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1 <tool id="rgDifferentialCount" name="Differential_Count" version="0.22">
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2 <description>models using BioConductor packages</description>
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3 <requirements>
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4 <requirement type="package" version="3.0.1">r3</requirement>
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5 <requirement type="package" version="1.3.18">graphicsmagick</requirement>
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6 <requirement type="package" version="9.07">ghostscript</requirement>
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7 <requirement type="package" version="2.12">biocbasics</requirement>
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8 </requirements>
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9
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10 <command interpreter="python">
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11 rgToolFactory.py --script_path "$runme" --interpreter "Rscript" --tool_name "DifferentialCounts"
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12 --output_dir "$html_file.files_path" --output_html "$html_file" --make_HTML "yes"
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13 </command>
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14 <inputs>
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15 <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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16 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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17 <param name="title" type="text" value="Differential Counts" size="80" label="Title for job outputs"
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18 help="Supply a meaningful name here to remind you what the outputs contain">
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19 <sanitizer invalid_char="">
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20 <valid initial="string.letters,string.digits"><add value="_" /> </valid>
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21 </sanitizer>
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22 </param>
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23 <param name="treatment_name" type="text" value="Treatment" size="50" label="Treatment Name"/>
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24 <param name="Treat_cols" label="Select columns containing treatment." type="data_column" data_ref="input1" numerical="True"
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25 multiple="true" use_header_names="true" size="120" display="checkboxes">
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26 <validator type="no_options" message="Please select at least one column."/>
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27 <sanitizer invalid_char="">
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28 <valid initial="string.letters,string.digits"><add value="_" /> </valid>
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29 </sanitizer>
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30 </param>
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31 <param name="control_name" type="text" value="Control" size="50" label="Control Name"/>
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32 <param name="Control_cols" label="Select columns containing control." type="data_column" data_ref="input1" numerical="True"
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33 multiple="true" use_header_names="true" size="120" display="checkboxes" optional="true">
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34 <validator type="no_options" message="Please select at least one column."/>
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35 <sanitizer invalid_char="">
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36 <valid initial="string.letters,string.digits"><add value="_" /> </valid>
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37 </sanitizer> <validator type="no_options" message="Please select at least one column."/>
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38 <sanitizer invalid_char="">
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39 <valid initial="string.letters,string.digits"><add value="_" /> </valid>
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40 </sanitizer>
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41
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42 </param>
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43 <param name="subjectids" type="text" optional="true" size="120" value = ""
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44 label="IF SUBJECTS NOT ALL INDEPENDENT! Enter comma separated strings to indicate sample labels for (eg) pairing - must be one for every column in input"
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45 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 'A99,C21,A99,C21'">
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46 <sanitizer>
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47 <valid initial="string.letters,string.digits"><add value="," /> </valid>
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48 </sanitizer>
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49 </param>
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50 <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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51 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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52 <param name="useNDF" type="boolean" truevalue="T" falsevalue="F" checked="false" size="1"
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53 label="Non differential filter - remove contigs below a threshold (1 per million) for half or more samples"
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54 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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55
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56 <conditional name="edgeR">
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57 <param name="doedgeR" type="select"
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58 label="Run this model using edgeR"
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59 help="edgeR uses a negative binomial model and seems to be powerful, even with few replicates">
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60 <option value="F">Do not run edgeR</option>
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61 <option value="T" selected="true">Run edgeR</option>
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62 </param>
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63 <when value="T">
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64 <param name="edgeR_priordf" type="integer" value="20" size="3"
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65 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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66 help="0 = Use edgeR default. Use a small value to 'smooth' small samples. See edgeR docs and note below"/>
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67 </when>
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68 <when value="F"></when>
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69 </conditional>
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70 <conditional name="DESeq2">
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71 <param name="doDESeq2" type="select"
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72 label="Run the same model with DESeq2 and compare findings"
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73 help="DESeq2 is an update to the DESeq package. It uses different assumptions and methods to edgeR">
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74 <option value="F" selected="true">Do not run DESeq2</option>
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75 <option value="T">Run DESeq2</option>
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76 </param>
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77 <when value="T">
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78 <param name="DESeq_fitType" type="select">
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79 <option value="parametric" selected="true">Parametric (default) fit for dispersions</option>
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80 <option value="local">Local fit - this will automagically be used if parametric fit fails</option>
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81 <option value="mean">Mean dispersion fit- use this if you really understand what you're doing - read the fine manual linked below in the documentation</option>
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82 </param>
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83 </when>
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84 <when value="F"> </when>
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85 </conditional>
