Mercurial > repos > fubar > differential_count_models
annotate rgedgeRpaired_nocamera.xml @ 50:60aceade5350 draft
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author | fubar |
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date | Mon, 23 Dec 2013 21:36:34 -0500 |
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50
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1 <tool id="rgDifferentialCount" name="Differential_Count" version="0.23"> |
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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="3.11.11">atlas</requirement> |
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5 <requirement type="package" version="3.0.1">r3</requirement> |
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6 <requirement type="package" version="1.3.18">graphicsmagick</requirement> |
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7 <requirement type="package" version="9.07">ghostscript</requirement> |
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8 <requirement type="package" version="2.12">biocbasics</requirement> |
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9 </requirements> |
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10 |
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11 <command interpreter="python"> |
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12 rgToolFactory.py --script_path "$runme" --interpreter "Rscript" --tool_name "DifferentialCounts" |
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13 --output_dir "$html_file.files_path" --output_html "$html_file" --make_HTML "yes" |
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14 </command> |
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15 <inputs> |
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16 <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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17 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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18 <param name="title" type="text" value="Differential Counts" size="80" label="Title for job outputs" |
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19 help="Supply a meaningful name here to remind you what the outputs contain"> |
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20 <sanitizer invalid_char=""> |
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21 <valid initial="string.letters,string.digits"><add value="_" /> </valid> |
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22 </sanitizer> |
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23 </param> |
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24 <param name="treatment_name" type="text" value="Treatment" size="50" label="Treatment Name"/> |
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25 <param name="Treat_cols" label="Select columns containing treatment." type="data_column" data_ref="input1" numerical="True" |
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26 multiple="true" use_header_names="true" size="120" display="checkboxes"> |
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27 <validator type="no_options" message="Please select at least one column."/> |
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28 <sanitizer invalid_char=""> |
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29 <valid initial="string.letters,string.digits"><add value="_" /> </valid> |
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30 </sanitizer> |
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31 </param> |
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32 <param name="control_name" type="text" value="Control" size="50" label="Control Name"/> |
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33 <param name="Control_cols" label="Select columns containing control." type="data_column" data_ref="input1" numerical="True" |
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34 multiple="true" use_header_names="true" size="120" display="checkboxes" optional="true"> |
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35 <validator type="no_options" message="Please select at least one column."/> |
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36 <sanitizer invalid_char=""> |
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37 <valid initial="string.letters,string.digits"><add value="_" /> </valid> |
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38 </sanitizer> <validator type="no_options" message="Please select at least one column."/> |
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39 <sanitizer invalid_char=""> |
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40 <valid initial="string.letters,string.digits"><add value="_" /> </valid> |
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41 </sanitizer> |
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42 |
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43 </param> |
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44 <param name="subjectids" type="text" optional="true" size="120" value = "" |
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45 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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46 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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47 <sanitizer> |
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48 <valid initial="string.letters,string.digits"><add value="," /> </valid> |
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49 </sanitizer> |
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50 </param> |
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51 <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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52 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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53 <param name="useNDF" type="boolean" truevalue="T" falsevalue="F" checked="false" size="1" |
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54 label="Non differential filter - remove contigs below a threshold (1 per million) for half or more samples" |
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55 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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56 |
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57 <conditional name="edgeR"> |
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58 <param name="doedgeR" type="select" |
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59 label="Run this model using edgeR" |
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60 help="edgeR uses a negative binomial model and seems to be powerful, even with few replicates"> |
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61 <option value="F">Do not run edgeR</option> |
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62 <option value="T" selected="true">Run edgeR</option> |
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63 </param> |
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64 <when value="T"> |
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65 <param name="edgeR_priordf" type="integer" value="20" size="3" |
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66 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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67 help="0 = Use edgeR default. Use a small value to 'smooth' small samples. See edgeR docs and note below"/> |
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68 </when> |
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69 <when value="F"></when> |
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70 </conditional> |
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71 <conditional name="DESeq2"> |
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72 <param name="doDESeq2" type="select" |
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73 label="Run the same model with DESeq2 and compare findings" |
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74 help="DESeq2 is an update to the DESeq package. It uses different assumptions and methods to edgeR"> |
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75 <option value="F" selected="true">Do not run DESeq2</option> |
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76 <option value="T">Run DESeq2</option> |
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77 </param> |
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78 <when value="T"> |
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79 <param name="DESeq_fitType" type="select"> |
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80 <option value="parametric" selected="true">Parametric (default) fit for dispersions</option> |
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81 <option value="local">Local fit - this will automagically be used if parametric fit fails</option> |
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82 <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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83 </param> |
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84 </when> |
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85 <when value="F"> </when> |
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86 </conditional> |
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87 <param name="doVoom" type="select" |
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88 label="Run the same model with Voom/limma and compare findings" |
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89 help="Voom uses counts per million and a precise transformation of variance so count data can be analysed using limma"> |
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90 <option value="F" selected="true">Do not run VOOM</option> |
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91 <option value="T">Run VOOM</option> |
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92 </param> |
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93 <!-- |
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94 <conditional name="camera"> |
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95 <param name="doCamera" type="select" label="Run the edgeR implementation of Camera GSEA for up/down gene sets" |
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96 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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97 <option value="F" selected="true">Do not run GSEA tests with the Camera algorithm</option> |
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98 <option value="T">Run GSEA tests with the Camera algorithm</option> |
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99 </param> |
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100 <when value="T"> |
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101 <conditional name="gmtSource"> |
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102 <param name="refgmtSource" type="select" |
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103 label="Use a gene set (.gmt) from your history and/or use a built-in (MSigDB etc) gene set"> |
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104 <option value="indexed" selected="true">Use a built-in gene set</option> |
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105 <option value="history">Use a gene set from my history</option> |
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106 <option value="both">Add a gene set from my history to a built in gene set</option> |
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107 </param> |
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108 <when value="indexed"> |
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109 <param name="builtinGMT" type="select" label="Select a gene set matrix (.gmt) file to use for the analysis"> |
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110 <options from_data_table="gseaGMT_3.1"> |
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111 <filter type="sort_by" column="2" /> |
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112 <validator type="no_options" message="No GMT v3.1 files are available - please install them"/> |
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113 </options> |
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114 </param> |
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115 </when> |
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116 <when value="history"> |
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117 <param name="ownGMT" type="data" format="gmt" label="Select a Gene Set from your history" /> |
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118 </when> |
