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date Fri, 27 Dec 2013 05:28:46 -0500
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1 <tool id="edgeR" name="edgeR" version="0.0.2">
2 <description> - Estimates differential gene expression for short read sequence count using methods appropriate for count data</description>
3 <requirements>
4 <requirement type="R-module">edgeR</requirement>
5 <requirement type="R-module">limma</requirement>
6 </requirements>
7 <command interpreter="perl">
8 edgeR.pl -a $analysis_type.analysis -e $html_file.files_path -f $fdr -h $html_file -o $output
9 ## Pairwise comparisons
10 #if $analysis_type.analysis == "pw":
11 -r $analysis_type.rowsumfilter
12 #if $analysis_type.tagwise_disp.twd == "TRUE":
13 -u $analysis_type.tagwise_disp.twd_trend
14 -t
15 #end if
16 ## GLM
17 #else if $analysis_type.analysis == "glm":
18 #if $analysis_type.exp.export_norm == "true":
19 -n $norm_exp
20 #end if
21 -d $analysis_type.disp
22 $analysis_type.cont_pw
23 #for $fct in $analysis_type.factors:
24 factor::${$fct.fact_name}::${$fct.fact}
25 #end for
26 #for $c in $analysis_type.cont_pred:
27 cp::${c.cp_name}::${c.cp}
28 #end for
29 #for $cnt in $analysis_type.contrasts:
30 "cnt::${cnt.add_cont}"
31 #end for
32 ## LIMMA
33 #else
34 #if $analysis_type.exp.export_norm == "true":
35 -n $norm_exp $analysis_type.exp.log
36 #end if
37 $analysis_type.cont_pw
38 #for $fct in $analysis_type.factors:
39 factor::${$fct.fact_name}::${$fct.fact}
40 #end for
41 #for $c in $analysis_type.cont_pred:
42 cp::${c.cp_name}::${c.cp}
43 #end for
44 #for $cnt in $analysis_type.contrasts:
45 "cnt::${cnt.add_cont}"
46 #end for
47 #end if
48 $matrix
49
50 </command>
51
52 <inputs>
53 <param name="matrix" type="data" format="tabular" label="Digital Expression Matrix"/>
54 <conditional name="analysis_type">
55 <param name="analysis" type="select" label="Type Of Analysis">
56 <option value="pw">Pairwise comparisons (1 Factor Analysis)</option>
57 <option value="glm" selected="true">Generalized Linear Models (Multiple Factor Analysis using GLM)</option>
58 <option value="limma">Linear Models for RNA-Seq (Multiple Factor Analysis using LIMMA)</option>
59 </param>
60 <when value="pw">
61 <param name="rowsumfilter" type="integer" value="5" label="Common Dispersion Rowsum Filter" help="Numeric scalar giving a value for the filtering out of low abundance tags in the estimation of the common dispersion. Only tags with total sum of counts above this value are used in the estimation of the common dispersion. Low abundance tags can adversely affect the estimation of the common dispersion, so this argument allows the user to select an appropriate filter threshold for the tag abundance."/>
62 <conditional name="tagwise_disp">
63 <param name="twd" type="select" label="Maximize the Negative Binomial Weighted Conditional Likelihood" help="Calculate and use an estimate of the dispersion parameter for each tag">
64 <option value="TRUE" selected="true">True</option>
65 <option value="FALSE">False</option>
66 </param>
67 <when value="TRUE">
68 <param name="twd_trend" type="select" label="Method for allowing the prior distribution for the dispersion to be abundance-dependent">
69 <option value="movingave" selected="true">Movingave</option>
70 <option value="tricube">Tricube</option>
71 <option value="none">None</option>
72 </param>
73 </when>
74 </conditional>
75 </when>
76 <when value="glm">
77 <param name="disp" type="select" label="Select The Dispersion Estimate To Use:">
78 <option value="common">Common Dispersion</option>
79 <option value="trend">Trended Dispersion</option>
80 <option value="tag" selected="true">Tagwise Dispersion</option>
81 </param>
82 <repeat name="factors" title="Factor">
83 <param name="fact_name" title="Factor Name" type="text" label="Name Of Factor (no spaces or commas)"/>
84 <param name="fact" title="Factor" type="text" size="100" label="The Level Of Each Sample Seperated By A Colon (no spaces or commas)"/>
85 </repeat>
86 <repeat name="cont_pred" title="Continuous Predictor">
87 <param name="cp_name" title="Continuous Predictor Name" type="text" label="Name Of Continuous Predictor (no spaces or commas)"/>
88 <param name="cp" title="Continuous Predictor" type="text" size="100" label="The Numerical Value For Each Sample Seperated By A Colon (no spaces or commas)"/>
89 </repeat>
90 <param name="cont_pw" type="boolean" truevalue="-m" falsevalue="" checked="True" label="Perform all pairwise comparisons" help="Include all pairwise comparisons in the contrast matrix."/>
91 <repeat name="contrasts" title="Contrast">
92 <param name="add_cont" title="Contrast" type="text" label="Enter the contrast of interest, e.g. (G1+G2)/2-G3 (no spaces or commas)"/>
93 </repeat>
94 <conditional name="exp">
95 <param name="export_norm" type="select" label="Save Normalised DGE Matrix">
96 <option value="true">Yes</option>
97 <option value="false">No</option>
98 </param>
99 </conditional>
100 </when>
101 <when value="limma">
102 <repeat name="factors" title="Factor">
103 <param name="fact_name" title="Factor Name" type="text" label="Name Of Factor (no spaces or commas)"/>
104 <param name="fact" title="Factor" type="text" size="100" label="The Level Of Each Sample Seperated By A Colon (no spaces or commas)"/>
105 </repeat>