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86 <param name="doVoom" type="select"
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87 label="Run the same model with Voom/limma and compare findings"
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88 help="Voom uses counts per million and a precise transformation of variance so count data can be analysed using limma">
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89 <option value="F" selected="true">Do not run VOOM</option>
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90 <option value="T">Run VOOM</option>
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91 </param>
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92 <!--
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93 <conditional name="camera">
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94 <param name="doCamera" type="select" label="Run the edgeR implementation of Camera GSEA for up/down gene sets"
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95 help="If yes, you can choose a set of genesets to test and/or supply a gmt format geneset collection from your history">
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96 <option value="F" selected="true">Do not run GSEA tests with the Camera algorithm</option>
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97 <option value="T">Run GSEA tests with the Camera algorithm</option>
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98 </param>
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99 <when value="T">
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100 <conditional name="gmtSource">
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101 <param name="refgmtSource" type="select"
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102 label="Use a gene set (.gmt) from your history and/or use a built-in (MSigDB etc) gene set">
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103 <option value="indexed" selected="true">Use a built-in gene set</option>
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104 <option value="history">Use a gene set from my history</option>
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105 <option value="both">Add a gene set from my history to a built in gene set</option>
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106 </param>
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107 <when value="indexed">
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108 <param name="builtinGMT" type="select" label="Select a gene set matrix (.gmt) file to use for the analysis">
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109 <options from_data_table="gseaGMT_3.1">
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110 <filter type="sort_by" column="2" />
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111 <validator type="no_options" message="No GMT v3.1 files are available - please install them"/>
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112 </options>
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113 </param>
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114 </when>
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115 <when value="history">
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116 <param name="ownGMT" type="data" format="gmt" label="Select a Gene Set from your history" />
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117 </when>
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118 <when value="both">
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119 <param name="ownGMT" type="data" format="gseagmt" label="Select a Gene Set from your history" />
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120 <param name="builtinGMT" type="select" label="Select a gene set matrix (.gmt) file to use for the analysis">
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121 <options from_data_table="gseaGMT_4">
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122 <filter type="sort_by" column="2" />
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123 <validator type="no_options" message="No GMT v4 files are available - please fix tool_data_table and loc files"/>
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124 </options>
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125 </param>
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126 </when>
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127 </conditional>
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128 </when>
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129 <when value="F">
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130 </when>
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131 </conditional>
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132 -->
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133 <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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134 help="Conventional default value of 0.05 recommended"/>
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135 <param name="fdrtype" type="select" label="FDR (Type II error) control method"
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136 help="Use fdr or bh typically to control for the number of tests in a reliable way">
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137 <option value="fdr" selected="true">fdr</option>
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138 <option value="BH">Benjamini Hochberg</option>
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139 <option value="BY">Benjamini Yukateli</option>
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140 <option value="bonferroni">Bonferroni</option>
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141 <option value="hochberg">Hochberg</option>
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142 <option value="holm">Holm</option>
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143 <option value="hommel">Hommel</option>
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144 <option value="none">no control for multiple tests</option>
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145 </param>
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146 </inputs>
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147 <outputs>
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148 <data format="tabular" name="out_edgeR" label="${title}_topTable_edgeR.xls">
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149 <filter>edgeR['doedgeR'] == "T"</filter>
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150 </data>
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151 <data format="tabular" name="out_DESeq2" label="${title}_topTable_DESeq2.xls">
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152 <filter>DESeq2['doDESeq2'] == "T"</filter>
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153 </data>
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154 <data format="tabular" name="out_VOOM" label="${title}_topTable_VOOM.xls">
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155 <filter>doVoom == "T"</filter>
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156 </data>
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157 <data format="html" name="html_file" label="${title}.html"/>
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158 </outputs>
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159 <stdio>
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160 <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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161 </stdio>
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162 <tests>
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163 <test>
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164 <param name='input1' value='test_bams2mx.xls' ftype='tabular' />
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165 <param name='treatment_name' value='liver' />
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166 <param name='title' value='edgeRtest' />
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167 <param name='useNDF' value='' />
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168 <param name='doedgeR' value='T' />
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169 <param name='doVoom' value='T' />
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170 <param name='doDESeq2' value='T' />
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171 <param name='fdrtype' value='fdr' />
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172 <param name='edgeR_priordf' value="8" />
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173 <param name='fdrthresh' value="0.05" />
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174 <param name='control_name' value='heart' />
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175 <param name='subjectids' value='' />
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176 <param name='Control_cols' value='3,4,5,9' />
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177 <param name='Treat_cols' value='2,6,7,8' />