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119 <when value="both"> |
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120 <param name="ownGMT" type="data" format="gseagmt" label="Select a Gene Set from your history" /> |
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121 <param name="builtinGMT" type="select" label="Select a gene set matrix (.gmt) file to use for the analysis"> |
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122 <options from_data_table="gseaGMT_4"> |
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123 <filter type="sort_by" column="2" /> |
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124 <validator type="no_options" message="No GMT v4 files are available - please fix tool_data_table and loc files"/> |
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125 </options> |
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126 </param> |
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127 </when> |
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128 </conditional> |
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129 </when> |
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130 <when value="F"> |
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131 </when> |
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132 </conditional> |
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133 --> |
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134 <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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135 help="Conventional default value of 0.05 recommended"/> |
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136 <param name="fdrtype" type="select" label="FDR (Type II error) control method" |
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137 help="Use fdr or bh typically to control for the number of tests in a reliable way"> |
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138 <option value="fdr" selected="true">fdr</option> |
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139 <option value="BH">Benjamini Hochberg</option> |
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140 <option value="BY">Benjamini Yukateli</option> |
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141 <option value="bonferroni">Bonferroni</option> |
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142 <option value="hochberg">Hochberg</option> |
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143 <option value="holm">Holm</option> |
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144 <option value="hommel">Hommel</option> |
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145 <option value="none">no control for multiple tests</option> |
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146 </param> |
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147 </inputs> |
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148 <outputs> |
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149 <data format="tabular" name="out_edgeR" label="${title}_topTable_edgeR.xls"> |
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150 <filter>edgeR['doedgeR'] == "T"</filter> |
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151 </data> |
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152 <data format="tabular" name="out_DESeq2" label="${title}_topTable_DESeq2.xls"> |
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153 <filter>DESeq2['doDESeq2'] == "T"</filter> |
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154 </data> |
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155 <data format="tabular" name="out_VOOM" label="${title}_topTable_VOOM.xls"> |
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156 <filter>doVoom == "T"</filter> |
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157 </data> |
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158 <data format="html" name="html_file" label="${title}.html"/> |
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159 </outputs> |
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160 <stdio> |
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161 <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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162 </stdio> |
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163 <tests> |
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164 <test> |
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165 <param name='input1' value='test_bams2mx.xls' ftype='tabular' /> |
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166 <param name='treatment_name' value='liver' /> |
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167 <param name='title' value='edgeRtest' /> |
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168 <param name='useNDF' value='' /> |
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169 <param name='doedgeR' value='T' /> |
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170 <param name='doVoom' value='T' /> |
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171 <param name='doDESeq2' value='T' /> |
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172 <param name='fdrtype' value='fdr' /> |
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173 <param name='edgeR_priordf' value="8" /> |
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174 <param name='fdrthresh' value="0.05" /> |
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175 <param name='control_name' value='heart' /> |
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176 <param name='subjectids' value='' /> |
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177 <param name='Control_cols' value='3,4,5,9' /> |
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178 <param name='Treat_cols' value='2,6,7,8' /> |
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179 <output name='out_edgeR' file='edgeRtest1out.xls' compare='diff' /> |
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180 <output name='html_file' file='edgeRtest1out.html' compare='diff' lines_diff='20' /> |
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181 </test> |
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182 </tests> |
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183 |
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184 <configfiles> |
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185 <configfile name="runme"> |
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186 <![CDATA[ |
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187 # |
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188 # edgeR.Rscript |
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189 # updated npv 2011 for R 2.14.0 and edgeR 2.4.0 by ross |
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190 # Performs DGE on a count table containing n replicates of two conditions |
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191 # |
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192 # Parameters |
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193 # |
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194 # 1 - Output Dir |
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195 |
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196 # Original edgeR code by: S.Lunke and A.Kaspi |
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197 reallybig = log10(.Machine\$double.xmax) |
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198 reallysmall = log10(.Machine\$double.xmin) |
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199 library('stringr') |
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200 library('gplots') |
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201 library('edgeR') |
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202 hmap2 = function(cmat,nsamp=100,outpdfname='heatmap2.pdf', TName='Treatment',group=NA,myTitle='title goes here') |
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203 { |
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204 # Perform clustering for significant pvalues after controlling FWER |
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205 samples = colnames(cmat) |
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206 gu = unique(group) |
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207 gn = rownames(cmat) |
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208 if (length(gu) == 2) { |
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209 col.map = function(g) {if (g==gu[1]) "#FF0000" else "#0000FF"} |
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210 pcols = unlist(lapply(group,col.map)) |
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211 } else { |
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212 colours = rainbow(length(gu),start=0,end=4/6) |
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213 pcols = colours[match(group,gu)] } |
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214 dm = cmat[(! is.na(gn)),] |
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215 # remove unlabelled hm rows |
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216 nprobes = nrow(dm) |
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217 # sub = paste('Showing',nprobes,'contigs ranked for evidence of differential abundance') |
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218 if (nprobes > nsamp) { |
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219 dm =dm[1:nsamp,] |
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220 #sub = paste('Showing',nsamp,'contigs ranked for evidence for differential abundance out of',nprobes,'total') |
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221 } |
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222 newcolnames = substr(colnames(dm),1,20) |
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223 colnames(dm) = newcolnames |
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224 pdf(outpdfname) |
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225 heatmap.2(dm,main=myTitle,ColSideColors=pcols,col=topo.colors(100),dendrogram="col",key=T,density.info='none', |
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226 Rowv=F,scale='row',trace='none',margins=c(8,8),cexRow=0.4,cexCol=0.5) |
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227 dev.off() |
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228 } |
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229 |
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230 hmap = function(cmat,nmeans=4,outpdfname="heatMap.pdf",nsamp=250,TName='Treatment',group=NA,myTitle="Title goes here") |
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231 { |
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232 # for 2 groups only was |
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233 #col.map = function(g) {if (g==TName) "#FF0000" else "#0000FF"} |
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234 #pcols = unlist(lapply(group,col.map)) |
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235 gu = unique(group) |
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236 colours = rainbow(length(gu),start=0.3,end=0.6) |
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237 pcols = colours[match(group,gu)] |
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238 nrows = nrow(cmat) |
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239 mtitle = paste(myTitle,'Heatmap: n contigs =',nrows) |
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240 if (nrows > nsamp) { |
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241 cmat = cmat[c(1:nsamp),] |
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242 mtitle = paste('Heatmap: Top ',nsamp,' DE contigs (of ',nrows,')',sep='') |
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243 } |
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244 newcolnames = substr(colnames(cmat),1,20) |
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245 colnames(cmat) = newcolnames |
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246 pdf(outpdfname) |
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247 heatmap(cmat,scale='row',main=mtitle,cexRow=0.3,cexCol=0.4,Rowv=NA,ColSideColors=pcols) |
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248 dev.off() |
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249 } |
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250 |
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251 qqPlot = function(descr='qqplot',pvector, outpdf='qqplot.pdf',...) |
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252 # stolen from https://gist.github.com/703512 |
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253 { |
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254 o = -log10(sort(pvector,decreasing=F)) |
