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comparison rank_pathways.xml @ 27:8997f2ca8c7a

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Update to Miller Lab devshed revision bae0d3306d3b
author Richard Burhans <burhans@bx.psu.edu>
date Mon, 15 Jul 2013 10:47:35 -0400
parents 95a05c1ef5d5
children 184d14e4270d
comparison
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26:91e835060ad2 27:8997f2ca8c7a
1 <tool id="gd_calc_freq" name="Rank Pathways" version="1.1.0"> 1 <tool id="gd_calc_freq" name="Rank Pathways" version="1.2.0">
2 <description>: Assess the impact of a gene set on KEGG pathways</description> 2 <description>: Assess the impact of a gene set on KEGG pathways</description>
3 3
4 <command interpreter="python"> 4 <command interpreter="python">
5 #if str($output_format) == 'a' 5 #if $rank_by.choice == 'pct'
6 rank_pathways_pct.py 6 rank_pathways_pct.py
7 #else if str($output_format) == 'b' 7 --input '$rank_by.input1'
8 --columnENSEMBLT '$rank_by.t_col1'
9 --inBckgrndfile '$rank_by.input2'
10 --columnENSEMBLTBckgrnd '$rank_by.t_col2'
11 --columnKEGGBckgrnd '$rank_by.k_col2'
12 --statsTest '$rank_by.stat'
13 --output '$output'
14 #else if $rank_by.choice == 'paths'
8 calclenchange.py 15 calclenchange.py
16 '--loc_file=${GALAXY_DATA_INDEX_DIR}/gd.rank.loc'
17 '--species=${rank_by.input.metadata.dbkey}'
18 '--input=${rank_by.input}'
19 '--output=${output}'
20 '--posKEGGclmn=${rank_by.kpath}'
21 '--KEGGgeneposcolmn=${rank_by.kgene}'
9 #end if 22 #end if
10 "--loc_file=${GALAXY_DATA_INDEX_DIR}/gd.rank.loc"
11 "--species=${input.metadata.dbkey}"
12 "--input=${input}"
13 "--output=${output}"
14 "--posKEGGclmn=${kpath}"
15 "--KEGGgeneposcolmn=${kgene}"
16 </command> 23 </command>
17 24
18 <inputs> 25 <inputs>
19 <param name="input" type="data" format="tab" label="Dataset" /> 26 <conditional name="rank_by">
20 <param name="kgene" type="data_column" data_ref="input" label="Column with KEGG gene ID" /> 27 <param name="choice" type="select" label="Rank by">
21 <param name="kpath" type="data_column" data_ref="input" numerical="false" label="Column with KEGG pathways" /> 28 <option value="pct" selected="true">percentage of genes affected</option>
22 <param name="output_format" type="select" label="Output"> 29 <option value="paths">change in length and number of paths</option>
23 <option value="a" selected="true">ranked by percentage of genes affected</option> 30 </param>
24 <option value="b">ranked by change in length and number of paths</option> 31 <when value="pct">
25 </param> 32 <!-- using fields similar to the Rank Terms tool -->
33 <param name="input1" type="data" format="tabular" label="Query dataset" />
34 <param name="t_col1" type="data_column" data_ref="input1" label="Column with ENSEMBL transcript codes" />
35 <param name="input2" type="data" format="tabular" label="Background dataset" />
36 <param name="t_col2" type="data_column" data_ref="input2" label="Column with ENSEMBL transcript codes" />
37 <param name="k_col2" type="data_column" data_ref="input2" label="Column with KEGG pathways" />
38 <param name="stat" type="select" label="Statistic for determining enrichment/depletion">
39 <option value="fisher" selected="true">two-tailed Fisher's exact test</option>
40 <option value="hypergeometric">hypergeometric test</option>
41 <option value="binomial">binomial probability</option>
42 </param>
43 </when>
44 <when value="paths">
45 <param name="input" type="data" format="tabular" label="Dataset" />
46 <param name="kgene" type="data_column" data_ref="input" label="Column with KEGG gene ID" />
47 <param name="kpath" type="data_column" data_ref="input" numerical="false" label="Column with KEGG pathways" />
48 </when>
49 </conditional>
26 </inputs> 50 </inputs>
27 51
28 <outputs> 52 <outputs>
29 <data name="output" format="tabular" /> 53 <data name="output" format="tabular" />
30 </outputs> 54 </outputs>
31 55
32 <tests> 56 <tests>
33 <test> 57 <test>
34 <param name="input" value="test_in/sample.gd_sap" ftype="gd_sap" />
35 <param name="kgene" value="10" />
36 <param name="kpath" value="12" />
37 <param name="output_format" value="a" />
38 <output name="output" file="test_out/rank_pathways/rank_pathways.tabular" />
39 </test> 58 </test>
40 </tests> 59 </tests>
41 60
42 <help> 61 <help>
43 62
44 **Dataset formats** 63 **Dataset formats**
45 64
46 The input and output datasets are in tabular_ format. 65 All of the input and output datasets are in tabular_ format.
47 The input dataset must have columns with KEGG gene ID and pathways. 66 The input dataset must have columns with KEGG gene ID and pathways.