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178 <output name='out_edgeR' file='edgeRtest1out.xls' compare='diff' />
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179 <output name='html_file' file='edgeRtest1out.html' compare='diff' lines_diff='20' />
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180 </test>
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181 </tests>
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182
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183 <configfiles>
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184 <configfile name="runme">
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185 <![CDATA[
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186 #
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187 # edgeR.Rscript
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188 # updated npv 2011 for R 2.14.0 and edgeR 2.4.0 by ross
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189 # Performs DGE on a count table containing n replicates of two conditions
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190 #
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191 # Parameters
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192 #
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193 # 1 - Output Dir
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194
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195 # Original edgeR code by: S.Lunke and A.Kaspi
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196 reallybig = log10(.Machine\$double.xmax)
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197 reallysmall = log10(.Machine\$double.xmin)
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198 library('stringr')
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199 library('gplots')
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200 library('edgeR')
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201 hmap2 = function(cmat,nsamp=100,outpdfname='heatmap2.pdf', TName='Treatment',group=NA,myTitle='title goes here')
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202 {
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203 # Perform clustering for significant pvalues after controlling FWER
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204 samples = colnames(cmat)
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205 gu = unique(group)
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206 gn = rownames(cmat)
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207 if (length(gu) == 2) {
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208 col.map = function(g) {if (g==gu[1]) "#FF0000" else "#0000FF"}
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209 pcols = unlist(lapply(group,col.map))
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210 } else {
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211 colours = rainbow(length(gu),start=0,end=4/6)
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212 pcols = colours[match(group,gu)] }
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213 dm = cmat[(! is.na(gn)),]
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214 # remove unlabelled hm rows
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215 nprobes = nrow(dm)
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216 # sub = paste('Showing',nprobes,'contigs ranked for evidence of differential abundance')
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217 if (nprobes > nsamp) {
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218 dm =dm[1:nsamp,]
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219 #sub = paste('Showing',nsamp,'contigs ranked for evidence for differential abundance out of',nprobes,'total')
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220 }
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221 newcolnames = substr(colnames(dm),1,20)
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222 colnames(dm) = newcolnames
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223 pdf(outpdfname)
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224 heatmap.2(dm,main=myTitle,ColSideColors=pcols,col=topo.colors(100),dendrogram="col",key=T,density.info='none',
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225 Rowv=F,scale='row',trace='none',margins=c(8,8),cexRow=0.4,cexCol=0.5)
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226 dev.off()
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227 }
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228
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229 hmap = function(cmat,nmeans=4,outpdfname="heatMap.pdf",nsamp=250,TName='Treatment',group=NA,myTitle="Title goes here")
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230 {
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231 # for 2 groups only was
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232 #col.map = function(g) {if (g==TName) "#FF0000" else "#0000FF"}
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233 #pcols = unlist(lapply(group,col.map))
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234 gu = unique(group)
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235 colours = rainbow(length(gu),start=0.3,end=0.6)
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236 pcols = colours[match(group,gu)]
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237 nrows = nrow(cmat)
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238 mtitle = paste(myTitle,'Heatmap: n contigs =',nrows)
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239 if (nrows > nsamp) {
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240 cmat = cmat[c(1:nsamp),]
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241 mtitle = paste('Heatmap: Top ',nsamp,' DE contigs (of ',nrows,')',sep='')
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242 }
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243 newcolnames = substr(colnames(cmat),1,20)
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244 colnames(cmat) = newcolnames
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245 pdf(outpdfname)
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246 heatmap(cmat,scale='row',main=mtitle,cexRow=0.3,cexCol=0.4,Rowv=NA,ColSideColors=pcols)
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247 dev.off()
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248 }
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249
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250 qqPlot = function(descr='qqplot',pvector, outpdf='qqplot.pdf',...)
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251 # stolen from https://gist.github.com/703512
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252 {
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253 o = -log10(sort(pvector,decreasing=F))
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254 e = -log10( 1:length(o)/length(o) )
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255 o[o==-Inf] = reallysmall
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256 o[o==Inf] = reallybig
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257 maint = descr
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258 pdf(outpdf)
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259 plot(e,o,pch=19,cex=1, main=maint, ...,
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260 xlab=expression(Expected~~-log[10](italic(p))),
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261 ylab=expression(Observed~~-log[10](italic(p))),
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262 xlim=c(0,max(e)), ylim=c(0,max(o)))
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263 lines(e,e,col="red")
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264 grid(col = "lightgray", lty = "dotted")
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265 dev.off()
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266 }
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267
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268 smearPlot = function(DGEList,deTags, outSmear, outMain)
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269 {
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270 pdf(outSmear)
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271 plotSmear(DGEList,de.tags=deTags,main=outMain)
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272 grid(col="lightgray", lty="dotted")
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273 dev.off()
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274 }
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275
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276 boxPlot = function(rawrs,cleanrs,maint,myTitle,pdfname)
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277 { #
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278 nc = ncol(rawrs)
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279 #### for (i in c(1:nc)) {rawrs[(rawrs[,i] < 0),i] = NA}
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280 fullnames = colnames(rawrs)
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281 newcolnames = substr(colnames(rawrs),1,20)