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255 e = -log10( 1:length(o)/length(o) ) |
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256 o[o==-Inf] = reallysmall |
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257 o[o==Inf] = reallybig |
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258 maint = descr |
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259 pdf(outpdf) |
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260 plot(e,o,pch=19,cex=1, main=maint, ..., |
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261 xlab=expression(Expected~~-log[10](italic(p))), |
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262 ylab=expression(Observed~~-log[10](italic(p))), |
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263 xlim=c(0,max(e)), ylim=c(0,max(o))) |
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264 lines(e,e,col="red") |
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265 grid(col = "lightgray", lty = "dotted") |
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266 dev.off() |
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267 } |
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268 |
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269 smearPlot = function(DGEList,deTags, outSmear, outMain) |
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270 { |
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271 pdf(outSmear) |
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272 plotSmear(DGEList,de.tags=deTags,main=outMain) |
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273 grid(col="lightgray", lty="dotted") |
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274 dev.off() |
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275 } |
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276 |
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277 boxPlot = function(rawrs,cleanrs,maint,myTitle,pdfname) |
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278 { # |
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279 nc = ncol(rawrs) |
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280 #### for (i in c(1:nc)) {rawrs[(rawrs[,i] < 0),i] = NA} |
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281 fullnames = colnames(rawrs) |
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282 newcolnames = substr(colnames(rawrs),1,20) |
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283 colnames(rawrs) = newcolnames |
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284 newcolnames = substr(colnames(cleanrs),1,20) |
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285 colnames(cleanrs) = newcolnames |
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286 defpar = par(no.readonly=T) |
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287 print.noquote('raw contig counts by sample:') |
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288 print.noquote(summary(rawrs)) |
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289 print.noquote('normalised contig counts by sample:') |
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290 print.noquote(summary(cleanrs)) |
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291 pdf(pdfname) |
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292 par(mfrow=c(1,2)) |
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293 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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294 grid(col="lightgray",lty="dotted") |
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295 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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296 grid(col="lightgray",lty="dotted") |
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297 dev.off() |
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298 pdfname = "sample_counts_histogram.pdf" |
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299 nc = ncol(rawrs) |
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300 print.noquote(paste('Using ncol rawrs=',nc)) |
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301 ncroot = round(sqrt(nc)) |
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302 if (ncroot*ncroot < nc) { ncroot = ncroot + 1 } |
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303 m = c() |
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304 for (i in c(1:nc)) { |
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305 rhist = hist(rawrs[,i],breaks=100,plot=F) |
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306 m = append(m,max(rhist\$counts)) |
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307 } |
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308 ymax = max(m) |
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309 ncols = length(fullnames) |
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310 if (ncols > 20) |
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311 { |
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312 scale = 7*ncols/20 |
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313 pdf(pdfname,width=scale,height=scale) |
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314 } else { |
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315 pdf(pdfname) |
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316 } |
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317 par(mfrow=c(ncroot,ncroot)) |
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318 for (i in c(1:nc)) { |
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319 hist(rawrs[,i], main=paste("Contig logcount",i), xlab='log raw count', col="maroon", |
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320 breaks=100,sub=fullnames[i],cex=0.8,ylim=c(0,ymax)) |
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321 } |
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322 dev.off() |
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323 par(defpar) |
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324 |
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325 } |
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326 |
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327 cumPlot = function(rawrs,cleanrs,maint,myTitle) |
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328 { # updated to use ecdf |
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329 pdfname = "Filtering_rowsum_bar_charts.pdf" |
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330 defpar = par(no.readonly=T) |
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331 lrs = log(rawrs,10) |
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332 lim = max(lrs) |
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333 pdf(pdfname) |
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334 par(mfrow=c(2,1)) |
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335 hist(lrs,breaks=100,main=paste('Before:',maint),xlab="# Reads (log)", |
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336 ylab="Count",col="maroon",sub=myTitle, xlim=c(0,lim),las=1) |
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337 grid(col="lightgray", lty="dotted") |
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338 lrs = log(cleanrs,10) |
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339 hist(lrs,breaks=100,main=paste('After:',maint),xlab="# Reads (log)", |
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340 ylab="Count",col="maroon",sub=myTitle,xlim=c(0,lim),las=1) |
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341 grid(col="lightgray", lty="dotted") |
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342 dev.off() |
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343 par(defpar) |
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344 } |
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345 |
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346 cumPlot1 = function(rawrs,cleanrs,maint,myTitle) |
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347 { # updated to use ecdf |
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348 pdfname = paste(gsub(" ","", myTitle , fixed=TRUE),"RowsumCum.pdf",sep='_') |
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349 pdf(pdfname) |
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350 par(mfrow=c(2,1)) |
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351 lastx = max(rawrs) |
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352 rawe = knots(ecdf(rawrs)) |
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353 cleane = knots(ecdf(cleanrs)) |
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354 cy = 1:length(cleane)/length(cleane) |
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355 ry = 1:length(rawe)/length(rawe) |
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356 plot(rawe,ry,type='l',main=paste('Before',maint),xlab="Log Contig Total Reads", |
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357 ylab="Cumulative proportion",col="maroon",log='x',xlim=c(1,lastx),sub=myTitle) |
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358 grid(col="blue") |
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359 plot(cleane,cy,type='l',main=paste('After',maint),xlab="Log Contig Total Reads", |
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360 ylab="Cumulative proportion",col="maroon",log='x',xlim=c(1,lastx),sub=myTitle) |
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361 grid(col="blue") |
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362 dev.off() |
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363 } |
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364 |
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365 |
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366 |
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367 doGSEA = function(y=NULL,design=NULL,histgmt="", |
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368 bigmt="/data/genomes/gsea/3.1/Abetterchoice_nocgp_c2_c3_c5_symbols_all.gmt", |
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369 ntest=0, myTitle="myTitle", outfname="GSEA.xls", minnin=5, maxnin=2000,fdrthresh=0.05,fdrtype="BH") |
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370 { |
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371 sink('Camera.log') |
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372 genesets = c() |
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373 if (bigmt > "") |
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374 { |
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375 bigenesets = readLines(bigmt) |
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376 genesets = bigenesets |
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377 } |
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378 if (histgmt > "") |
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379 { |
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380 hgenesets = readLines(histgmt) |
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381 if (bigmt > "") { |
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382 genesets = rbind(genesets,hgenesets) |
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383 } else { |
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384 genesets = hgenesets |
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385 } # use only history if no bi |
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386 } |
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387 print.noquote(paste("@@@read",length(genesets), 'genesets from',histgmt,bigmt)) |
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388 genesets = strsplit(genesets,'\t') # tabular. genesetid\tURLorwhatever\tgene_1\t..\tgene_n |
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389 outf = outfname |
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390 head=paste(myTitle,'edgeR GSEA') |
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391 write(head,file=outfname,append=F) |
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392 ntest=length(genesets) |
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393 urownames = toupper(rownames(y)) |
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394 upcam = c() |
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395 downcam = c() |
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396 for (i in 1:ntest) { |
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397 gs = unlist(genesets[i]) |
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398 g = gs[1] # geneset_id |
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399 u = gs[2] |
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400 if (u > "") { u = paste("<a href=\'",u,"\'>",u,"</a>",sep="") } |
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401 glist = gs[3:length(gs)] # member gene symbols |
