diff rank_pathways.xml @ 28:184d14e4270d

Update to Miller Lab devshed revision 4ede22dd5500

--- a/rank_pathways.xml	Mon Jul 15 10:47:35 2013 -0400
+++ b/rank_pathways.xml	Wed Jul 17 12:46:46 2013 -0400
@@ -62,11 +62,19 @@
 
 **Dataset formats**
 
-All of the input and output datasets are in tabular_ format.
-The input dataset must have columns with KEGG gene ID and pathways.
-[Need to update this, since input columns now depend on the "Rank by" choice.]
-The output datasets are described below.
-(`Dataset missing?`_)
+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 GO terms, 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
@@ -75,37 +83,36 @@
 
 **What it does**
 
-This tool produces a table ranking the pathways based on the percentage
-of genes in an input dataset, out of the total in each pathway
-[please clarify w.r.t. query and background datasets].
-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.
+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:
+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. KEGG pathway
+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)
+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)
+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
 
@@ -113,27 +120,42 @@
 
 **Examples**
 
-- input (column 10 for KEGG gene ID, column 12 for KEGG pathways)::
- 
+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.
- 
-- output ranked by percentage of genes affected [need new sample output with more columns]::
+
+- 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.
 
-   3   0.25   1   cfa03450=Non-homologous end-joining
-   1   0.25   1   cfa00750=Vitamin B6 metabolism
-   2   0.2    3   cfa00290=Valine, leucine and isoleucine biosynthesis
-   3   0.18   4   cfa00770=Pantothenate and CoA biosynthesis
+- 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.
 
-- output ranked by change in length and number of paths::
+Rank by change in length and number of paths:
 
-    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
+- 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>