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282 colnames(rawrs) = newcolnames
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283 newcolnames = substr(colnames(cleanrs),1,20)
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284 colnames(cleanrs) = newcolnames
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285 defpar = par(no.readonly=T)
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286 print.noquote('raw contig counts by sample:')
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287 print.noquote(summary(rawrs))
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288 print.noquote('normalised contig counts by sample:')
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289 print.noquote(summary(cleanrs))
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290 pdf(pdfname)
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291 par(mfrow=c(1,2))
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292 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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293 grid(col="lightgray",lty="dotted")
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294 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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295 grid(col="lightgray",lty="dotted")
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296 dev.off()
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297 pdfname = "sample_counts_histogram.pdf"
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298 nc = ncol(rawrs)
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299 print.noquote(paste('Using ncol rawrs=',nc))
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300 ncroot = round(sqrt(nc))
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301 if (ncroot*ncroot < nc) { ncroot = ncroot + 1 }
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302 m = c()
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303 for (i in c(1:nc)) {
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304 rhist = hist(rawrs[,i],breaks=100,plot=F)
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305 m = append(m,max(rhist\$counts))
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306 }
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307 ymax = max(m)
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308 ncols = length(fullnames)
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309 if (ncols > 20)
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310 {
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311 scale = 7*ncols/20
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312 pdf(pdfname,width=scale,height=scale)
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313 } else {
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314 pdf(pdfname)
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315 }
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316 par(mfrow=c(ncroot,ncroot))
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317 for (i in c(1:nc)) {
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318 hist(rawrs[,i], main=paste("Contig logcount",i), xlab='log raw count', col="maroon",
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319 breaks=100,sub=fullnames[i],cex=0.8,ylim=c(0,ymax))
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320 }
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321 dev.off()
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322 par(defpar)
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323
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|
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324 }
|
|
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325
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326 cumPlot = function(rawrs,cleanrs,maint,myTitle)
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|
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327 { # updated to use ecdf
|
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|
328 pdfname = "Filtering_rowsum_bar_charts.pdf"
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329 defpar = par(no.readonly=T)
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|
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330 lrs = log(rawrs,10)
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|
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331 lim = max(lrs)
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332 pdf(pdfname)
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333 par(mfrow=c(2,1))
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334 hist(lrs,breaks=100,main=paste('Before:',maint),xlab="# Reads (log)",
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335 ylab="Count",col="maroon",sub=myTitle, xlim=c(0,lim),las=1)
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336 grid(col="lightgray", lty="dotted")
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337 lrs = log(cleanrs,10)
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338 hist(lrs,breaks=100,main=paste('After:',maint),xlab="# Reads (log)",
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339 ylab="Count",col="maroon",sub=myTitle,xlim=c(0,lim),las=1)
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340 grid(col="lightgray", lty="dotted")
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341 dev.off()
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342 par(defpar)
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343 }
|
|
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344
|
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345 cumPlot1 = function(rawrs,cleanrs,maint,myTitle)
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346 { # updated to use ecdf
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347 pdfname = paste(gsub(" ","", myTitle , fixed=TRUE),"RowsumCum.pdf",sep='_')
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348 pdf(pdfname)
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349 par(mfrow=c(2,1))
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350 lastx = max(rawrs)
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351 rawe = knots(ecdf(rawrs))
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352 cleane = knots(ecdf(cleanrs))
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353 cy = 1:length(cleane)/length(cleane)
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354 ry = 1:length(rawe)/length(rawe)
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355 plot(rawe,ry,type='l',main=paste('Before',maint),xlab="Log Contig Total Reads",
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356 ylab="Cumulative proportion",col="maroon",log='x',xlim=c(1,lastx),sub=myTitle)
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357 grid(col="blue")
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358 plot(cleane,cy,type='l',main=paste('After',maint),xlab="Log Contig Total Reads",
|
|
|
359 ylab="Cumulative proportion",col="maroon",log='x',xlim=c(1,lastx),sub=myTitle)
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360 grid(col="blue")
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361 dev.off()
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|
362 }
|
|
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363
|
|
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364
|
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|
365
|
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366 doGSEA = function(y=NULL,design=NULL,histgmt="",
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367 bigmt="/data/genomes/gsea/3.1/Abetterchoice_nocgp_c2_c3_c5_symbols_all.gmt",
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368 ntest=0, myTitle="myTitle", outfname="GSEA.xls", minnin=5, maxnin=2000,fdrthresh=0.05,fdrtype="BH")
|
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|
369 {
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|
370 sink('Camera.log')
|
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|
371 genesets = c()
|
|
|
372 if (bigmt > "")
|
|
|
373 {
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|
374 bigenesets = readLines(bigmt)
|
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|
375 genesets = bigenesets
|
|
|
376 }
|
|
|
377 if (histgmt > "")
|
|
|
378 {
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|
|
379 hgenesets = readLines(histgmt)
|
|
|
380 if (bigmt > "") {
|
|
|
381 genesets = rbind(genesets,hgenesets)
|
|
|
382 } else {
|
|
|
383 genesets = hgenesets
|
|
|
384 } # use only history if no bi
|
|
|
385 }
|
|
|
386 print.noquote(paste("@@@read",length(genesets), 'genesets from',histgmt,bigmt))
|
|
|
387 genesets = strsplit(genesets,'\t') # tabular. genesetid\tURLorwhatever\tgene_1\t..\tgene_n
|
|
|
388 outf = outfname
|
|
|
389 head=paste(myTitle,'edgeR GSEA')
|
|
|
390 write(head,file=outfname,append=F)
|
|
|
391 ntest=length(genesets)
|
|
|
392 urownames = toupper(rownames(y))
|
|
|
393 upcam = c()
|
|
|
394 downcam = c()
|
|
|
395 for (i in 1:ntest) {
|
|
|
396 gs = unlist(genesets[i])
|
|
|
397 g = gs[1] # geneset_id
|
|
|
398 u = gs[2]
|
|
|
399 if (u > "") { u = paste("<a href=\'",u,"\'>",u,"</a>",sep="") }
|
|
|