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402 glist = toupper(glist) |
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403 inglist = urownames %in% glist |
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404 nin = sum(inglist) |
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405 if ((nin > minnin) && (nin < maxnin)) { |
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406 ### print(paste('@@found',sum(inglist),'genes in glist')) |
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407 camres = camera(y=y,index=inglist,design=design) |
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408 if (! is.null(camres)) { |
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409 rownames(camres) = g # gene set name |
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410 camres = cbind(GeneSet=g,URL=u,camres) |
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411 if (camres\$Direction == "Up") |
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412 { |
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413 upcam = rbind(upcam,camres) } else { |
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414 downcam = rbind(downcam,camres) |
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415 } |
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416 } |
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417 } |
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418 } |
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419 uscam = upcam[order(upcam\$PValue),] |
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420 unadjp = uscam\$PValue |
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421 uscam\$adjPValue = p.adjust(unadjp,method=fdrtype) |
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422 nup = max(10,sum((uscam\$adjPValue < fdrthresh))) |
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423 dscam = downcam[order(downcam\$PValue),] |
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424 unadjp = dscam\$PValue |
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425 dscam\$adjPValue = p.adjust(unadjp,method=fdrtype) |
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426 ndown = max(10,sum((dscam\$adjPValue < fdrthresh))) |
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427 write.table(uscam,file=paste('camera_up',outfname,sep='_'),quote=F,sep='\t',row.names=F) |
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428 write.table(dscam,file=paste('camera_down',outfname,sep='_'),quote=F,sep='\t',row.names=F) |
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429 print.noquote(paste('@@@@@ Camera up top',nup,'gene sets:')) |
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430 write.table(head(uscam,nup),file="",quote=F,sep='\t',row.names=F) |
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431 print.noquote(paste('@@@@@ Camera down top',ndown,'gene sets:')) |
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432 write.table(head(dscam,ndown),file="",quote=F,sep='\t',row.names=F) |
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433 sink() |
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434 } |
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435 |
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436 |
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437 |
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438 |
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439 doGSEAatonce = function(y=NULL,design=NULL,histgmt="", |
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440 bigmt="/data/genomes/gsea/3.1/Abetterchoice_nocgp_c2_c3_c5_symbols_all.gmt", |
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441 ntest=0, myTitle="myTitle", outfname="GSEA.xls", minnin=5, maxnin=2000,fdrthresh=0.05,fdrtype="BH") |
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442 { |
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443 sink('Camera.log') |
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444 genesets = c() |
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445 if (bigmt > "") |
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changeset
|
446 { |
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447 bigenesets = readLines(bigmt) |
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448 genesets = bigenesets |
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|
449 } |
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450 if (histgmt > "") |
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changeset
|
451 { |
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452 hgenesets = readLines(histgmt) |
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453 if (bigmt > "") { |
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454 genesets = rbind(genesets,hgenesets) |
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parents:
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455 } else { |
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456 genesets = hgenesets |
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457 } # use only history if no bi |
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458 } |
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459 print.noquote(paste("@@@read",length(genesets), 'genesets from',histgmt,bigmt)) |
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460 genesets = strsplit(genesets,'\t') # tabular. genesetid\tURLorwhatever\tgene_1\t..\tgene_n |
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461 outf = outfname |
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462 head=paste(myTitle,'edgeR GSEA') |
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463 write(head,file=outfname,append=F) |
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464 ntest=length(genesets) |
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465 urownames = toupper(rownames(y)) |
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466 upcam = c() |
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467 downcam = c() |
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468 incam = c() |
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469 urls = c() |
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470 gsids = c() |
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471 for (i in 1:ntest) { |
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472 gs = unlist(genesets[i]) |
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473 gsid = gs[1] # geneset_id |
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474 url = gs[2] |
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475 if (url > "") { url = paste("<a href=\'",url,"\'>",url,"</a>",sep="") } |
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476 glist = gs[3:length(gs)] # member gene symbols |
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477 glist = toupper(glist) |
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478 inglist = urownames %in% glist |
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479 nin = sum(inglist) |
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480 if ((nin > minnin) && (nin < maxnin)) { |
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481 incam = c(incam,inglist) |
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482 gsids = c(gsids,gsid) |
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483 urls = c(urls,url) |
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|
484 } |
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fubar
parents:
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changeset
|
485 } |
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486 incam = as.list(incam) |
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487 names(incam) = gsids |
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488 allcam = camera(y=y,index=incam,design=design) |
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489 allcamres = cbind(geneset=gsids,allcam,URL=urls) |
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490 for (i in 1:ntest) { |
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491 camres = allcamres[i] |
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492 res = try(test = (camres\$Direction == "Up")) |
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493 if ("try-error" %in% class(res)) { |
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494 cat("test failed, camres = :") |
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495 print.noquote(camres) |
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496 } else { if (camres\$Direction == "Up") |
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497 { upcam = rbind(upcam,camres) |
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498 } else { downcam = rbind(downcam,camres) |
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parents:
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changeset
|
499 } |
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parents:
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changeset
|
500 |
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parents:
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changeset
|
501 } |
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parents:
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changeset
|
502 } |
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503 uscam = upcam[order(upcam\$PValue),] |
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504 unadjp = uscam\$PValue |
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505 uscam\$adjPValue = p.adjust(unadjp,method=fdrtype) |
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506 nup = max(10,sum((uscam\$adjPValue < fdrthresh))) |
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507 dscam = downcam[order(downcam\$PValue),] |
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508 unadjp = dscam\$PValue |
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509 dscam\$adjPValue = p.adjust(unadjp,method=fdrtype) |
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510 ndown = max(10,sum((dscam\$adjPValue < fdrthresh))) |
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511 write.table(uscam,file=paste('camera_up',outfname,sep='_'),quote=F,sep='\t',row.names=F) |
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512 write.table(dscam,file=paste('camera_down',outfname,sep='_'),quote=F,sep='\t',row.names=F) |
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513 print.noquote(paste('@@@@@ Camera up top',nup,'gene sets:')) |
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514 write.table(head(uscam,nup),file="",quote=F,sep='\t',row.names=F) |
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515 print.noquote(paste('@@@@@ Camera down top',ndown,'gene sets:')) |
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516 write.table(head(dscam,ndown),file="",quote=F,sep='\t',row.names=F) |
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517 sink() |
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parents:
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changeset
|
518 } |
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parents:
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changeset
|
519 |
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parents:
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changeset
|
520 |
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521 edgeIt = function (Count_Matrix=c(),group=c(),out_edgeR=F,out_VOOM=F,out_DESeq2=F,fdrtype='fdr',priordf=5, |
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522 fdrthresh=0.05,outputdir='.', myTitle='Differential Counts',libSize=c(),useNDF=F, |
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523 filterquantile=0.2, subjects=c(),mydesign=NULL, |
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524 doDESeq2=T,doVoom=T,doCamera=T,doedgeR=T,org='hg19', |
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525 histgmt="", bigmt="/data/genomes/gsea/3.1/Abetterchoice_nocgp_c2_c3_c5_symbols_all.gmt", |
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526 doCook=F,DESeq_fitType="parameteric") |
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parents:
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changeset
|
527 { |
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changeset
|
528 # Error handling |
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529 if (length(unique(group))!=2){ |
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parents:
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530 print("Number of conditions identified in experiment does not equal 2") |
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fubar
parents:
diff
changeset
|
531 q() |
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fubar
parents:
diff
changeset
|
532 } |
