Mercurial > repos > mvdbeek > r_goseq_1_22_0

annotate goseq.xml @ 10:f7f3f7db2d4a draft default tip

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author mvdbeek
date Thu, 31 Mar 2016 12:30:01 -0400
parents 04b9c519d3e1
children
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1 <tool id="goseq" name="goseq" version="0.2.2">
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2 <description>tests for overrepresented gene categories</description>
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3 <macros>
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4 <import>go_macros.xml</import>
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5 </macros>
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6 <expand macro="requirements" />
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7 <expand macro="stdio" />
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8 <command interpreter="Rscript">
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9 goseq.r --dge_file "$dge_file"
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10 --length_file "$length_file"
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11 --category_file "$category_file"
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12 #if "$methods['wallenius']":
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13 --wallenius_tab "$wallenius_tab"
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14 #end if
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15 #if "$methods['hypergeometric']":
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16 --nobias_tab "$nobias_tab"
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17 #end if
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18 --repcnt "$methods.repcnt"
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19 --sampling_tab "$sampling_tab"
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20 --p_adj_method "$p_adj_method"
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21 --use_genes_without_cat "$use_genes_without_cat"
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22 --make_plots "$make_plots"
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23 --length_bias_plot "$length_bias_plot"
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24 --sample_vs_wallenius_plot "$sample_vs_wallenius_plot"
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25 </command>
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26 <inputs>
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27 <param help="A tabular file with gene names in the first column, and TRUE or FALSE in the last column. TRUE means a gene is differentially expressed. See help section for details." label="Differentially expressed gene file" name="dge_file" type="data" format="tabular" />
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28 <param label="Gene length file for length bias correction" help="You can calculate the gene length using the get length and gc content tool" name="length_file" type="data" format="tabular" required="true" />
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29 <param label="Gene category file" help="You can obtain a mapping of gene id to gene ontology using the getgo tool" name="category_file" type="data" format="tabular" required="true" />
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30 <param help="For example, a large number of gene may have no GO term annotated. If this option is set to FALSE, those genes will be ignored in the calculation of p-values. If this option is set to TRUE, then these genes will count towards the total number of genes outside the category being tested"
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31 name="use_genes_without_cat" label="Count genes without any category?" type="boolean"/>
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32 <section name="methods" title="Method options" expanded="True">
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33 <param name="wallenius" type="boolean" checked="true" label="Use wallenius method" help="See help for details" />
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34 <param name="hypergeometric" type="boolean" checked="false" label="Use hypergeometric method" help="Does not use gene length information. See help for details" />
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35 <param help="Draw this many random control gene sets. Set to 0 to not do sampling. Larger values take a long time" label="sampling depth" name="repcnt" size="3" type="integer" min="0" max="10000" value="0" />
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36 </section>
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37 <param name="p_adj_method" type="select" label="Select a method for multiple hypothesis testing correction">
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38 <option value="BH" selected="true">Benjamini-Hochberg [FDR] (1995)</option>
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39 <option value="holm">Holm (1979)</option>
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40 <option value="hommel">Hommel (1988)</option>
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41 <option value="hochberg">Hochberg (1988)</option>
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42 <option value="bonferroni">Bonferroni</option>
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43 <option value="BY">Benjamini - Yekutieli (2001)</option>
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44 </param>
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45 <param help="These plots may help you compare the different p-value estimation methods that goseq can use." label="Produce diagnostic plots?" name="make_plots" type="boolean"></param>
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46 </inputs>
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47 <outputs>
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48 <data format="pdf" label="length bias plot" name="length_bias_plot">
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49 <filter>make_plots</filter>
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50 <filter>methods['hypergeometric']</filter>
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51 </data>
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52 <data format="pdf" label="Plot P-value from sampling against wallenius distribution" name="sample_vs_wallenius_plot">
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53 <filter>methods['repcnt'] != 0</filter>
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54 <filter>methods['wallenius']</filter>
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55 <filter>make_plots</filter>
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56 </data>
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57 <data format="tabular" label="Ranked category list - no length bias correction" name="nobias_tab">
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58 <filter>methods['hypergeometric']</filter>
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59 </data>
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60 <data format="tabular" label="Ranked category list - sampling" name="sampling_tab">
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61 <filter>methods['repcnt'] != 0</filter>
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62 </data>
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63 <data format="tabular" label="Ranked category list - wallenius method" name="wallenius_tab">
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64 <filter>methods['wallenius']</filter>
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65 </data>
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66 </outputs>
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67 <tests>
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68 <test>
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69 <param name="dge_file" value="dge_list.tab" ftype="tabular"/>
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70 <param name="length_file" value="gene_length.tab" ftype="tabular"/>
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71 <param name="category_file" value="category.tab" ftype="tabular"/>
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72 <param name="use_genes_without_cat" value="true" />
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73 <output name="wallenius_tab" file="wal.tab" compare="re_match"/>/>
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74 </test>
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75 </tests>
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76 <help>
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77
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78 **What it does**
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79
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80 Detects Gene Ontology and/or other user defined categories which are over/under-represented in RNA-seq data.
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81
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82 Options map closely to the excellent manual_
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83
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84
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85 **Input files**
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86
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87 *DGE list:*
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88 goseq needs a tabular file with genes in the first column, and TRUE or FALSE in the last column.
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89 TRUE means the gene should count as differentially expressed, FALSE means it is not differentially expressed.
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90 You can use the "Compute an expression on every row" tool to create a TRUE / FALSE column for your dataset.
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91
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92 *Gene length file:*
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93 goseq needs information about the length of a gene to correct for potential length bias in differentially expressed genes
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94 using a prodbability weight function (PWF).
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95 The format of this file is tabular, with gene_id in the first column and length in the second column.
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96 The "get length and gc content" tool can produce such a file.
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97
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98 *Gene category file:*
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99 You will also need a file describing the membership of genes in categories. The format of this file is gene_id in the first column,
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100 category name in the second column. If you are interested in gene ontology categories you can use the getgo file to retrive
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101 gene ontologies for model organisms, or you can construct your own file.
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102
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103 **Method options**
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104
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105 3 methods, "Wallenius", "Sampling" and "Hypergeometric", can be used to calculate the p-values as follows.
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106
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107 *"Wallenius"* approximates the true distribution of numbers of members of a category amongst DE genes by the Wallenius non-central hypergeometric distribution.
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108 This distribution assumes that within a category all genes have the same probability of being chosen.
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109 Therefore, this approximation works best when the range in probabilities obtained by the probability weighting function is small.
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110
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111 *"Sampling"* uses random sampling to approximate the true distribution and uses it to calculate the p-values for over (and under) representation of categories.
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112 Although this is the most accurate method given a high enough value of sampling depth, its use quickly becomes computationally prohibitive.
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113
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114 *"Hypergeometric"* assumes there is no bias in power to detect differential expression at all and calculates the p-values using a standard hypergeometric distribution.
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115 Useful if you wish to test the effect of selection bias on your results.
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116
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117 CAUTION: "Hypergeometric" should NEVER be used for producing results for biological interpretation.
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118 If there is genuinely no bias in power to detect DE in your experiment, the PWF will reflect this and the other methods will produce accuracte results.
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119
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120 .. _manual: https://bioconductor.org/packages/release/bioc/vignettes/goseq/inst/doc/goseq.pdf
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121
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122
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123 </help>
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124 <citations>
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125 <citation type="doi">10.1186/gb-2010-11-2-r14</citation>
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126 </citations>
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127 </tool>