106 <repeat name="cont_pred" title="Continuous Predictor">
107 <param name="cp_name" title="Continuous Predictor Name" type="text" label="Name Of Continuous Predictor (no spaces or commas)"/>
108 <param name="cp" title="Continuous Predictor" type="text" size="100" label="The Numerical Value For Each Sample Seperated By A Colon (no spaces or commas)"/>
109 </repeat>
110 <param name="cont_pw" type="boolean" truevalue="-m" falsevalue="" checked="True" label="Perform all pairwise comparisons" help="Include all pairwise comparisons in the contrast matrix."/>
111 <repeat name="contrasts" title="Contrast">
112 <param name="add_cont" title="Contrast" type="text" label="Enter the contrast of interest, e.g. (G1+G2)/2-G3 (no spaces or commas)"/>
113 </repeat>
114 <conditional name="exp">
115 <param name="export_norm" type="select" label="Save Normalised DGE Matrix">
116 <option value="true">Yes</option>
117 <option value="false">No</option>
118 </param>
119 <when value="true">
120 <param name="log" type="boolean" truevalue="-l" falsevalue="" checked="True" label="Export Normalised DGE Matrix in Log2" help="Selecting this will log base 2 transform the Normalised Digital Gene Expression Matrix."/>
121 </when>
122 </conditional>
123 </when>
124 </conditional>
125 <param name="fdr" type="select" label="False discovery rate adjustment method">
126 <option value="BH">Benjamini and Hochberg (1995)</option>
127 <option value="holm">Holm (1979)</option>
128 <option value="hochberg">Hochberg (1988)</option>
129 <option value="hommel">Hommel (1988)</option>
130 <option value="BY">Benjamini and Yekutieli (2001)</option>
131 <option value="none">None</option>
132 </param>
133 </inputs>
134
135 <outputs>
136 <data format="tabular" name="output" label="EdgeR analysis on ${matrix.name}"/>
137 <data name="html_file" format="html" label="EdgeR analysis plots for ${matrix.name}"/>
138 <data name="norm_exp" format="tabular" label="EdgeR Norm Expr Matrix for ${matrix.name}">
139 <filter>analysis_type[ "analysis" ] != "pw" and analysis_type[ "exp" ][ "export_norm" ] == "true"</filter>
140 </data>
141 </outputs>
142
143 <help>
144
145 .. class:: infomark
146
147 **What it does**
148
149 Estimates differential gene expression for short read sequence count using methods appropriate for count data.
150 If you have paired data you may also want to consider Tophat/Cufflinks.
151 Input must be raw count data for each sequence arranged in a rectangular matrix as a tabular file.
152 Note - no scaling - please make sure you have untransformed raw counts of reads for each sequence.
153
154 Performs digital differential gene expression analysis between groups (eg a treatment and control).
155 Biological replicates provide information about experimental variability required for reliable inference.
156
157 **What it does not do**
158 edgeR_ requires biological replicates.
159 Without replicates you can't account for known important experimental sources of variability that the approach implemented here requires.
160
161
162 **Input**
163 A count matrix containing sequence names as rows and sample specific counts of reads from this sequence as columns.
164 The matrix must have 2 header rows, the first indicating the group assignment and the second uniquely identifiying the samples. It must also contain a unique set of (eg Feature) names in the first column.
165
166 Example::
167
168 # G1:Mut G1:Mut G1:Mut G2:WT G2:WT G2:WT
169 #Feature Spl1 Spl2 Spl3 Spl4 Spl5 Spl6
170 NM_001001130 97 43 61 34 73 26
171 NM_001001144 25 8 9 3 5 5
172 NM_001001152 72 45 29 20 31 13
173 NM_001001160 0 1 1 1 0 0
174 NM_001001177 0 1 0 4 3 3
175 NM_001001178 0 2 1 0 4 0
176 NM_001001179 0 0 0 0 0 2
177 NM_001001180 0 0 0 0 0 2
178 NM_001001181 415 319 462 185 391 155
179 NM_001001182 1293 945 987 297 938 496
180 NM_001001183 5 4 11 7 11 2
181 NM_001001184 135 198 178 110 205 64
182 NM_001001185 186 1 0 1 1 0
183 NM_001001186 75 90 91 34 63 54
184 NM_001001187 267 236 170 165 202 51
185 NM_001001295 5 2 6 1 7 0
186 NM_001001309 1 0 0 1 2 1
187 ...
188
189
190 Please use the "Count reads in features with htseq-count" tool to generate the count matrix.
191
192 **Output**
193
194 A tabular file containing relative expression levels, statistical estimates of differential expression probability, R scripts, log, and some helpful diagnostic plots.
195
196 .. class:: infomark
197
198 **Attribution**
199 This tool wraps the edgeR_ Bioconductor package so all calculations and plots are controlled by that code. See edgeR_ for all documentation and appropriate attribution.
200 Recommended reference is Mark D. Robinson, Davis J. McCarthy, Gordon K. Smyth, PMCID: PMC2796818
201
202 .. class:: infomark
203
204 **Attribution**
205 When applying the LIMMA (Linear models for RNA-Seq) anlysis the tool also makes use of the limma_ Bioconductor package.
206 Recommended reference is Smyth, G. K. (2005). Limma: linear models for microarray data. In: 'Bioinformatics and Computational Biology Solutions using R and Bioconductor'. R. Gentleman, V. Carey, S. Dudoit, R. Irizarry, W. Huber (eds), Springer, New York, pages 397--420.
207
208 .. _edgeR: http://www.bioconductor.org/packages/release/bioc/html/edgeR.html
209 .. _limma: http://www.bioconductor.org/packages/release/bioc/html/limma.html
210
211
212 </help>
213
214 </tool>