400 glist = gs[3:length(gs)] # member gene symbols
|
|
|
401 glist = toupper(glist)
|
|
|
402 inglist = urownames %in% glist
|
|
|
403 nin = sum(inglist)
|
|
|
404 if ((nin > minnin) && (nin < maxnin)) {
|
|
|
405 ### print(paste('@@found',sum(inglist),'genes in glist'))
|
|
|
406 camres = camera(y=y,index=inglist,design=design)
|
|
|
407 if (! is.null(camres)) {
|
|
|
408 rownames(camres) = g # gene set name
|
|
|
409 camres = cbind(GeneSet=g,URL=u,camres)
|
|
|
410 if (camres\$Direction == "Up")
|
|
|
411 {
|
|
|
412 upcam = rbind(upcam,camres) } else {
|
|
|
413 downcam = rbind(downcam,camres)
|
|
|
414 }
|
|
|
415 }
|
|
|
416 }
|
|
|
417 }
|
|
|
418 uscam = upcam[order(upcam\$PValue),]
|
|
|
419 unadjp = uscam\$PValue
|
|
|
420 uscam\$adjPValue = p.adjust(unadjp,method=fdrtype)
|
|
|
421 nup = max(10,sum((uscam\$adjPValue < fdrthresh)))
|
|
|
422 dscam = downcam[order(downcam\$PValue),]
|
|
|
423 unadjp = dscam\$PValue
|
|
|
424 dscam\$adjPValue = p.adjust(unadjp,method=fdrtype)
|
|
|
425 ndown = max(10,sum((dscam\$adjPValue < fdrthresh)))
|
|
|
426 write.table(uscam,file=paste('camera_up',outfname,sep='_'),quote=F,sep='\t',row.names=F)
|
|
|
427 write.table(dscam,file=paste('camera_down',outfname,sep='_'),quote=F,sep='\t',row.names=F)
|
|
|
428 print.noquote(paste('@@@@@ Camera up top',nup,'gene sets:'))
|
|
|
429 write.table(head(uscam,nup),file="",quote=F,sep='\t',row.names=F)
|
|
|
430 print.noquote(paste('@@@@@ Camera down top',ndown,'gene sets:'))
|
|
|
431 write.table(head(dscam,ndown),file="",quote=F,sep='\t',row.names=F)
|
|
|
432 sink()
|
|
|
433 }
|
|
|
434
|
|
|
435
|
|
|
436
|
|
|
437
|
|
|
438 doGSEAatonce = function(y=NULL,design=NULL,histgmt="",
|
|
|
439 bigmt="/data/genomes/gsea/3.1/Abetterchoice_nocgp_c2_c3_c5_symbols_all.gmt",
|
|
|
440 ntest=0, myTitle="myTitle", outfname="GSEA.xls", minnin=5, maxnin=2000,fdrthresh=0.05,fdrtype="BH")
|
|
|
441 {
|
|
|
442 sink('Camera.log')
|
|
|
443 genesets = c()
|
|
|
444 if (bigmt > "")
|
|
|
445 {
|
|
|
446 bigenesets = readLines(bigmt)
|
|
|
447 genesets = bigenesets
|
|
|
448 }
|
|
|
449 if (histgmt > "")
|
|
|
450 {
|
|
|
451 hgenesets = readLines(histgmt)
|
|
|
452 if (bigmt > "") {
|
|
|
453 genesets = rbind(genesets,hgenesets)
|
|
|
454 } else {
|
|
|
455 genesets = hgenesets
|
|
|
456 } # use only history if no bi
|
|
|
457 }
|
|
|
458 print.noquote(paste("@@@read",length(genesets), 'genesets from',histgmt,bigmt))
|
|
|
459 genesets = strsplit(genesets,'\t') # tabular. genesetid\tURLorwhatever\tgene_1\t..\tgene_n
|
|
|
460 outf = outfname
|
|
|
461 head=paste(myTitle,'edgeR GSEA')
|
|
|
462 write(head,file=outfname,append=F)
|
|
|
463 ntest=length(genesets)
|
|
|
464 urownames = toupper(rownames(y))
|
|
|
465 upcam = c()
|
|
|
466 downcam = c()
|
|
|
467 incam = c()
|
|
|
468 urls = c()
|
|
|
469 gsids = c()
|
|
|
470 for (i in 1:ntest) {
|
|
|
471 gs = unlist(genesets[i])
|
|
|
472 gsid = gs[1] # geneset_id
|
|
|
473 url = gs[2]
|
|
|
474 if (url > "") { url = paste("<a href=\'",url,"\'>",url,"</a>",sep="") }
|
|
|
475 glist = gs[3:length(gs)] # member gene symbols
|
|
|
476 glist = toupper(glist)
|
|
|
477 inglist = urownames %in% glist
|
|
|
478 nin = sum(inglist)
|
|
|
479 if ((nin > minnin) && (nin < maxnin)) {
|
|
|
480 incam = c(incam,inglist)
|
|
|
481 gsids = c(gsids,gsid)
|
|
|
482 urls = c(urls,url)
|
|
|
483 }
|
|
|
484 }
|
|
|
485 incam = as.list(incam)
|
|
|
486 names(incam) = gsids
|
|
|
487 allcam = camera(y=y,index=incam,design=design)
|
|
|
488 allcamres = cbind(geneset=gsids,allcam,URL=urls)
|
|
|
489 for (i in 1:ntest) {
|
|
|
490 camres = allcamres[i]
|
|
|
491 res = try(test = (camres\$Direction == "Up"))
|
|
|
492 if ("try-error" %in% class(res)) {
|
|
|
493 cat("test failed, camres = :")
|
|
|
494 print.noquote(camres)
|
|
|
495 } else { if (camres\$Direction == "Up")
|
|
|
496 { upcam = rbind(upcam,camres)
|
|
|
497 } else { downcam = rbind(downcam,camres)
|
|
|
498 }
|
|
|
499
|
|
|
500 }
|
|
|
501 }
|
|
|
502 uscam = upcam[order(upcam\$PValue),]
|
|
|
503 unadjp = uscam\$PValue
|
|
|
504 uscam\$adjPValue = p.adjust(unadjp,method=fdrtype)
|
|
|
505 nup = max(10,sum((uscam\$adjPValue < fdrthresh)))
|
|
|
506 dscam = downcam[order(downcam\$PValue),]
|
|
|
507 unadjp = dscam\$PValue
|
|
|
508 dscam\$adjPValue = p.adjust(unadjp,method=fdrtype)
|
|
|
509 ndown = max(10,sum((dscam\$adjPValue < fdrthresh)))
|
|
|
510 write.table(uscam,file=paste('camera_up',outfname,sep='_'),quote=F,sep='\t',row.names=F)
|
|
|
511 write.table(dscam,file=paste('camera_down',outfname,sep='_'),quote=F,sep='\t',row.names=F)
|
|
|
512 print.noquote(paste('@@@@@ Camera up top',nup,'gene sets:'))
|
|
|
513 write.table(head(uscam,nup),file="",quote=F,sep='\t',row.names=F)
|
|
|
514 print.noquote(paste('@@@@@ Camera down top',ndown,'gene sets:'))
|
|
|
515 write.table(head(dscam,ndown),file="",quote=F,sep='\t',row.names=F)
|
|
|
516 sink()
|
|
|
517 }
|
|
|
518
|
|
|
519
|
|
|
520 edgeIt = function (Count_Matrix=c(),group=c(),out_edgeR=F,out_VOOM=F,out_DESeq2=F,fdrtype='fdr',priordf=5,
|
|
|
521 fdrthresh=0.05,outputdir='.', myTitle='Differential Counts',libSize=c(),useNDF=F,
|
|
|
522 filterquantile=0.2, subjects=c(),mydesign=NULL,
|
|
|
523 doDESeq2=T,doVoom=T,doCamera=T,doedgeR=T,org='hg19',
|
|
|
524 histgmt="", bigmt="/data/genomes/gsea/3.1/Abetterchoice_nocgp_c2_c3_c5_symbols_all.gmt",
|
|
|
525 doCook=F,DESeq_fitType="parameteric")
|
|
|
526 {
|
|
|
527 # Error handling
|
|
|
528 if (length(unique(group))!=2){
|
|
|
529 print("Number of conditions identified in experiment does not equal 2")
|
|
|
530 q()
|
|
|
531 }
|
|
|
532 require(edgeR)
|
|
|
533 options(width = 512)
|
|
|
534 mt = paste(unlist(strsplit(myTitle,'_')),collapse=" ")
|
|
|
535 allN = nrow(Count_Matrix)
|
|
|
536 nscut = round(ncol(Count_Matrix)/2)
|
|
|
537 colTotmillionreads = colSums(Count_Matrix)/1e6
|
|
|
538 counts.dataframe = as.data.frame(c())
|
|
|
539 rawrs = rowSums(Count_Matrix)
|
|
|
540 nonzerod = Count_Matrix[(rawrs > 0),] # remove all zero count genes
|
|
|
541 nzN = nrow(nonzerod)
|
|
|
542 nzrs = rowSums(nonzerod)
|
|
|
543 zN = allN - nzN
|
|
|
544 print('# Quantiles for non-zero row counts:',quote=F)
|
|
|
545 print(quantile(nzrs,probs=seq(0,1,0.1)),quote=F)
|
|
|
546 if (useNDF == T)
|
|
|
547 {
|
|
|
548 gt1rpin3 = rowSums(Count_Matrix/expandAsMatrix(colTotmillionreads,dim(Count_Matrix)) >= 1) >= nscut
|
|
|
549 lo = colSums(Count_Matrix[!gt1rpin3,])
|
|
|
550 workCM = Count_Matrix[gt1rpin3,]
|
|
|
551 cleanrs = rowSums(workCM)
|
|
|
552 cleanN = length(cleanrs)
|
|
|
553 meth = paste( "After removing",length(lo),"contigs with fewer than ",nscut," sample read counts >= 1 per million, there are",sep="")
|
|
|
554 print(paste("Read",allN,"contigs. Removed",zN,"contigs with no reads.",meth,cleanN,"contigs"),quote=F)
|
|
|
555 maint = paste('Filter >=1/million reads in >=',nscut,'samples')
|
|
|
556 } else {
|
|
|
557 useme = (nzrs > quantile(nzrs,filterquantile))
|
|
|
558 workCM = nonzerod[useme,]
|
|
|
559 lo = colSums(nonzerod[!useme,])
|
|
|
560 cleanrs = rowSums(workCM)
|
|
|
561 cleanN = length(cleanrs)
|
|
|
562 meth = paste("After filtering at count quantile =",filterquantile,", there are",sep="")
|
|
|
563 print(paste('Read',allN,"contigs. Removed",zN,"with no reads.",meth,cleanN,"contigs"),quote=F)
|
|
|
564 maint = paste('Filter below',filterquantile,'quantile')
|
|
|
565 }
|
|
|
566 cumPlot(rawrs=rawrs,cleanrs=cleanrs,maint=maint,myTitle=myTitle)
|
|
|
567 allgenes = rownames(workCM)
|
|
|
568 reg = "^chr([0-9]+):([0-9]+)-([0-9]+)"
|
|
|
569 genecards="<a href=\'http://www.genecards.org/index.php?path=/Search/keyword/"
|
|
|
570 ucsc = paste("<a href=\'http://genome.ucsc.edu/cgi-bin/hgTracks?db=",org,sep='')
|
|
|
571 testreg = str_match(allgenes,reg)
|
|
|
572 if (sum(!is.na(testreg[,1]))/length(testreg[,1]) > 0.8) # is ucsc style string
|
|
|
573 {
|
|
|
574 print("@@ using ucsc substitution for urls")
|
|
|
575 contigurls = paste0(ucsc,"&position=chr",testreg[,2],":",testreg[,3],"-",testreg[,4],"\'>",allgenes,"</a>")
|
|
|
576 } else {
|
|
|
577 print.noquote("@@ using genecards substitution for urls")
|
|
|
578 contigurls = paste0(genecards,allgenes,"\'>",allgenes,"</a>")
|
|
|
579 }
|
|
|
580 print(paste("# Total low count contigs per sample = ",paste(lo,collapse=',')),quote=F)
|
|
|
581 cmrowsums = rowSums(workCM)
|
|
|
582 TName=unique(group)[1]
|
|
|
583 CName=unique(group)[2]
|
|
|
584 if (is.null(mydesign)) {
|
|
|
585 if (length(subjects) == 0)
|
|
|
586 {
|
|
|
587 mydesign = model.matrix(~group)
|
|
|
588 }
|
|
|
589 else {
|
|
|
590 subjf = factor(subjects)
|
|
|
591 mydesign = model.matrix(~subjf+group) # we block on subject so make group last to simplify finding it
|
|
|
592 }
|
|
|
593 }
|
|
|
594 print.noquote(paste('Using samples:',paste(colnames(workCM),collapse=',')))
|
|
|
595 print.noquote('Using design matrix:')
|
|
|
596 print.noquote(mydesign)
|
|
|
597 if (doedgeR) {
|
|
|
598 sink('edgeR.log')
|
|
|
599 #### Setup DGEList object
|
|
|
600 DGEList = DGEList(counts=workCM, group = group)
|
|
|
601 DGEList = calcNormFactors(DGEList)
|
|
|
602
|
|
|
603 DGEList = estimateGLMCommonDisp(DGEList,mydesign)
|
|
