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1 <tool id="gd_calc_freq" name="Rank" version="1.0.0">
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2 <description>affected KEGG pathways</description>
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3
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4 <command interpreter="python">
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5 #if str($output_format) == 'a'
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6 calctfreq.py
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7 #else if str($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=${input.metadata.kegg_path}"
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15 "--KEGGgeneposcolmn=${input.metadata.kegg_gene}"
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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="gd_sap" label="Table">
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20 <validator type="metadata" check="kegg_gene,kegg_path" message="Missing KEGG gene code column and/or KEGG pathway code/name column metadata. Click the pencil icon in the history item to edit/save the metadata attributes" />
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21 </param>
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22 <param name="output_format" type="select" label="Output format">
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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="output_format" value="a" />
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36 <output name="output" file="test_out/rank_pathways/rank_pathways.tabular" />
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37 </test>
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38 </tests>
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39
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40 <help>
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41 **What it does**
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42
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43 This tool produces a table ranking the pathways based on the percentage
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44 of genes in an input dataset, out of the total in each pathway.
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45 Alternatively, the tool ranks the pathways based on the change in
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46 length and number of paths connecting sources and sinks. This change is
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47 calculated between graphs representing pathways with and without excluding
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48 the nodes that represent the genes in an input list. Sources are all
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49 the nodes representing the initial reactants/products in the pathway.
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50 Sinks are all the nodes representing the final reactants/products in
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51 the pathway.
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52
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53 If pathways are ranked by percentage of genes affected, the output is
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54 a tabular dataset with the following columns:
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55
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56 1. number of genes in the pathway present in the input dataset
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57 2. percentage of the total genes in the pathway included in the input dataset
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58 3. rank of the frequency (from high freq to low freq)
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59 4. name of the pathway
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60
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61 If pathways are ranked by change in length and number of paths, the
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62 output is a tabular dataset with the following columns:
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63
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64 1. change in the mean length of paths between sources and sinks
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65 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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66 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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67 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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68 5. change in the number of paths between sources and sinks
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69 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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70 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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71 8. rank of the change in the number of paths between sources and sinks (from high change to low change)
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72 9. name of the pathway
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73 </help>
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74 </tool>
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