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fubar
parents:
diff
changeset
|
533 require(edgeR) |
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changeset
|
534 options(width = 512) |
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535 mt = paste(unlist(strsplit(myTitle,'_')),collapse=" ") |
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parents:
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changeset
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536 allN = nrow(Count_Matrix) |
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changeset
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537 nscut = round(ncol(Count_Matrix)/2) |
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parents:
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changeset
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538 colTotmillionreads = colSums(Count_Matrix)/1e6 |
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parents:
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changeset
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539 counts.dataframe = as.data.frame(c()) |
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changeset
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540 rawrs = rowSums(Count_Matrix) |
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parents:
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changeset
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541 nonzerod = Count_Matrix[(rawrs > 0),] # remove all zero count genes |
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fubar
parents:
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changeset
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542 nzN = nrow(nonzerod) |
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parents:
diff
changeset
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543 nzrs = rowSums(nonzerod) |
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parents:
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changeset
|
544 zN = allN - nzN |
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parents:
diff
changeset
|
545 print('# Quantiles for non-zero row counts:',quote=F) |
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parents:
diff
changeset
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546 print(quantile(nzrs,probs=seq(0,1,0.1)),quote=F) |
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parents:
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changeset
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547 if (useNDF == T) |
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fubar
parents:
diff
changeset
|
548 { |
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parents:
diff
changeset
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549 gt1rpin3 = rowSums(Count_Matrix/expandAsMatrix(colTotmillionreads,dim(Count_Matrix)) >= 1) >= nscut |
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parents:
diff
changeset
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550 lo = colSums(Count_Matrix[!gt1rpin3,]) |
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parents:
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changeset
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551 workCM = Count_Matrix[gt1rpin3,] |
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parents:
diff
changeset
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552 cleanrs = rowSums(workCM) |
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parents:
diff
changeset
|
553 cleanN = length(cleanrs) |
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parents:
diff
changeset
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554 meth = paste( "After removing",length(lo),"contigs with fewer than ",nscut," sample read counts >= 1 per million, there are",sep="") |
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fubar
parents:
diff
changeset
|
555 print(paste("Read",allN,"contigs. Removed",zN,"contigs with no reads.",meth,cleanN,"contigs"),quote=F) |
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fubar
parents:
diff
changeset
|
556 maint = paste('Filter >=1/million reads in >=',nscut,'samples') |
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557 } else { |
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558 useme = (nzrs > quantile(nzrs,filterquantile)) |
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559 workCM = nonzerod[useme,] |
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560 lo = colSums(nonzerod[!useme,]) |
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561 cleanrs = rowSums(workCM) |
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562 cleanN = length(cleanrs) |
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563 meth = paste("After filtering at count quantile =",filterquantile,", there are",sep="") |
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564 print(paste('Read',allN,"contigs. Removed",zN,"with no reads.",meth,cleanN,"contigs"),quote=F) |
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565 maint = paste('Filter below',filterquantile,'quantile') |
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566 } |
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567 cumPlot(rawrs=rawrs,cleanrs=cleanrs,maint=maint,myTitle=myTitle) |
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568 allgenes = rownames(workCM) |
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569 reg = "^chr([0-9]+):([0-9]+)-([0-9]+)" |
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570 genecards="<a href=\'http://www.genecards.org/index.php?path=/Search/keyword/" |
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571 ucsc = paste("<a href=\'http://genome.ucsc.edu/cgi-bin/hgTracks?db=",org,sep='') |
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572 testreg = str_match(allgenes,reg) |
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573 if (sum(!is.na(testreg[,1]))/length(testreg[,1]) > 0.8) # is ucsc style string |
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574 { |
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575 print("@@ using ucsc substitution for urls") |
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576 contigurls = paste0(ucsc,"&position=chr",testreg[,2],":",testreg[,3],"-",testreg[,4],"\'>",allgenes,"</a>") |
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577 } else { |
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578 print.noquote("@@ using genecards substitution for urls") |
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579 contigurls = paste0(genecards,allgenes,"\'>",allgenes,"</a>") |
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580 } |
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581 print(paste("# Total low count contigs per sample = ",paste(lo,collapse=',')),quote=F) |
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582 cmrowsums = rowSums(workCM) |
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583 TName=unique(group)[1] |
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584 CName=unique(group)[2] |
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585 if (is.null(mydesign)) { |
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586 if (length(subjects) == 0) |
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587 { |
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588 mydesign = model.matrix(~group) |
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589 } |
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590 else { |
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591 subjf = factor(subjects) |
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592 mydesign = model.matrix(~subjf+group) # we block on subject so make group last to simplify finding it |
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593 } |
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594 } |
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595 print.noquote(paste('Using samples:',paste(colnames(workCM),collapse=','))) |
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596 print.noquote('Using design matrix:') |
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597 print.noquote(mydesign) |
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598 if (doedgeR) { |
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599 sink('edgeR.log') |
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600 #### Setup DGEList object |
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601 DGEList = DGEList(counts=workCM, group = group) |
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602 DGEList = calcNormFactors(DGEList) |
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603 |
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604 DGEList = estimateGLMCommonDisp(DGEList,mydesign) |
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605 comdisp = DGEList\$common.dispersion |
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606 DGEList = estimateGLMTrendedDisp(DGEList,mydesign) |
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607 if (edgeR_priordf > 0) { |
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608 print.noquote(paste("prior.df =",edgeR_priordf)) |
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609 DGEList = estimateGLMTagwiseDisp(DGEList,mydesign,prior.df = edgeR_priordf) |
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610 } else { |
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611 DGEList = estimateGLMTagwiseDisp(DGEList,mydesign) |
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612 } |
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613 DGLM = glmFit(DGEList,design=mydesign) |
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614 DE = glmLRT(DGLM,coef=ncol(DGLM\$design)) # always last one - subject is first if needed |
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615 efflib = DGEList\$samples\$lib.size*DGEList\$samples\$norm.factors |
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616 normData = (1e+06*DGEList\$counts/efflib) |
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617 uoutput = cbind( |
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618 Name=as.character(rownames(DGEList\$counts)), |
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619 DE\$table, |
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620 adj.p.value=p.adjust(DE\$table\$PValue, method=fdrtype), |
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621 Dispersion=DGEList\$tagwise.dispersion,totreads=cmrowsums,normData, |
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622 DGEList\$counts |
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623 ) |
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624 soutput = uoutput[order(DE\$table\$PValue),] # sorted into p value order - for quick toptable |
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625 goodness = gof(DGLM, pcutoff=fdrthresh) |
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626 if (sum(goodness\$outlier) > 0) { |
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627 print.noquote('GLM outliers:') |
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628 print(paste(rownames(DGLM)[(goodness\$outlier)],collapse=','),quote=F) |
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629 } else { |
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630 print('No GLM fit outlier genes found\n') |
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631 } |
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632 z = limma::zscoreGamma(goodness\$gof.statistic, shape=goodness\$df/2, scale=2) |
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633 pdf("edgeR_GoodnessofFit.pdf") |
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634 qq = qqnorm(z, panel.first=grid(), main="tagwise dispersion") |
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635 abline(0,1,lwd=3) |
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636 points(qq\$x[goodness\$outlier],qq\$y[goodness\$outlier], pch=16, col="maroon") |
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637 dev.off() |
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638 estpriorn = getPriorN(DGEList) |
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639 print(paste("Common Dispersion =",comdisp,"CV = ",sqrt(comdisp),"getPriorN = ",estpriorn),quote=F) |
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640 efflib = DGEList\$samples\$lib.size*DGEList\$samples\$norm.factors |
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641 normData = (1e+06*DGEList\$counts)/efflib |
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642 lnormData = log(normData + 1e-6,10) |
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643 uniqueg = unique(group) |
