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annotate rank_pathways.xml @ 25:cba0d7a63b82

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workaround for gd_genotype datatype admix shift int -> float
author Richard Burhans <burhans@bx.psu.edu>
date Wed, 29 May 2013 13:49:19 -0400
parents 95a05c1ef5d5
children 8997f2ca8c7a
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1 <tool id="gd_calc_freq" name="Rank Pathways" version="1.1.0">
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2 <description>: Assess the impact of a gene set on KEGG pathways</description>
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3
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4 <command interpreter="python">
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5 #if str($output_format) == 'a'
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6 rank_pathways_pct.py
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7 #else if str($output_format) == 'b'
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8 calclenchange.py
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9 #end if
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10 "--loc_file=${GALAXY_DATA_INDEX_DIR}/gd.rank.loc"
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11 "--species=${input.metadata.dbkey}"
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12 "--input=${input}"
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13 "--output=${output}"
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14 "--posKEGGclmn=${kpath}"
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15 "--KEGGgeneposcolmn=${kgene}"
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16 </command>
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17
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18 <inputs>
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19 <param name="input" type="data" format="tab" label="Dataset" />
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20 <param name="kgene" type="data_column" data_ref="input" label="Column with KEGG gene ID" />
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21 <param name="kpath" type="data_column" data_ref="input" numerical="false" label="Column with KEGG pathways" />
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22 <param name="output_format" type="select" label="Output">
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23 <option value="a" selected="true">ranked by percentage of genes affected</option>
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24 <option value="b">ranked by change in length and number of paths</option>
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25 </param>
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26 </inputs>
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27
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28 <outputs>
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29 <data name="output" format="tabular" />
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30 </outputs>
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31
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32 <tests>
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33 <test>
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34 <param name="input" value="test_in/sample.gd_sap" ftype="gd_sap" />
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35 <param name="kgene" value="10" />
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36 <param name="kpath" value="12" />
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37 <param name="output_format" value="a" />
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38 <output name="output" file="test_out/rank_pathways/rank_pathways.tabular" />
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39 </test>
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40 </tests>
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41
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42 <help>
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43
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44 **Dataset formats**
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45
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46 The input and output datasets are in tabular_ format.
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47 The input dataset must have columns with KEGG gene ID and pathways.
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48 The output dataset is described below.
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49 (`Dataset missing?`_)
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50
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51 .. _tabular: ./static/formatHelp.html#tab
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52 .. _Dataset missing?: ./static/formatHelp.html
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53
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54 -----
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55
14
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56 **What it does**
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57
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58 This tool produces a table ranking the pathways based on the percentage
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59 of genes in an input dataset, out of the total in each pathway.
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60 Alternatively, the tool ranks the pathways based on the change in
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61 length and number of paths connecting sources and sinks. This change is
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62 calculated between graphs representing pathways with and without excluding
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63 the nodes that represent the genes in an input list. Sources are all
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64 the nodes representing the initial reactants/products in the pathway.
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65 Sinks are all the nodes representing the final reactants/products in
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66 the pathway.
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67
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68 If pathways are ranked by percentage of genes affected, the output is
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69 a tabular dataset with the following columns:
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70
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71 1. number of genes in the pathway present in the input dataset
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72 2. percentage of the total genes in the pathway included in the input dataset
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73 3. rank of the frequency (from high freq to low freq)
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74 4. Fisher probability of enrichment/depletion of pathway genes in the input dataset
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75 5. name of the pathway
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76
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77 If pathways are ranked by change in length and number of paths, the
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78 output is a tabular dataset with the following columns:
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79
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80 1. change in the mean length of paths between sources and sinks
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81 2. mean length of paths between sources and sinks in the pathway including the genes in the input dataset. If the pathway do not have sources/sinks, the length is assumed to be infinite (I)
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82 3. mean length of paths between sources and sinks in the pathway excluding the genes in the input dataset. If the pathway do not have sources/sinks, the length is assumed to be infinite (I)
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83 4. rank of the change in the mean length of paths between sources and sinks (from high change to low change)
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84 5. change in the number of paths between sources and sinks
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85 6. number of paths between sources and sinks in the pathway including the genes in the input dataset. If the pathway do not have sources/sinks, it is assumed to be a circuit (C)
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86 7. number of paths between sources and sinks in the pathway excluding the genes in the input dataset. If the pathway do not have sources/sinks, it is assumed to be a circuit (C)
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87 8. rank of the change in the number of paths between sources and sinks (from high change to low change)
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88 9. name of the pathway
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89
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90 -----
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91
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92 **Examples**
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93
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94 - input (column 10 for KEGG gene ID, column 12 for KEGG pathways)::
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95
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96 Contig39_chr1_3261104_3261850 414 chr1 3261546 ENSCAFT00000000001 ENSCAFP00000000001 S 667 F 476153 probably damaging cfa00230=Purine metabolism.cfa00500=Starch and sucrose metabolism.cfa00740=Riboflavin metabolism.cfa00760=Nicotinate and nicotinamide metabolism.cfa00770=Pantothenate and CoA biosynthesis.cfa01100=Metabolic pathways
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97 Contig62_chr1_19011969_19012646 265 chr1 19012240 ENSCAFT00000000144 ENSCAFP00000000125 * 161 R 483960 probably damaging N
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98 etc.
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99
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100 - output ranked by percentage of genes affected::
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101
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102 3 0.25 1 cfa03450=Non-homologous end-joining
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103 1 0.25 1 cfa00750=Vitamin B6 metabolism
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104 2 0.2 3 cfa00290=Valine, leucine and isoleucine biosynthesis
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105 3 0.18 4 cfa00770=Pantothenate and CoA biosynthesis
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106 etc.
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107
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108 - output ranked by change in length and number of paths::
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109
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110 3.64 8.44 4.8 2 4 9 5 1 cfa00260=Glycine, serine and threonine metabolism
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111 7.6 9.6 2 1 3 5 2 2 cfa00240=Pyrimidine metabolism
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112 0.05 2.67 2.62 6 1 30 29 3 cfa00982=Drug metabolism - cytochrome P450
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113 -0.08 8.33 8.41 84 1 30 29 3 cfa00564=Glycerophospholipid metabolism
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114 etc.
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115
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116 </help>
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117 </tool>