|
604 comdisp = DGEList\$common.dispersion
|
|
|
605 DGEList = estimateGLMTrendedDisp(DGEList,mydesign)
|
|
|
606 if (edgeR_priordf > 0) {
|
|
|
607 print.noquote(paste("prior.df =",edgeR_priordf))
|
|
|
608 DGEList = estimateGLMTagwiseDisp(DGEList,mydesign,prior.df = edgeR_priordf)
|
|
|
609 } else {
|
|
|
610 DGEList = estimateGLMTagwiseDisp(DGEList,mydesign)
|
|
|
611 }
|
|
|
612 DGLM = glmFit(DGEList,design=mydesign)
|
|
|
613 DE = glmLRT(DGLM,coef=ncol(DGLM\$design)) # always last one - subject is first if needed
|
|
|
614 efflib = DGEList\$samples\$lib.size*DGEList\$samples\$norm.factors
|
|
|
615 normData = (1e+06*DGEList\$counts/efflib)
|
|
|
616 uoutput = cbind(
|
|
|
617 Name=as.character(rownames(DGEList\$counts)),
|
|
|
618 DE\$table,
|
|
|
619 adj.p.value=p.adjust(DE\$table\$PValue, method=fdrtype),
|
|
|
620 Dispersion=DGEList\$tagwise.dispersion,totreads=cmrowsums,normData,
|
|
|
621 DGEList\$counts
|
|
|
622 )
|
|
|
623 soutput = uoutput[order(DE\$table\$PValue),] # sorted into p value order - for quick toptable
|
|
|
624 goodness = gof(DGLM, pcutoff=fdrthresh)
|
|
|
625 if (sum(goodness\$outlier) > 0) {
|
|
|
626 print.noquote('GLM outliers:')
|
|
|
627 print(paste(rownames(DGLM)[(goodness\$outlier)],collapse=','),quote=F)
|
|
|
628 } else {
|
|
|
629 print('No GLM fit outlier genes found\n')
|
|
|
630 }
|
|
|
631 z = limma::zscoreGamma(goodness\$gof.statistic, shape=goodness\$df/2, scale=2)
|
|
|
632 pdf("edgeR_GoodnessofFit.pdf")
|
|
|
633 qq = qqnorm(z, panel.first=grid(), main="tagwise dispersion")
|
|
|
634 abline(0,1,lwd=3)
|
|
|
635 points(qq\$x[goodness\$outlier],qq\$y[goodness\$outlier], pch=16, col="maroon")
|
|
|
636 dev.off()
|
|
|
637 estpriorn = getPriorN(DGEList)
|
|
|
638 print(paste("Common Dispersion =",comdisp,"CV = ",sqrt(comdisp),"getPriorN = ",estpriorn),quote=F)
|
|
|
639 efflib = DGEList\$samples\$lib.size*DGEList\$samples\$norm.factors
|
|
|
640 normData = (1e+06*DGEList\$counts)/efflib
|
|
|
641 lnormData = log(normData + 1e-6,10)
|
|
|
642 uniqueg = unique(group)
|
|
|
643 #### Plot MDS
|
|
|
644 sample_colors = match(group,levels(group))
|
|
|
645 sampleTypes = levels(factor(group))
|
|
|
646 print.noquote(sampleTypes)
|
|
|
647 pdf("edgeR_MDSplot.pdf")
|
|
|
648 plotMDS.DGEList(DGEList,main=paste("edgeR MDS for",myTitle),cex=0.5,col=sample_colors,pch=sample_colors)
|
|
|
649 legend(x="topleft", legend = sampleTypes,col=c(1:length(sampleTypes)), pch=19)
|
|
|
650 grid(col="blue")
|
|
|
651 dev.off()
|
|
|
652 colnames(normData) = paste( colnames(normData),'N',sep="_")
|
|
|
653 print(paste('Raw sample read totals',paste(colSums(nonzerod,na.rm=T),collapse=',')))
|
|
|
654 nzd = data.frame(log(nonzerod + 1e-2,10))
|
|
|
655 try( boxPlot(rawrs=nzd,cleanrs=lnormData,maint='TMM Normalisation',myTitle=myTitle,pdfname="edgeR_raw_norm_counts_box.pdf") )
|
|
|
656 write.table(soutput,file=out_edgeR, quote=FALSE, sep="\t",row.names=F)
|
|
|
657 tt = cbind(
|
|
|
658 Name=as.character(rownames(DGEList\$counts)),
|
|
|
659 DE\$table,
|
|
|
660 adj.p.value=p.adjust(DE\$table\$PValue, method=fdrtype),
|
|
|
661 Dispersion=DGEList\$tagwise.dispersion,totreads=cmrowsums
|
|
|
662 )
|
|
|
663 print.noquote("# edgeR Top tags\n")
|
|
|
664 tt = cbind(tt,URL=contigurls) # add to end so table isn't laid out strangely
|
|
|
665 tt = tt[order(DE\$table\$PValue),]
|
|
|
666 print.noquote(tt[1:50,])
|
|
|
667 deTags = rownames(uoutput[uoutput\$adj.p.value < fdrthresh,])
|
|
|
668 nsig = length(deTags)
|
|
|
669 print(paste('#',nsig,'tags significant at adj p=',fdrthresh),quote=F)
|
|
|
670 deColours = ifelse(deTags,'red','black')
|
|
|
671 pdf("edgeR_BCV_vs_abundance.pdf")
|
|
|
672 plotBCV(DGEList, cex=0.3, main="Biological CV vs abundance",col.tagwise=deColours)
|
|
|
673 dev.off()
|
|
|
674 dg = DGEList[order(DE\$table\$PValue),]
|
|
|
675 #normData = (1e+06 * dg\$counts/expandAsMatrix(dg\$samples\$lib.size, dim(dg)))
|
|
|
676 efflib = dg\$samples\$lib.size*dg\$samples\$norm.factors
|
|
|
677 normData = (1e+06*dg\$counts/efflib)
|
|
|
678 outpdfname="edgeR_top_100_heatmap.pdf"
|
|
|
679 hmap2(normData,nsamp=100,TName=TName,group=group,outpdfname=outpdfname,myTitle=paste('edgeR Heatmap',myTitle))
|
|
|
680 outSmear = "edgeR_smearplot.pdf"
|
|
|
681 outMain = paste("Smear Plot for ",TName,' Vs ',CName,' (FDR@',fdrthresh,' N = ',nsig,')',sep='')
|
|
|
682 smearPlot(DGEList=DGEList,deTags=deTags, outSmear=outSmear, outMain = outMain)
|
|
|
683 qqPlot(descr=paste(myTitle,'edgeR adj p QQ plot'),pvector=tt\$adj.p.value,outpdf='edgeR_qqplot.pdf')
|
|
|
684 norm.factor = DGEList\$samples\$norm.factors
|
|
|
685 topresults.edgeR = soutput[which(soutput\$adj.p.value < fdrthresh), ]
|
|
|
686 edgeRcountsindex = which(allgenes %in% rownames(topresults.edgeR))
|
|
|
687 edgeRcounts = rep(0, length(allgenes))
|
|
|
688 edgeRcounts[edgeRcountsindex] = 1 # Create venn diagram of hits
|
|
|
689 sink()
|
|
|
690 } ### doedgeR
|
|
|
691 if (doDESeq2 == T)
|
|
|
692 {
|
|
|
693 sink("DESeq2.log")
|
|
|
694 # DESeq2
|
|
|
695 require('DESeq2')
|
|
|
696 library('RColorBrewer')
|
|
|
697 if (length(subjects) == 0)
|
|
|
698 {
|
|
|
699 pdata = data.frame(Name=colnames(workCM),Rx=group,row.names=colnames(workCM))
|
|
|
700 deSEQds = DESeqDataSetFromMatrix(countData = workCM, colData = pdata, design = formula(~ Rx))
|
|
|
701 } else {
|
|
|
702 pdata = data.frame(Name=colnames(workCM),Rx=group,subjects=subjects,row.names=colnames(workCM))
|
|
|
703 deSEQds = DESeqDataSetFromMatrix(countData = workCM, colData = pdata, design = formula(~ subjects + Rx))
|
|
|
704 }
|
|
|
705 #DESeq2 = DESeq(deSEQds,fitType='local',pAdjustMethod=fdrtype)
|
|
|
706 #rDESeq = results(DESeq2)
|
|
|
707 #newCountDataSet(workCM, group)
|
|
|
708 deSeqDatsizefac = estimateSizeFactors(deSEQds)
|
|
|
709 deSeqDatdisp = estimateDispersions(deSeqDatsizefac,fitType=DESeq_fitType)
|
|
|
710 resDESeq = nbinomWaldTest(deSeqDatdisp, pAdjustMethod=fdrtype)
|
|
|
711 rDESeq = as.data.frame(results(resDESeq))
|
|
|
712 rDESeq = cbind(Contig=rownames(workCM),rDESeq,NReads=cmrowsums)
|
|
|
713 srDESeq = rDESeq[order(rDESeq\$pvalue),]
|
|
|
714 write.table(srDESeq,file=out_DESeq2, quote=FALSE, sep="\t",row.names=F)
|
|
|
715 qqPlot(descr=paste(myTitle,'DESeq2 adj p qq plot'),pvector=rDESeq\$padj,outpdf='DESeq2_qqplot.pdf')
|
|
|
716 cat("# DESeq top 50\n")
|
|
|
717 rDESeq = cbind(Contig=rownames(workCM),rDESeq,NReads=cmrowsums,URL=contigurls)
|
|
|
718 srDESeq = rDESeq[order(rDESeq\$pvalue),]
|
|
|
719 print.noquote(srDESeq[1:50,])
|
|
|
720 topresults.DESeq = rDESeq[which(rDESeq\$padj < fdrthresh), ]
|
|
|
721 DESeqcountsindex = which(allgenes %in% rownames(topresults.DESeq))
|
|
|
722 DESeqcounts = rep(0, length(allgenes))
|
|
|
723 DESeqcounts[DESeqcountsindex] = 1
|
|
|
724 pdf("DESeq2_dispersion_estimates.pdf")
|
|
|
725 plotDispEsts(resDESeq)
|
|
|
726 dev.off()
|
|
|
727 ysmall = abs(min(rDESeq\$log2FoldChange))
|
|
|
728 ybig = abs(max(rDESeq\$log2FoldChange))
|
|
|
729 ylimit = min(4,ysmall,ybig)
|
|
|
730 pdf("DESeq2_MA_plot.pdf")
|
|
|
731 plotMA(resDESeq,main=paste(myTitle,"DESeq2 MA plot"),ylim=c(-ylimit,ylimit))
|
|
|
732 dev.off()
|
|
|
733 rlogres = rlogTransformation(resDESeq)
|
|
|
734 sampledists = dist( t( assay(rlogres) ) )
|
|
|
735 sdmat = as.matrix(sampledists)
|
|
|
736 pdf("DESeq2_sample_distance_plot.pdf")
|
|
|
737 heatmap.2(sdmat,trace="none",main=paste(myTitle,"DESeq2 sample distances"),
|
|
|
738 col = colorRampPalette( rev(brewer.pal(9, "RdBu")) )(255))
|
|
|
739 dev.off()
|
|
|
740 ###outpdfname="DESeq2_top50_heatmap.pdf"
|
|
|
741 ###hmap2(sresDESeq,nsamp=50,TName=TName,group=group,outpdfname=outpdfname,myTitle=paste('DESeq2 vst rlog Heatmap',myTitle))
|
|
|
742 sink()
|
|
|
743 result = try( (ppca = plotPCA( varianceStabilizingTransformation(deSeqDatdisp,blind=T), intgroup=c("Rx","Name")) ) )
|
|
|
744 if ("try-error" %in% class(result)) {
|
|
|
745 print.noquote('DESeq2 plotPCA failed.')
|
|
|
746 } else {
|
|
|
747 pdf("DESeq2_PCA_plot.pdf")
|
|
|
748 #### wtf - print? Seems needed to get this to work
|
|
|
749 print(ppca)
|
|
|
750 dev.off()
|
|
|
751 }
|
|
|
752 }
|
|
|
753
|
|
|
754 if (doVoom == T) {
|
|
|
755 sink('Voom.log')
|
|
|
756 if (doedgeR == F) {
|
|
|
757 #### Setup DGEList object
|
|
|
758 DGEList = DGEList(counts=workCM, group = group)
|
|
|
759 DGEList = calcNormFactors(DGEList)
|
|
|
760 DGEList = estimateGLMCommonDisp(DGEList,mydesign)
|
|
|
761 DGEList = estimateGLMTrendedDisp(DGEList,mydesign)
|
|
|
762 DGEList = estimateGLMTagwiseDisp(DGEList,mydesign)
|
|
|
763 DGEList = estimateGLMTagwiseDisp(DGEList,mydesign)
|
|
|
764 norm.factor = DGEList\$samples\$norm.factors
|
|
|
765 }
|
|
|
766 pdf("Voom_mean_variance_plot.pdf")
|
|
|
767 dat.voomed = voom(DGEList, mydesign, plot = TRUE, lib.size = colSums(workCM) * norm.factor)
|
|
|
768 dev.off()
|
|
|
769 # Use limma to fit data
|
|
|
770 fit = lmFit(dat.voomed, mydesign)
|
|
|
771 fit = eBayes(fit)
|
|
|
772 rvoom = topTable(fit, coef = length(colnames(mydesign)), adj = fdrtype, n = Inf, sort="none")
|
|
|
773 qqPlot(descr=paste(myTitle,'Voom-limma adj p QQ plot'),pvector=rvoom\$adj.P.Val,outpdf='Voom_qqplot.pdf')
|
|
|