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644 #### Plot MDS |
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645 sample_colors = match(group,levels(group)) |
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646 sampleTypes = levels(factor(group)) |
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647 print.noquote(sampleTypes) |
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648 pdf("edgeR_MDSplot.pdf") |
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649 plotMDS.DGEList(DGEList,main=paste("edgeR MDS for",myTitle),cex=0.5,col=sample_colors,pch=sample_colors) |
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650 legend(x="topleft", legend = sampleTypes,col=c(1:length(sampleTypes)), pch=19) |
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651 grid(col="blue") |
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652 dev.off() |
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653 colnames(normData) = paste( colnames(normData),'N',sep="_") |
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654 print(paste('Raw sample read totals',paste(colSums(nonzerod,na.rm=T),collapse=','))) |
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655 nzd = data.frame(log(nonzerod + 1e-2,10)) |
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656 try( boxPlot(rawrs=nzd,cleanrs=lnormData,maint='TMM Normalisation',myTitle=myTitle,pdfname="edgeR_raw_norm_counts_box.pdf") ) |
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657 write.table(soutput,file=out_edgeR, quote=FALSE, sep="\t",row.names=F) |
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658 tt = cbind( |
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659 Name=as.character(rownames(DGEList\$counts)), |
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660 DE\$table, |
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661 adj.p.value=p.adjust(DE\$table\$PValue, method=fdrtype), |
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662 Dispersion=DGEList\$tagwise.dispersion,totreads=cmrowsums |
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663 ) |
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664 print.noquote("# edgeR Top tags\n") |
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665 tt = cbind(tt,URL=contigurls) # add to end so table isn't laid out strangely |
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666 tt = tt[order(DE\$table\$PValue),] |
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667 print.noquote(tt[1:50,]) |
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668 deTags = rownames(uoutput[uoutput\$adj.p.value < fdrthresh,]) |
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669 nsig = length(deTags) |
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670 print(paste('#',nsig,'tags significant at adj p=',fdrthresh),quote=F) |
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671 deColours = ifelse(deTags,'red','black') |
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672 pdf("edgeR_BCV_vs_abundance.pdf") |
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673 plotBCV(DGEList, cex=0.3, main="Biological CV vs abundance",col.tagwise=deColours) |
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674 dev.off() |
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675 dg = DGEList[order(DE\$table\$PValue),] |
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676 #normData = (1e+06 * dg\$counts/expandAsMatrix(dg\$samples\$lib.size, dim(dg))) |
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677 efflib = dg\$samples\$lib.size*dg\$samples\$norm.factors |
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678 normData = (1e+06*dg\$counts/efflib) |
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679 outpdfname="edgeR_top_100_heatmap.pdf" |
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680 hmap2(normData,nsamp=100,TName=TName,group=group,outpdfname=outpdfname,myTitle=paste('edgeR Heatmap',myTitle)) |
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681 outSmear = "edgeR_smearplot.pdf" |
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682 outMain = paste("Smear Plot for ",TName,' Vs ',CName,' (FDR@',fdrthresh,' N = ',nsig,')',sep='') |
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683 smearPlot(DGEList=DGEList,deTags=deTags, outSmear=outSmear, outMain = outMain) |
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684 qqPlot(descr=paste(myTitle,'edgeR adj p QQ plot'),pvector=tt\$adj.p.value,outpdf='edgeR_qqplot.pdf') |
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685 norm.factor = DGEList\$samples\$norm.factors |
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686 topresults.edgeR = soutput[which(soutput\$adj.p.value < fdrthresh), ] |
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687 edgeRcountsindex = which(allgenes %in% rownames(topresults.edgeR)) |
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688 edgeRcounts = rep(0, length(allgenes)) |
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689 edgeRcounts[edgeRcountsindex] = 1 # Create venn diagram of hits |
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690 sink() |
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691 } ### doedgeR |
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692 if (doDESeq2 == T) |
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693 { |
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694 sink("DESeq2.log") |
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695 # DESeq2 |
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696 require('DESeq2') |
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697 library('RColorBrewer') |
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698 if (length(subjects) == 0) |
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699 { |
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700 pdata = data.frame(Name=colnames(workCM),Rx=group,row.names=colnames(workCM)) |
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701 deSEQds = DESeqDataSetFromMatrix(countData = workCM, colData = pdata, design = formula(~ Rx)) |
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702 } else { |
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703 pdata = data.frame(Name=colnames(workCM),Rx=group,subjects=subjects,row.names=colnames(workCM)) |
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704 deSEQds = DESeqDataSetFromMatrix(countData = workCM, colData = pdata, design = formula(~ subjects + Rx)) |
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705 } |
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706 #DESeq2 = DESeq(deSEQds,fitType='local',pAdjustMethod=fdrtype) |
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707 #rDESeq = results(DESeq2) |
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708 #newCountDataSet(workCM, group) |
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709 deSeqDatsizefac = estimateSizeFactors(deSEQds) |
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710 deSeqDatdisp = estimateDispersions(deSeqDatsizefac,fitType=DESeq_fitType) |
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711 resDESeq = nbinomWaldTest(deSeqDatdisp, pAdjustMethod=fdrtype) |
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712 rDESeq = as.data.frame(results(resDESeq)) |
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713 rDESeq = cbind(Contig=rownames(workCM),rDESeq,NReads=cmrowsums) |
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714 srDESeq = rDESeq[order(rDESeq\$pvalue),] |
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715 write.table(srDESeq,file=out_DESeq2, quote=FALSE, sep="\t",row.names=F) |
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716 qqPlot(descr=paste(myTitle,'DESeq2 adj p qq plot'),pvector=rDESeq\$padj,outpdf='DESeq2_qqplot.pdf') |
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717 cat("# DESeq top 50\n") |
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718 rDESeq = cbind(Contig=rownames(workCM),rDESeq,NReads=cmrowsums,URL=contigurls) |
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719 srDESeq = rDESeq[order(rDESeq\$pvalue),] |
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720 print.noquote(srDESeq[1:50,]) |
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721 topresults.DESeq = rDESeq[which(rDESeq\$padj < fdrthresh), ] |
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722 DESeqcountsindex = which(allgenes %in% rownames(topresults.DESeq)) |
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723 DESeqcounts = rep(0, length(allgenes)) |
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724 DESeqcounts[DESeqcountsindex] = 1 |
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725 pdf("DESeq2_dispersion_estimates.pdf") |
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726 plotDispEsts(resDESeq) |
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727 dev.off() |
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728 ysmall = abs(min(rDESeq\$log2FoldChange)) |
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729 ybig = abs(max(rDESeq\$log2FoldChange)) |
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730 ylimit = min(4,ysmall,ybig) |
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|
731 pdf("DESeq2_MA_plot.pdf") |
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732 plotMA(resDESeq,main=paste(myTitle,"DESeq2 MA plot"),ylim=c(-ylimit,ylimit)) |
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733 dev.off() |
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734 rlogres = rlogTransformation(resDESeq) |
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735 sampledists = dist( t( assay(rlogres) ) ) |
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736 sdmat = as.matrix(sampledists) |
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737 pdf("DESeq2_sample_distance_plot.pdf") |
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738 heatmap.2(sdmat,trace="none",main=paste(myTitle,"DESeq2 sample distances"), |
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739 col = colorRampPalette( rev(brewer.pal(9, "RdBu")) )(255)) |
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740 dev.off() |
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741 ###outpdfname="DESeq2_top50_heatmap.pdf" |
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742 ###hmap2(sresDESeq,nsamp=50,TName=TName,group=group,outpdfname=outpdfname,myTitle=paste('DESeq2 vst rlog Heatmap',myTitle)) |
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743 sink() |
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744 result = try( (ppca = plotPCA( varianceStabilizingTransformation(deSeqDatdisp,blind=T), intgroup=c("Rx","Name")) ) ) |
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745 if ("try-error" %in% class(result)) { |
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746 print.noquote('DESeq2 plotPCA failed.') |
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747 } else { |
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748 pdf("DESeq2_PCA_plot.pdf") |
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|
749 #### wtf - print? Seems needed to get this to work |
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750 print(ppca) |
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751 dev.off() |
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|
752 } |
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|
753 } |
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|
754 |
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755 if (doVoom == T) { |
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756 sink('Voom.log') |
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757 if (doedgeR == F) { |
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|
758 #### Setup DGEList object |
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759 DGEList = DGEList(counts=workCM, group = group) |
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760 DGEList = calcNormFactors(DGEList) |
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761 DGEList = estimateGLMCommonDisp(DGEList,mydesign) |
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762 DGEList = estimateGLMTrendedDisp(DGEList,mydesign) |
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763 DGEList = estimateGLMTagwiseDisp(DGEList,mydesign) |
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764 DGEList = estimateGLMTagwiseDisp(DGEList,mydesign) |
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765 norm.factor = DGEList\$samples\$norm.factors |
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|
766 } |
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767 pdf("Voom_mean_variance_plot.pdf") |
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768 dat.voomed = voom(DGEList, mydesign, plot = TRUE, lib.size = colSums(workCM) * norm.factor) |
