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annotate rank_pathways.xml @ 28:184d14e4270d

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author Richard Burhans <burhans@bx.psu.edu>
date Wed, 17 Jul 2013 12:46:46 -0400
parents 8997f2ca8c7a
children a631c2f6d913
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1 <tool id="gd_calc_freq" name="Rank Pathways" version="1.2.0">
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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 $rank_by.choice == 'pct'
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6 rank_pathways_pct.py
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7 --input '$rank_by.input1'
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8 --columnENSEMBLT '$rank_by.t_col1'
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9 --inBckgrndfile '$rank_by.input2'
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10 --columnENSEMBLTBckgrnd '$rank_by.t_col2'
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11 --columnKEGGBckgrnd '$rank_by.k_col2'
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12 --statsTest '$rank_by.stat'
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13 --output '$output'
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14 #else if $rank_by.choice == 'paths'
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15 calclenchange.py
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16 '--loc_file=${GALAXY_DATA_INDEX_DIR}/gd.rank.loc'
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17 '--species=${rank_by.input.metadata.dbkey}'
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18 '--input=${rank_by.input}'
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19 '--output=${output}'
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20 '--posKEGGclmn=${rank_by.kpath}'
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21 '--KEGGgeneposcolmn=${rank_by.kgene}'
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22 #end if
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23 </command>
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25 <inputs>
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26 <conditional name="rank_by">
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27 <param name="choice" type="select" label="Rank by">
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28 <option value="pct" selected="true">percentage of genes affected</option>
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29 <option value="paths">change in length and number of paths</option>
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30 </param>
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31 <when value="pct">
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32 <!-- using fields similar to the Rank Terms tool -->
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33 <param name="input1" type="data" format="tabular" label="Query dataset" />
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34 <param name="t_col1" type="data_column" data_ref="input1" label="Column with ENSEMBL transcript codes" />
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35 <param name="input2" type="data" format="tabular" label="Background dataset" />
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36 <param name="t_col2" type="data_column" data_ref="input2" label="Column with ENSEMBL transcript codes" />
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37 <param name="k_col2" type="data_column" data_ref="input2" label="Column with KEGG pathways" />
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38 <param name="stat" type="select" label="Statistic for determining enrichment/depletion">
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39 <option value="fisher" selected="true">two-tailed Fisher's exact test</option>
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40 <option value="hypergeometric">hypergeometric test</option>
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41 <option value="binomial">binomial probability</option>
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42 </param>
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43 </when>
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44 <when value="paths">
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45 <param name="input" type="data" format="tabular" label="Dataset" />
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46 <param name="kgene" type="data_column" data_ref="input" label="Column with KEGG gene ID" />
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47 <param name="kpath" type="data_column" data_ref="input" numerical="false" label="Column with KEGG pathways" />
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48 </when>
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49 </conditional>
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50 </inputs>
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51
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52 <outputs>
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53 <data name="output" format="tabular" />
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54 </outputs>
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55
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56 <tests>
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57 <test>
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58 </test>
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59 </tests>
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60
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61 <help>
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62
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63 **Dataset formats**
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64
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65 The query dataset has a column containing ENSEMBL transcript codes for
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66 the gene set of interest, while the background dataset has one column
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67 with ENSEMBL transcript codes and another with GO terms, for some larger
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68 universe of genes.
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69
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70 All of the input and output datasets are in tabular_ format. The input
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71 dataset (i.e. query) to rank by "percentage of genes affected" has a
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72 column containing ENSEMBL transcript codes for the gene set of interest,
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73 while the background dataset has one column with ENSEMBL transcript
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74 codes and another with KEGG pathways, for some larger universe of genes.
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75 The input dataset to rank by "change in length and number of paths"
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76 must have columns with KEGG gene ID and pathways. The output datasets
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77 are described below. (`Dataset missing?`_)
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78
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79 .. _tabular: ./static/formatHelp.html#tab
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80 .. _Dataset missing?: ./static/formatHelp.html
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81
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82 -----
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83
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84 **What it does**
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85
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86 Given a query set of genes from a larger background dataset, this tool
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87 evaluates the over- or under-representation of KEGG pathways in the query
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88 set, using the specified statistical test. Alternatively, the tool ranks
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89 the pathways based on the change in length and number of paths connecting
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90 sources and sinks. This change is calculated between graphs representing
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91 pathways with and without excluding the nodes that represent the genes
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92 in an input list. Sources are all the nodes representing the initial
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93 reactants/products in the pathway. Sinks are all the nodes representing
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94 the final reactants/products in the pathway.
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95
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96 If pathways are ranked by percentage of genes affected, the output
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97 contains a row for each KEGG pathway, with the following columns:
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98
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99 1. count: the number of genes in the query set that are in this pathway
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100 2. representation: the percentage of this pathway's genes (from the background dataset) that appear in the query set
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101 3. ranking of this pathway, based on its representation ("1" is highest)
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102 4. probability of depletion of this pathway in the query dataset
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103 5. probability of enrichment of this pathway in the query dataset
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104 6. name of the pathway
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105
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106 If pathways are ranked by change in length and number of paths, the
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107 output is a tabular dataset with the following columns:
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108
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109 1. change in the mean length of paths between sources and sinks
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110 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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111 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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112 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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113 5. change in the number of paths between sources and sinks
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114 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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115 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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116 8. rank of the change in the number of paths between sources and sinks (from high change to low change)
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117 9. name of the pathway
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118
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119 -----
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120
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121 **Examples**
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122
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123 Rank by percentage of genes affected:
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124
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125 - input background dataset (column 5 for ENSEMBL transcript, column 12 for KEGG pathways, two-tailed Fisher's exact test for statistic)::
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126
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127 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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128 Contig62_chr1_19011969_19012646 265 chr1 19012240 ENSCAFT00000000144 ENSCAFP00000000125 * 161 R 483960 probably damaging N
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129 etc.
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130
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131 - input query dataset (column 5 for ENSEMBL transcript)::
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132
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133 Contig12_chr20_101969_112646 265 chr20 9822141 ENSCAFT00000001234 ENSCAFP00000021123 T 101 R 476153 probably damaging
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134 Contig39_chr1_3261104_3261850 414 chr1 3261546 ENSCAFT00000000001 ENSCAFP00000000001 S 667 F 476153 probably damaging
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135 etc.
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136
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137 - output::
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138
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139 3 0.20 1 1.0 0.0065 cfa03450=Non-homologous end-joining
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140 1 0.067 2 1.0 0.019 cfa00750=Vitamin B6 metabolism
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141 2 0.062 3 1.0 0.021 cfa00290=Valine, leucine and isoleucine biosynthesis
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142 1 0.037 4 1.0 0.035 cfa00770=Pantothenate and CoA biosynthesis
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143 etc.
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144
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145 Rank by change in length and number of paths:
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146
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147 - input (column 10 for KEGG gene ID, column 12 for KEGG pathways)::
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148
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149 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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150 Contig62_chr1_19011969_19012646 265 chr1 19012240 ENSCAFT00000000144 ENSCAFP00000000125 * 161 R 483960 probably damaging N
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151 etc.
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152
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153 - output::
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154
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155 3.64 8.44 4.8 2 4 9 5 1 cfa00260=Glycine, serine and threonine metabolism
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156 7.6 9.6 2 1 3 5 2 2 cfa00240=Pyrimidine metabolism
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157 0.05 2.67 2.62 6 1 30 29 3 cfa00982=Drug metabolism - cytochrome P450
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158 -0.08 8.33 8.41 84 1 30 29 3 cfa00564=Glycerophospholipid metabolism
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159 etc.
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160 </help>
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161 </tool>