774 rownames(rvoom) = rownames(workCM)
|
|
|
775 rvoom = cbind(rvoom,NReads=cmrowsums)
|
|
|
776 srvoom = rvoom[order(rvoom\$P.Value),]
|
|
|
777 write.table(srvoom,file=out_VOOM, quote=FALSE, sep="\t",row.names=F)
|
|
|
778 rvoom = cbind(rvoom,URL=contigurls)
|
|
|
779 deTags = rownames(rvoom[rvoom\$adj.p.value < fdrthresh,])
|
|
|
780 nsig = length(deTags)
|
|
|
781 srvoom = rvoom[order(rvoom\$P.Value),]
|
|
|
782 cat("# Voom top 50\n")
|
|
|
783 print(srvoom[1:50,])
|
|
|
784 normData = srvoom\$E
|
|
|
785 outpdfname="VOOM_top_100_heatmap.pdf"
|
|
|
786 hmap2(normData,nsamp=100,TName=TName,group=group,outpdfname=outpdfname,myTitle=paste('VOOM Heatmap',myTitle))
|
|
|
787 outSmear = "VOOM_smearplot.pdf"
|
|
|
788 outMain = paste("Smear Plot for ",TName,' Vs ',CName,' (FDR@',fdrthresh,' N = ',nsig,')',sep='')
|
|
|
789 smearPlot(DGEList=rvoom,deTags=deTags, outSmear=outSmear, outMain = outMain)
|
|
|
790 qqPlot(descr=paste(myTitle,'VOOM adj p QQ plot'),pvector=srvoom\$adj.P.Val,outpdf='VOOM_qqplot.pdf')
|
|
|
791 # Use an FDR cutoff to find interesting samples for edgeR, DESeq and voom/limma
|
|
|
792 topresults.voom = rvoom[which(rvoom\$adj.P.Val < fdrthresh), ]
|
|
|
793 voomcountsindex = which(allgenes %in% topresults.voom\$ID)
|
|
|
794 voomcounts = rep(0, length(allgenes))
|
|
|
795 voomcounts[voomcountsindex] = 1
|
|
|
796 sink()
|
|
|
797 }
|
|
|
798
|
|
|
799 if (doCamera) {
|
|
|
800 doGSEA(y=DGEList,design=mydesign,histgmt=histgmt,bigmt=bigmt,ntest=20,myTitle=myTitle,
|
|
|
801 outfname=paste(mt,"GSEA.xls",sep="_"),fdrthresh=fdrthresh,fdrtype=fdrtype)
|
|
|
802 }
|
|
|
803
|
|
|
804 if ((doDESeq2==T) || (doVoom==T) || (doedgeR==T)) {
|
|
|
805 if ((doVoom==T) && (doDESeq2==T) && (doedgeR==T)) {
|
|
|
806 vennmain = paste(mt,'Voom,edgeR and DESeq2 overlap at FDR=',fdrthresh)
|
|
|
807 counts.dataframe = data.frame(edgeR = edgeRcounts, DESeq2 = DESeqcounts,
|
|
|
808 VOOM_limma = voomcounts, row.names = allgenes)
|
|
|
809 } else if ((doDESeq2==T) && (doedgeR==T)) {
|
|
|
810 vennmain = paste(mt,'DESeq2 and edgeR overlap at FDR=',fdrthresh)
|
|
|
811 counts.dataframe = data.frame(edgeR = edgeRcounts, DESeq2 = DESeqcounts, row.names = allgenes)
|
|
|
812 } else if ((doVoom==T) && (doedgeR==T)) {
|
|
|
813 vennmain = paste(mt,'Voom and edgeR overlap at FDR=',fdrthresh)
|
|
|
814 counts.dataframe = data.frame(edgeR = edgeRcounts, VOOM_limma = voomcounts, row.names = allgenes)
|
|
|
815 }
|
|
|
816
|
|
|
817 if (nrow(counts.dataframe > 1)) {
|
|
|
818 counts.venn = vennCounts(counts.dataframe)
|
|
|
819 vennf = "Venn_significant_genes_overlap.pdf"
|
|
|
820 pdf(vennf)
|
|
|
821 vennDiagram(counts.venn,main=vennmain,col="maroon")
|
|
|
822 dev.off()
|
|
|
823 }
|
|
|
824 } #### doDESeq2 or doVoom
|
|
|
825
|
|
|
826 }
|
|
|
827 #### Done
|
|
|
828
|
|
|
829 ###sink(stdout(),append=T,type="message")
|
|
|
830 builtin_gmt = ""
|
|
|
831 history_gmt = ""
|
|
|
832 history_gmt_name = ""
|
|
|
833 out_edgeR = F
|
|
|
834 out_DESeq2 = F
|
|
|
835 out_VOOM = "$out_VOOM"
|
|
|
836 doDESeq2 = $DESeq2.doDESeq2 # make these T or F
|
|
|
837 doVoom = $doVoom
|
|
|
838 doCamera = F
|
|
|
839 doedgeR = $edgeR.doedgeR
|
|
|
840 edgeR_priordf = 0
|
|
|
841
|
|
|
842
|
|
|
843 #if $doVoom == "T":
|
|
|
844 out_VOOM = "$out_VOOM"
|
|
|
845 #end if
|
|
|
846
|
|
|
847 #if $DESeq2.doDESeq2 == "T":
|
|
|
848 out_DESeq2 = "$out_DESeq2"
|
|
|
849 DESeq_fitType = "$DESeq2.DESeq_fitType"
|
|
|
850 #end if
|
|
|
851
|
|
|
852 #if $edgeR.doedgeR == "T":
|
|
|
853 out_edgeR = "$out_edgeR"
|
|
|
854 edgeR_priordf = $edgeR.edgeR_priordf
|
|
|
855 #end if
|
|
|
856
|
|
|
857
|
|
|
858 if (sum(c(doedgeR,doVoom,doDESeq2)) == 0)
|
|
|
859 {
|
|
|
860 write("No methods chosen - nothing to do! Please try again after choosing one or more methods", stderr())
|
|
|
861 quit(save="no",status=2)
|
|
|
862 }
|
|
|
863
|
|
|
864 Out_Dir = "$html_file.files_path"
|
|
|
865 Input = "$input1"
|
|
|
866 TreatmentName = "$treatment_name"
|
|
|
867 TreatmentCols = "$Treat_cols"
|
|
|
868 ControlName = "$control_name"
|
|
|
869 ControlCols= "$Control_cols"
|
|
|
870 org = "$input1.dbkey"
|
|
|
871 if (org == "") { org = "hg19"}
|
|
|
872 fdrtype = "$fdrtype"
|
|
|
873 fdrthresh = $fdrthresh
|
|
|
874 useNDF = $useNDF
|
|
|
875 fQ = $fQ # non-differential centile cutoff
|
|
|
876 myTitle = "$title"
|
|
|
877 sids = strsplit("$subjectids",',')
|
|
|
878 subjects = unlist(sids)
|
|
|
879 nsubj = length(subjects)
|
|
|
880 TCols = as.numeric(strsplit(TreatmentCols,",")[[1]])-1
|
|
|
881 CCols = as.numeric(strsplit(ControlCols,",")[[1]])-1
|
|
|
882 cat('Got TCols=')
|
|
|
883 cat(TCols)
|
|
|
884 cat('; CCols=')
|
|
|
885 cat(CCols)
|
|
|
886 cat('\n')
|
|
|
887 useCols = c(TCols,CCols)
|
|
|
888 if (file.exists(Out_Dir) == F) dir.create(Out_Dir)
|
|
|
889 Count_Matrix = read.table(Input,header=T,row.names=1,sep='\t') #Load tab file assume header
|
|
|
890 snames = colnames(Count_Matrix)
|
|
|
891 nsamples = length(snames)
|
|
|
892 if (nsubj > 0 & nsubj != nsamples) {
|
|
|
893 options("show.error.messages"=T)
|
|
|
894 mess = paste('Fatal error: Supplied subject id list',paste(subjects,collapse=','),
|
|
|
895 'has length',nsubj,'but there are',nsamples,'samples',paste(snames,collapse=','))
|
|
|
896 write(mess, stderr())
|
|
|
897 quit(save="no",status=4)
|
|
|
898 }
|
|
|
899 if (length(subjects) != 0) {subjects = subjects[useCols]}
|
|
|
900 Count_Matrix = Count_Matrix[,useCols] ### reorder columns
|
|
|
901 rn = rownames(Count_Matrix)
|
|
|
902 islib = rn %in% c('librarySize','NotInBedRegions')
|
|
|
903 LibSizes = Count_Matrix[subset(rn,islib),][1] # take first
|
|
|
904 Count_Matrix = Count_Matrix[subset(rn,! islib),]
|
|
|
905 group = c(rep(TreatmentName,length(TCols)), rep(ControlName,length(CCols)) ) #Build a group descriptor
|
|
|
906 group = factor(group, levels=c(ControlName,TreatmentName))
|
|
|
907 colnames(Count_Matrix) = paste(group,colnames(Count_Matrix),sep="_") #Relable columns
|
|
|
908 results = edgeIt(Count_Matrix=Count_Matrix,group=group, out_edgeR=out_edgeR, out_VOOM=out_VOOM, out_DESeq2=out_DESeq2,
|
|
|
909 fdrtype='BH',mydesign=NULL,priordf=edgeR_priordf,fdrthresh=fdrthresh,outputdir='.',
|
|
|
910 myTitle=myTitle,useNDF=F,libSize=c(),filterquantile=fQ,subjects=subjects,
|
|
|
911 doDESeq2=doDESeq2,doVoom=doVoom,doCamera=doCamera,doedgeR=doedgeR,org=org,
|
|
|
912 histgmt=history_gmt,bigmt=builtin_gmt,DESeq_fitType=DESeq_fitType)
|
|
|
913 sessionInfo()
|
|
|
914 ]]>
|
|
|
915 </configfile>
|
|
|
916 </configfiles>
|
|
|
917 <help>
|
|
|
918
|
|
|
919 **What it does**
|
|
|
920
|
|
|
921 Allows short read sequence counts from controlled experiments to be analysed for differentially expressed genes.
|
|
|
922 Optionally adds a term for subject if not all samples are independent or if some other factor needs to be blocked in the design.
|
|
|
923
|
|
|
924 **Input**
|
|
|
925
|
|
|
926 Requires a count matrix as a tabular file. These are best made using the companion HTSeq_ based counter Galaxy wrapper
|
|
|
927 and your fave gene model to generate inputs. Each row is a genomic feature (gene or exon eg) and each column the
|
|
|
928 non-negative integer count of reads from one sample overlapping the feature.
|
|
|
929 The matrix must have a header row uniquely identifying the source samples, and unique row names in
|
|
|
930 the first column. Typically the row names are gene symbols or probe ids for downstream use in GSEA and other methods.
|
|
|
931
|
|
|
932 **Specifying comparisons**
|
|
|
933
|
|
|
934 This is basically dumbed down for two factors - case vs control.
|
|
|
935
|
|
|
936 More complex interfaces are possible but painful at present.
|
|
|
937 Probably need to specify a phenotype file to do this better.
|
|
|
938 Work in progress. Send code.
|
|
|
939
|
|
|
940 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),
|
|
|
941 put a comma separated list of indicators for every sample (whether modelled or not!) indicating (eg) the subject number or
|
|
|
942 A list of integers, one for each subject or an empty string if samples are all independent.
|
|
|
943 If not empty, there must be exactly as many integers in the supplied integer list as there are columns (samples) in the count matrix.
|
|
|
944 Integers for samples that are not in the analysis *must* be present in the string as filler even if not used.
|
|
|
945
|
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946 So if you have 2 pairs out of 6 samples, you need to put in unique integers for the unpaired ones
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947 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
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948 8,9,1,1,2,2
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949 as subject IDs to indicate two paired samples from the same subject in columns 3/4 and 5/6
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950
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951 **Methods available**
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952
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953 You can run 3 popular Bioconductor packages available for count data.