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769 dev.off() |
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770 # Use limma to fit data |
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771 fit = lmFit(dat.voomed, mydesign) |
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772 fit = eBayes(fit) |
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773 rvoom = topTable(fit, coef = length(colnames(mydesign)), adj = fdrtype, n = Inf, sort="none") |
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774 qqPlot(descr=paste(myTitle,'Voom-limma adj p QQ plot'),pvector=rvoom\$adj.P.Val,outpdf='Voom_qqplot.pdf') |
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|
775 rownames(rvoom) = rownames(workCM) |
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776 rvoom = cbind(rvoom,NReads=cmrowsums) |
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777 srvoom = rvoom[order(rvoom\$P.Value),] |
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778 write.table(srvoom,file=out_VOOM, quote=FALSE, sep="\t",row.names=F) |
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779 rvoom = cbind(rvoom,URL=contigurls) |
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780 deTags = rownames(rvoom[rvoom\$adj.p.value < fdrthresh,]) |
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781 nsig = length(deTags) |
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782 cat("# Voom top 50\n") |
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783 print(srvoom[1:50,]) |
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784 normData = srvoom\$E |
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785 outpdfname="Voom_top_100_heatmap.pdf" |
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786 hmap2(normData,nsamp=100,TName=TName,group=group,outpdfname=outpdfname,myTitle=paste('VOOM Heatmap',myTitle)) |
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787 outSmear = "Voom_smearplot.pdf" |
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788 outMain = paste("Smear Plot for ",TName,' Vs ',CName,' (FDR@',fdrthresh,' N = ',nsig,')',sep='') |
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789 smearPlot(DGEList=rvoom,deTags=deTags, outSmear=outSmear, outMain = outMain) |
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790 qqPlot(descr=paste(myTitle,'VOOM adj p QQ plot'),pvector=srvoom\$adj.P.Val,outpdf='Voom_qqplot.pdf') |
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791 # Use an FDR cutoff to find interesting samples for edgeR, DESeq and voom/limma |
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792 topresults.voom = rvoom[which(rvoom\$adj.P.Val < fdrthresh), ] |
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793 voomcountsindex = which(allgenes %in% topresults.voom\$ID) |
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794 voomcounts = rep(0, length(allgenes)) |
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795 voomcounts[voomcountsindex] = 1 |
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796 sink() |
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797 } |
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|
798 |
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799 if (doCamera) { |
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800 doGSEA(y=DGEList,design=mydesign,histgmt=histgmt,bigmt=bigmt,ntest=20,myTitle=myTitle, |
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801 outfname=paste(mt,"GSEA.xls",sep="_"),fdrthresh=fdrthresh,fdrtype=fdrtype) |
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|
802 } |
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|
803 |
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804 if ((doDESeq2==T) || (doVoom==T) || (doedgeR==T)) { |
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805 if ((doVoom==T) && (doDESeq2==T) && (doedgeR==T)) { |
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806 vennmain = paste(mt,'Voom,edgeR and DESeq2 overlap at FDR=',fdrthresh) |
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807 counts.dataframe = data.frame(edgeR = edgeRcounts, DESeq2 = DESeqcounts, |
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808 VOOM_limma = voomcounts, row.names = allgenes) |
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809 } else if ((doDESeq2==T) && (doedgeR==T)) { |
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810 vennmain = paste(mt,'DESeq2 and edgeR overlap at FDR=',fdrthresh) |
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811 counts.dataframe = data.frame(edgeR = edgeRcounts, DESeq2 = DESeqcounts, row.names = allgenes) |
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812 } else if ((doVoom==T) && (doedgeR==T)) { |
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813 vennmain = paste(mt,'Voom and edgeR overlap at FDR=',fdrthresh) |
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814 counts.dataframe = data.frame(edgeR = edgeRcounts, VOOM_limma = voomcounts, row.names = allgenes) |
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|
815 } |
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816 |
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817 if (nrow(counts.dataframe > 1)) { |
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818 counts.venn = vennCounts(counts.dataframe) |
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819 vennf = "Venn_significant_genes_overlap.pdf" |
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|
820 pdf(vennf) |
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821 vennDiagram(counts.venn,main=vennmain,col="maroon") |
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822 dev.off() |
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823 } |
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824 } #### doDESeq2 or doVoom |
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825 |
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826 } |
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827 #### Done |
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|
828 |
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829 ###sink(stdout(),append=T,type="message") |
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830 builtin_gmt = "" |
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831 history_gmt = "" |
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832 history_gmt_name = "" |
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833 out_edgeR = F |
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834 out_DESeq2 = F |
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835 out_VOOM = "$out_VOOM" |
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836 doDESeq2 = $DESeq2.doDESeq2 # make these T or F |
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837 doVoom = $doVoom |
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838 doCamera = F |
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839 doedgeR = $edgeR.doedgeR |
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840 edgeR_priordf = 0 |
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841 |
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842 |
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843 #if $doVoom == "T": |
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844 out_VOOM = "$out_VOOM" |
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845 #end if |
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846 |
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847 #if $DESeq2.doDESeq2 == "T": |
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848 out_DESeq2 = "$out_DESeq2" |
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849 DESeq_fitType = "$DESeq2.DESeq_fitType" |
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850 #end if |
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|
851 |
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852 #if $edgeR.doedgeR == "T": |
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853 out_edgeR = "$out_edgeR" |
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854 edgeR_priordf = $edgeR.edgeR_priordf |
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855 #end if |
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856 |
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857 |
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858 if (sum(c(doedgeR,doVoom,doDESeq2)) == 0) |
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859 { |
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860 write("No methods chosen - nothing to do! Please try again after choosing one or more methods", stderr()) |
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861 quit(save="no",status=2) |
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862 } |
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863 |
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864 Out_Dir = "$html_file.files_path" |
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865 Input = "$input1" |
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866 TreatmentName = "$treatment_name" |
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867 TreatmentCols = "$Treat_cols" |
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|
868 ControlName = "$control_name" |
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869 ControlCols= "$Control_cols" |
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870 org = "$input1.dbkey" |
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871 if (org == "") { org = "hg19"} |
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872 fdrtype = "$fdrtype" |
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873 fdrthresh = $fdrthresh |
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874 useNDF = $useNDF |
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875 fQ = $fQ # non-differential centile cutoff |
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876 myTitle = "$title" |
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|
877 sids = strsplit("$subjectids",',') |
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|
878 subjects = unlist(sids) |
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|
879 nsubj = length(subjects) |
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880 TCols = as.numeric(strsplit(TreatmentCols,",")[[1]])-1 |
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881 CCols = as.numeric(strsplit(ControlCols,",")[[1]])-1 |
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882 cat('Got TCols=') |
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883 cat(TCols) |
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884 cat('; CCols=') |
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885 cat(CCols) |
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886 cat('\n') |
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887 useCols = c(TCols,CCols) |
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888 if (file.exists(Out_Dir) == F) dir.create(Out_Dir) |
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889 Count_Matrix = read.table(Input,header=T,row.names=1,sep='\t') #Load tab file assume header |
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890 snames = colnames(Count_Matrix) |
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891 nsamples = length(snames) |
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892 if (nsubj > 0 & nsubj != nsamples) { |
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893 options("show.error.messages"=T) |
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894 mess = paste('Fatal error: Supplied subject id list',paste(subjects,collapse=','), |
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895 'has length',nsubj,'but there are',nsamples,'samples',paste(snames,collapse=',')) |
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896 write(mess, stderr()) |
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897 quit(save="no",status=4) |
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898 } |
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899 if (length(subjects) != 0) {subjects = subjects[useCols]} |
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900 Count_Matrix = Count_Matrix[,useCols] ### reorder columns |
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901 rn = rownames(Count_Matrix) |
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902 islib = rn %in% c('librarySize','NotInBedRegions') |
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903 LibSizes = Count_Matrix[subset(rn,islib),][1] # take first |
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904 Count_Matrix = Count_Matrix[subset(rn,! islib),] |
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905 group = c(rep(TreatmentName,length(TCols)), rep(ControlName,length(CCols)) ) #Build a group descriptor |
