W. Huber (eds), Springer, New York, pages 397-420.
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338
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339 * Law, CW, Chen, Y, Shi, W, and Smyth, GK (2014). Voom:
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340 precision weights unlock linear model analysis tools for
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341 RNA-seq read counts. Genome Biology 15, R29.
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342
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1
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343 * Ritchie, M. E., Diyagama, D., Neilson, J., van Laar, R., Dobrovic,
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344 A., Holloway, A., and Smyth, G. K. (2006). Empirical array quality weights
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345 for microarray data. BMC Bioinformatics 7, Article 261.
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346
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0
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347 .. class:: infomark
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348
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349 edgeR
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350
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351 Please cite the first paper for the software itself and the other papers for
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352 the various original statistical methods implemented in edgeR. See
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353 Section 1.2 in the User's Guide for more detail.
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354
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355 * Robinson MD, McCarthy DJ and Smyth GK (2010). edgeR: a Bioconductor
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356 package for differential expression analysis of digital gene expression
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357 data. Bioinformatics 26, 139-140
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358
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359 * Robinson MD and Smyth GK (2007). Moderated statistical tests for assessing
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360 differences in tag abundance. Bioinformatics 23, 2881-2887
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361
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362 * Robinson MD and Smyth GK (2008). Small-sample estimation of negative
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363 binomial dispersion, with applications to SAGE data.
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364 Biostatistics, 9, 321-332
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365
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366 * McCarthy DJ, Chen Y and Smyth GK (2012). Differential expression analysis
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367 of multifactor RNA-Seq experiments with respect to biological variation.
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368 Nucleic Acids Research 40, 4288-4297
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369
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1
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370 Please report problems or suggestions to: su.s@wehi.edu.au
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0
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371
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372 .. _edgeR: http://www.bioconductor.org/packages/release/bioc/html/edgeR.html
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373 .. _limma: http://www.bioconductor.org/packages/release/bioc/html/limma.html
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374
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375 </help>
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376 </tool>
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