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954
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955 edgeR - see edgeR_ for details
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956
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957 VOOM/limma - see limma_VOOM_ for details
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958
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959 DESeq2 - see DESeq2_ for details
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960
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961 and optionally camera in edgeR which works better if MSigDB is installed.
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962
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963 **Outputs**
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964
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965 Some helpful plots and analysis results. Note that most of these are produced using R code
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966 suggested by the excellent documentation and vignettes for the Bioconductor
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967 packages invoked. The Tool Factory is used to automatically lay these out for you to enjoy.
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968
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969 **Note on Voom**
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970
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971 The voom from limma version 3.16.6 help in R includes this from the authors - but you should read the paper to interpret this method.
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972
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973 This function is intended to process RNA-Seq or ChIP-Seq data prior to linear modelling in limma.
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974
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975 voom is an acronym for mean-variance modelling at the observational level.
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976 The key concern is to estimate the mean-variance relationship in the data, then use this to compute appropriate weights for each observation.
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977 Count data almost show non-trivial mean-variance relationships. Raw counts show increasing variance with increasing count size, while log-counts typically show a decreasing mean-variance trend.
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978 This function estimates the mean-variance trend for log-counts, then assigns a weight to each observation based on its predicted variance.
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979 The weights are then used in the linear modelling process to adjust for heteroscedasticity.
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980
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981 In an experiment, a count value is observed for each tag in each sample. A tag-wise mean-variance trend is computed using lowess.
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982 The tag-wise mean is the mean log2 count with an offset of 0.5, across samples for a given tag.
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983 The tag-wise variance is the quarter-root-variance of normalized log2 counts per million values with an offset of 0.5, across samples for a given tag.
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984 Tags with zero counts across all samples are not included in the lowess fit. Optional normalization is performed using normalizeBetweenArrays.
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985 Using fitted values of log2 counts from a linear model fit by lmFit, variances from the mean-variance trend were interpolated for each observation.
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986 This was carried out by approxfun. Inverse variance weights can be used to correct for mean-variance trend in the count data.
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987
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988
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989 Author(s)
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990
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991 Charity Law and Gordon Smyth
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992
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993 References
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994
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|
995 Law, CW (2013). Precision weights for gene expression analysis. PhD Thesis. University of Melbourne, Australia.
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996
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|
997 Law, CW, Chen, Y, Shi, W, Smyth, GK (2013). Voom! Precision weights unlock linear model analysis tools for RNA-seq read counts.
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|
998 Technical Report 1 May 2013, Bioinformatics Division, Walter and Eliza Hall Institute of Medical Reseach, Melbourne, Australia.
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|
999 http://www.statsci.org/smyth/pubs/VoomPreprint.pdf
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1000
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1001 See Also
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1002
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1003 A voom case study is given in the edgeR User's Guide.
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1004
|
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|
1005 vooma is a similar function but for microarrays instead of RNA-seq.
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1006
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1007
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|
1008 ***old rant on changes to Bioconductor package variable names between versions***
|
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1009
|
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|
1010 The edgeR authors made a small cosmetic change in the name of one important variable (from p.value to PValue)
|
|
|
1011 breaking this and all other code that assumed the old name for this variable,
|
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|
1012 between edgeR2.4.4 and 2.4.6 (the version for R 2.14 as at the time of writing).
|
|
|
1013 This means that all code using edgeR is sensitive to the version. I think this was a very unwise thing
|
|
|
1014 to do because it wasted hours of my time to track down and will similarly cost other edgeR users dearly
|
|
|
1015 when their old scripts break. This tool currently now works with 2.4.6.
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|
1016
|
|
|
1017 **Note on prior.N**
|
|
|
1018
|
|
|
1019 http://seqanswers.com/forums/showthread.php?t=5591 says:
|
|
|
1020
|
|
|
1021 *prior.n*
|
|
|
1022
|
|
|
1023 The value for prior.n determines the amount of smoothing of tagwise dispersions towards the common dispersion.
|
|
|
1024 You can think of it as like a "weight" for the common value. (It is actually the weight for the common likelihood
|
|
|
1025 in the weighted likelihood equation). The larger the value for prior.n, the more smoothing, i.e. the closer your
|
|
|
1026 tagwise dispersion estimates will be to the common dispersion. If you use a prior.n of 1, then that gives the
|
|
|
1027 common likelihood the weight of one observation.
|
|
|
1028
|
|
|
1029 In answer to your question, it is a good thing to squeeze the tagwise dispersions towards a common value,
|
|
|
1030 or else you will be using very unreliable estimates of the dispersion. I would not recommend using the value that
|
|
|
1031 you obtained from estimateSmoothing()---this is far too small and would result in virtually no moderation
|
|
|
1032 (squeezing) of the tagwise dispersions. How many samples do you have in your experiment?
|
|
|
1033 What is the experimental design? If you have few samples (less than 6) then I would suggest a prior.n of at least 10.
|
|
|
1034 If you have more samples, then the tagwise dispersion estimates will be more reliable,
|
|
|
1035 so you could consider using a smaller prior.n, although I would hesitate to use a prior.n less than 5.
|
|
|
1036
|
|
|
1037
|
|
|
1038 From Bioconductor Digest, Vol 118, Issue 5, Gordon writes:
|
|
|
1039
|
|
|
1040 Dear Dorota,
|
|
|
1041
|
|
|
1042 The important settings are prior.df and trend.
|
|
|
1043
|
|
|
1044 prior.n and prior.df are related through prior.df = prior.n * residual.df,
|
|
|
1045 and your experiment has residual.df = 36 - 12 = 24. So the old setting of
|
|
|
1046 prior.n=10 is equivalent for your data to prior.df = 240, a very large
|
|
|
1047 value. Going the other way, the new setting of prior.df=10 is equivalent
|
|
|
1048 to prior.n=10/24.
|
|
|
1049
|
|
|
1050 To recover old results with the current software you would use
|
|
|
1051
|
|
|
1052 estimateTagwiseDisp(object, prior.df=240, trend="none")
|
|
|
1053
|
|
|
1054 To get the new default from old software you would use
|
|
|
1055
|
|
|
1056 estimateTagwiseDisp(object, prior.n=10/24, trend=TRUE)
|
|
|
1057
|
|
|
1058 Actually the old trend method is equivalent to trend="loess" in the new
|
|
|
1059 software. You should use plotBCV(object) to see whether a trend is
|
|
|
1060 required.
|
|
|
1061
|
|
|
1062 Note you could also use
|
|
|
1063
|
|
|
1064 prior.n = getPriorN(object, prior.df=10)
|
|
|
1065
|
|
|
1066 to map between prior.df and prior.n.
|
|
|
1067
|
|
|
1068 ----
|
|
|
1069
|
|
|
1070 **Attributions**
|
|
|
1071
|
|
|
1072 edgeR - edgeR_
|
|
|
1073
|
|
|
1074 VOOM/limma - limma_VOOM_
|
|
|
1075
|
|
|
1076 DESeq2 - DESeq2_ for details
|
|
|
1077
|
|
|
1078 See above for Bioconductor package documentation for packages exposed in Galaxy by this tool and app store package.
|
|
|
1079
|
|
|
1080 Galaxy_ (that's what you are using right now!) for gluing everything together
|
|
|
1081
|
|
|
1082 Otherwise, all code and documentation comprising this tool was written by Ross Lazarus and is
|
|
|
1083 licensed to you under the LGPL_ like other rgenetics artefacts
|
|
|
1084
|
|
|
1085 .. _LGPL: http://www.gnu.org/copyleft/lesser.html
|
|
|
1086 .. _HTSeq: http://www-huber.embl.de/users/anders/HTSeq/doc/index.html
|
|
|
1087 .. _edgeR: http://www.bioconductor.org/packages/release/bioc/html/edgeR.html
|
|
|
1088 .. _DESeq2: http://www.bioconductor.org/packages/release/bioc/html/DESeq2.html
|
|
|
1089 .. _limma_VOOM: http://www.bioconductor.org/packages/release/bioc/html/limma.html
|
|
|
1090 .. _Galaxy: http://getgalaxy.org
|
|
|
1091 </help>
|
|
|
1092
|
|
|
1093 </tool>
|
|
|
1094
|
|
|
1095
|