48 The output dataset is described below. 67 [Need to update this, since input columns now depend on the "Rank by" choice.]
68 The output datasets are described below.
49 (`Dataset missing?`_) 69 (`Dataset missing?`_)
50 70
51 .. _tabular: ./static/formatHelp.html#tab 71 .. _tabular: ./static/formatHelp.html#tab
52 .. _Dataset missing?: ./static/formatHelp.html 72 .. _Dataset missing?: ./static/formatHelp.html
53 73
54 ----- 74 -----
55 75
56 **What it does** 76 **What it does**
57 77
58 This tool produces a table ranking the pathways based on the percentage 78 This tool produces a table ranking the pathways based on the percentage
59 of genes in an input dataset, out of the total in each pathway. 79 of genes in an input dataset, out of the total in each pathway
80 [please clarify w.r.t. query and background datasets].
60 Alternatively, the tool ranks the pathways based on the change in 81 Alternatively, the tool ranks the pathways based on the change in
61 length and number of paths connecting sources and sinks. This change is 82 length and number of paths connecting sources and sinks. This change is
62 calculated between graphs representing pathways with and without excluding 83 calculated between graphs representing pathways with and without excluding
63 the nodes that represent the genes in an input list. Sources are all 84 the nodes that represent the genes in an input list. Sources are all
64 the nodes representing the initial reactants/products in the pathway. 85 the nodes representing the initial reactants/products in the pathway.
65 Sinks are all the nodes representing the final reactants/products in 86 Sinks are all the nodes representing the final reactants/products in
66 the pathway. 87 the pathway.
67 88
68 If pathways are ranked by percentage of genes affected, the output is 89 If pathways are ranked by percentage of genes affected, the output contains
69 a tabular dataset with the following columns: 90 a row for each KEGG pathway, with the following columns:
70 91
71 1. number of genes in the pathway present in the input dataset 92 1. count: the number of genes in the query set that are in this pathway
72 2. percentage of the total genes in the pathway included in the input dataset 93 2. representation: the percentage of this pathway's genes (from the background dataset) that appear in the query set
73 3. rank of the frequency (from high freq to low freq) 94 3. ranking of this pathway, based on its representation ("1" is highest)
74 4. Fisher probability of enrichment/depletion of pathway genes in the input dataset 95 4. probability of depletion of this pathway in the query dataset
75 5. name of the pathway 96 5. probability of enrichment of this pathway in the query dataset
97 6. KEGG pathway
76 98
77 If pathways are ranked by change in length and number of paths, the 99 If pathways are ranked by change in length and number of paths, the
78 output is a tabular dataset with the following columns: 100 output is a tabular dataset with the following columns:
79 101
80 1. change in the mean length of paths between sources and sinks 102 1. change in the mean length of paths between sources and sinks
95 117
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 118 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
97 Contig62_chr1_19011969_19012646 265 chr1 19012240 ENSCAFT00000000144 ENSCAFP00000000125 * 161 R 483960 probably damaging N 119 Contig62_chr1_19011969_19012646 265 chr1 19012240 ENSCAFT00000000144 ENSCAFP00000000125 * 161 R 483960 probably damaging N
98 etc. 120 etc.
99 121
100 - output ranked by percentage of genes affected:: 122 - output ranked by percentage of genes affected [need new sample output with more columns]::
101 123
102 3 0.25 1 cfa03450=Non-homologous end-joining 124 3 0.25 1 cfa03450=Non-homologous end-joining
103 1 0.25 1 cfa00750=Vitamin B6 metabolism 125 1 0.25 1 cfa00750=Vitamin B6 metabolism
104 2 0.2 3 cfa00290=Valine, leucine and isoleucine biosynthesis 126 2 0.2 3 cfa00290=Valine, leucine and isoleucine biosynthesis
105 3 0.18 4 cfa00770=Pantothenate and CoA biosynthesis 127 3 0.18 4 cfa00770=Pantothenate and CoA biosynthesis
106 etc. 128 etc.
107 129
108 - output ranked by change in length and number of paths:: 130 - output ranked by change in length and number of paths::
109 131
110 3.64 8.44 4.8 2 4 9 5 1 cfa00260=Glycine, serine and threonine metabolism 132 3.64 8.44 4.8 2 4 9 5 1 cfa00260=Glycine, serine and threonine metabolism
111 7.6 9.6 2 1 3 5 2 2 cfa00240=Pyrimidine metabolism 133 7.6 9.6 2 1 3 5 2 2 cfa00240=Pyrimidine metabolism
112 0.05 2.67 2.62 6 1 30 29 3 cfa00982=Drug metabolism - cytochrome P450 134 0.05 2.67 2.62 6 1 30 29 3 cfa00982=Drug metabolism - cytochrome P450
113 -0.08 8.33 8.41 84 1 30 29 3 cfa00564=Glycerophospholipid metabolism 135 -0.08 8.33 8.41 84 1 30 29 3 cfa00564=Glycerophospholipid metabolism
114 etc. 136 etc.
115 137
116 </help> 138 </help>
117 </tool> 139 </tool>