<tool id="gd_calc_freq" name="Rank Pathways" version="1.2.0">
  <description>: Assess the impact of a gene set on KEGG pathways</description>

  <command interpreter="python">
    #if $rank_by.choice == 'pct'
      rank_pathways_pct.py
      --input '$rank_by.input1'
      --columnENSEMBLT '$rank_by.t_col1'
      --inBckgrndfile '$rank_by.input2'
      --columnENSEMBLTBckgrnd '$rank_by.t_col2'
      --columnKEGGBckgrnd '$rank_by.k_col2'
      --statsTest '$rank_by.stat'
      --output '$output'
    #else if $rank_by.choice == 'paths'
      calclenchange.py
      '--loc_file=${GALAXY_DATA_INDEX_DIR}/gd.rank.loc'
      '--species=${rank_by.input.metadata.dbkey}'
      '--input=${rank_by.input}'
      '--output=${output}'
      '--posKEGGclmn=${rank_by.kpath}'
      '--KEGGgeneposcolmn=${rank_by.kgene}'
    #end if
  </command>

  <inputs>
    <conditional name="rank_by">
      <param name="choice" type="select" label="Rank by">
        <option value="pct" selected="true">percentage of genes affected</option>
        <option value="paths">change in length and number of paths</option>
      </param>
      <when value="pct">
        <!-- using fields similar to the Rank Terms tool -->
        <param name="input1" type="data" format="tabular" label="Query dataset" />
        <param name="t_col1" type="data_column" data_ref="input1" label="Column with ENSEMBL transcript codes" />
        <param name="input2" type="data" format="tabular" label="Background dataset" />
        <param name="t_col2" type="data_column" data_ref="input2" label="Column with ENSEMBL transcript codes" />
        <param name="k_col2" type="data_column" data_ref="input2" label="Column with KEGG pathways" />
        <param name="stat" type="select" label="Statistic for determining enrichment/depletion">
          <option value="fisher" selected="true">two-tailed Fisher's exact test</option>
          <option value="hypergeometric">hypergeometric test</option>
          <option value="binomial">binomial probability</option>
        </param>
      </when>
      <when value="paths">
        <param name="input" type="data" format="tabular" label="Dataset" />
        <param name="kgene" type="data_column" data_ref="input" label="Column with KEGG gene ID" />
        <param name="kpath" type="data_column" data_ref="input" numerical="false" label="Column with KEGG pathways" />
      </when>
    </conditional>
  </inputs>

  <outputs>
    <data name="output" format="tabular" />
  </outputs>

  <requirements>
    <requirement type="package" version="0.2.5">mechanize</requirement>
    <requirement type="package" version="1.8.1">networkx</requirement>
    <requirement type="package" version="0.1.4">fisher</requirement>
  </requirements>


  <tests>
    <test>
    </test>
  </tests>

  <help>

**Dataset formats**

The query dataset has a column containing ENSEMBL transcript codes for
the gene set of interest, while the background dataset has one column
with ENSEMBL transcript codes and another with KEGG pathways, for some larger
universe of genes.

All of the input and output datasets are in tabular_ format.  The input
dataset (i.e. query) to rank by "percentage of genes affected" has a
column containing ENSEMBL transcript codes for the gene set of interest,
while the background dataset has one column with ENSEMBL transcript
codes and another with KEGG pathways, for some larger universe of genes.
The input dataset to rank by "change in length and number of paths"
must have columns with KEGG gene ID and pathways.  The output datasets
are described below.  (`Dataset missing?`_)

.. _tabular: ./static/formatHelp.html#tab
.. _Dataset missing?: ./static/formatHelp.html

-----

**What it does**

Given a query set of genes from a larger background dataset, this tool
evaluates the over- or under-representation of KEGG pathways in the query
set, using the specified statistical test.  Alternatively, the tool ranks
the pathways based on the change in length and number of paths connecting
sources and sinks.  This change is calculated between graphs representing
pathways with and without excluding the nodes that represent the genes
in an input list.  Sources are all the nodes representing the initial
reactants/products in the pathway.  Sinks are all the nodes representing
the final reactants/products in the pathway.

If pathways are ranked by percentage of genes affected, the output
contains a row for each KEGG pathway, with the following columns:

1. count: the number of genes in the query set that are in this pathway
2. representation: the percentage of this pathway's genes (from the background dataset) that appear in the query set
3. ranking of this pathway, based on its representation ("1" is highest)
4. probability of depletion of this pathway in the query dataset
5. probability of enrichment of this pathway in the query dataset
6. name of the pathway

If pathways are ranked by change in length and number of paths, the
output is a tabular dataset with the following columns:

1. change in the mean length of paths between sources and sinks
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)
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)
4. rank of the change in the mean length of paths between sources and sinks (from high change to low change)
5. change in the number of paths between sources and sinks
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)
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)
8. rank of the change in the number of paths between sources and sinks (from high change to low change)
9. name of the pathway

-----

**Examples**

Rank by percentage of genes affected:

- input background dataset (column 5 for ENSEMBL transcript, column 12 for KEGG pathways, two-tailed Fisher's exact test for statistic)::

   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
   Contig62_chr1_19011969_19012646 265  chr1  19012240 ENSCAFT00000000144   ENSCAFP00000000125   *    161   R    483960  probably damaging    N
   etc.

- input query dataset (column 5 for ENSEMBL transcript)::

   Contig12_chr20_101969_112646    265  chr20 9822141  ENSCAFT00000001234   ENSCAFP00000021123   T    101   R    476153  probably damaging
   Contig39_chr1_3261104_3261850   414  chr1  3261546  ENSCAFT00000000001   ENSCAFP00000000001   S    667   F    476153  probably damaging
   etc.

- output::

   3   0.20    1   1.0 0.0065  cfa03450=Non-homologous end-joining
   1   0.067   2   1.0 0.019   cfa00750=Vitamin B6 metabolism
   2   0.062   3   1.0 0.021   cfa00290=Valine, leucine and isoleucine biosynthesis
   1   0.037   4   1.0 0.035   cfa00770=Pantothenate and CoA biosynthesis
   etc.

Rank by change in length and number of paths:

- input (column 10 for KEGG gene ID, column 12 for KEGG pathways)::

   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
   Contig62_chr1_19011969_19012646 265  chr1  19012240 ENSCAFT00000000144   ENSCAFP00000000125   *    161   R    483960  probably damaging    N
   etc.

- output::

   3.64   8.44   4.8     2   4    9    5   1   cfa00260=Glycine, serine and threonine metabolism
   7.6    9.6    2       1   3    5    2   2   cfa00240=Pyrimidine metabolism
   0.05   2.67   2.62    6   1   30   29   3   cfa00982=Drug metabolism - cytochrome P450
   -0.08  8.33   8.41   84   1   30   29   3   cfa00564=Glycerophospholipid metabolism
   etc.
  </help>
</tool>