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906 group = factor(group, levels=c(ControlName,TreatmentName)) |
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907 colnames(Count_Matrix) = paste(group,colnames(Count_Matrix),sep="_") #Relable columns |
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908 results = edgeIt(Count_Matrix=Count_Matrix,group=group, out_edgeR=out_edgeR, out_VOOM=out_VOOM, out_DESeq2=out_DESeq2, |
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909 fdrtype='BH',mydesign=NULL,priordf=edgeR_priordf,fdrthresh=fdrthresh,outputdir='.', |
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910 myTitle=myTitle,useNDF=F,libSize=c(),filterquantile=fQ,subjects=subjects, |
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911 doDESeq2=doDESeq2,doVoom=doVoom,doCamera=doCamera,doedgeR=doedgeR,org=org, |
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912 histgmt=history_gmt,bigmt=builtin_gmt,DESeq_fitType=DESeq_fitType) |
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913 sessionInfo() |
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914 ]]> |
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915 </configfile> |
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916 </configfiles> |
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917 <help> |
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918 |
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919 **What it does** |
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920 |
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921 Allows short read sequence counts from controlled experiments to be analysed for differentially expressed genes. |
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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. |
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923 |
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924 **Input** |
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925 |
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926 Requires a count matrix as a tabular file. These are best made using the companion HTSeq_ based counter Galaxy wrapper |
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927 and your fave gene model to generate inputs. Each row is a genomic feature (gene or exon eg) and each column the |
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928 non-negative integer count of reads from one sample overlapping the feature. |
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929 The matrix must have a header row uniquely identifying the source samples, and unique row names in |
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930 the first column. Typically the row names are gene symbols or probe ids for downstream use in GSEA and other methods. |
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931 |
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932 **Specifying comparisons** |
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933 |
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934 This is basically dumbed down for two factors - case vs control. |
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935 |
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936 More complex interfaces are possible but painful at present. |
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937 Probably need to specify a phenotype file to do this better. |
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938 Work in progress. Send code. |
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939 |
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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), |
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941 put a comma separated list of indicators for every sample (whether modelled or not!) indicating (eg) the subject number or |
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942 A list of integers, one for each subject or an empty string if samples are all independent. |
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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. |
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944 Integers for samples that are not in the analysis *must* be present in the string as filler even if not used. |
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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) |
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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). |
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1013 This means that all code using edgeR is sensitive to the version. I think this was a very unwise thing |
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1014 to do because it wasted hours of my time to track down and will similarly cost other edgeR users dearly |
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1015 when their old scripts break. This tool currently now works with 2.4.6. |
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1016 |
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1017 **Note on prior.N** |
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1018 |
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1019 http://seqanswers.com/forums/showthread.php?t=5591 says: |
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1020 |
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1021 *prior.n* |
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1022 |
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1023 The value for prior.n determines the amount of smoothing of tagwise dispersions towards the common dispersion. |
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1024 You can think of it as like a "weight" for the common value. (It is actually the weight for the common likelihood |
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1025 in the weighted likelihood equation). The larger the value for prior.n, the more smoothing, i.e. the closer your |
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1026 tagwise dispersion estimates will be to the common dispersion. If you use a prior.n of 1, then that gives the |
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1027 common likelihood the weight of one observation. |
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1028 |
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1029 In answer to your question, it is a good thing to squeeze the tagwise dispersions towards a common value, |
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1030 or else you will be using very unreliable estimates of the dispersion. I would not recommend using the value that |
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1031 you obtained from estimateSmoothing()---this is far too small and would result in virtually no moderation |
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1032 (squeezing) of the tagwise dispersions. How many samples do you have in your experiment? |
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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. |
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1034 If you have more samples, then the tagwise dispersion estimates will be more reliable, |
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1035 so you could consider using a smaller prior.n, although I would hesitate to use a prior.n less than 5. |
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1036 |
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1037 |
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1038 From Bioconductor Digest, Vol 118, Issue 5, Gordon writes: |
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1039 |
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1040 Dear Dorota, |
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1041 |
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1042 The important settings are prior.df and trend. |
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1043 |
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1044 prior.n and prior.df are related through prior.df = prior.n * residual.df, |
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1045 and your experiment has residual.df = 36 - 12 = 24. So the old setting of |
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1046 prior.n=10 is equivalent for your data to prior.df = 240, a very large |
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1047 value. Going the other way, the new setting of prior.df=10 is equivalent |
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1048 to prior.n=10/24. |
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1049 |
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1050 To recover old results with the current software you would use |
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1051 |
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1052 estimateTagwiseDisp(object, prior.df=240, trend="none") |
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1053 |
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1054 To get the new default from old software you would use |
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1055 |
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1056 estimateTagwiseDisp(object, prior.n=10/24, trend=TRUE) |
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1057 |
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1058 Actually the old trend method is equivalent to trend="loess" in the new |
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1059 software. You should use plotBCV(object) to see whether a trend is |
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1060 required. |
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1061 |
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1062 Note you could also use |
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1063 |
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1064 prior.n = getPriorN(object, prior.df=10) |
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1065 |
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1066 to map between prior.df and prior.n. |
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1067 |
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1068 ---- |
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1069 |
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1070 **Attributions** |
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1071 |
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1072 edgeR - edgeR_ |
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1073 |
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1074 VOOM/limma - limma_VOOM_ |
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1075 |
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1076 DESeq2 - DESeq2_ for details |
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1077 |
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1078 See above for Bioconductor package documentation for packages exposed in Galaxy by this tool and app store package. |
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1079 |
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1080 Galaxy_ (that's what you are using right now!) for gluing everything together |
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1081 |
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1082 Otherwise, all code and documentation comprising this tool was written by Ross Lazarus and is |
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1083 licensed to you under the LGPL_ like other rgenetics artefacts |
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1084 |
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1085 .. _LGPL: http://www.gnu.org/copyleft/lesser.html |
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1086 .. _HTSeq: http://www-huber.embl.de/users/anders/HTSeq/doc/index.html |
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1087 .. _edgeR: http://www.bioconductor.org/packages/release/bioc/html/edgeR.html |
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1088 .. _DESeq2: http://www.bioconductor.org/packages/release/bioc/html/DESeq2.html |
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1089 .. _limma_VOOM: http://www.bioconductor.org/packages/release/bioc/html/limma.html |
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1090 .. _Galaxy: http://getgalaxy.org |
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1091 </help> |
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1092 |
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1093 </tool> |
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1094 |
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1095 |