| Author: | Haibao Tang (tanghaibao), Brent Pedersen (brentp), Aurelien Naldi (aurelien-naldi), Patrick Flick (r4d2), Jeff Yunes (yunesj), Kenta Sato (bicycle1885) |
|---|---|
| Email: | tanghaibao@gmail.com |
| License: | BSD |
Contents
This package contains a Python library to
- process over- and under-representation of certain GO terms, based on Fisher's exact test. Also implemented several multiple correction routines (including Bonferroni, Sidak, and false discovery rate).
- process the obo-formatted file from Gene Ontology website. The data structure is a directed acyclic graph (DAG) that allows easy traversal from leaf to root.
- map GO terms (or protein products with multiple associations to GO terms) to GOslim terms (analog to the map2slim.pl script supplied by geneontology.org)
Make sure your Python version >= 2.6, install it via PyPI:
easy_install goatools
.obofile for the most current gene ontology:wget http://geneontology.org/ontology/obo_format_1_2/gene_ontology.1_2.obo
.obofile for the most current GO Slim terms (.e.g generic GOslim)wget http://www.geneontology.org/GO_slims/goslim_generic.obo
fisher module for calculating Fisher's exact test:
easy_install fisher
If you need to plot the ontology lineage, you need the following tools to be installed.
Graphviz, for graph visualization.
pygraphviz, Python binding for communicating with Graphviz:
easy_install pygraphviz
run.sh contains example cases, which calls the utility scripts in the
scripts folder.
See find_enrichment.py for usage. It takes as arguments files containing:
- gene names in a study
- gene names in population (or other study if --compare is specified)
- an association file that maps a gene name to a GO category.
Please look at tests/data/ folder to see examples on how to make these
files. when ready, the command looks like:
python scripts/find_enrichment.py --pval=0.05 --indent data/study data/population data/association
and can filter on the significance of (e)nrichment or (p)urification. it can report various multiple testing corrected p-values as well as the false discovery rate.
The "e" in the "Enrichment" column means "enriched" - the concentration of GO term in the study group is significantly higher than those in the population. The "p" stands for "purified" - significantly lower concentration of the GO term in the study group than in the population.
See plot_go_term.py for usage. plot_go_term.py can plot the lineage of
a certain GO term, by:
python scripts/plot_go_term.py --term=GO:0008135
This command will plot the following image.
Sometimes people like to stylize the graph themselves, use option --gml to
generate a GML output which can then be used in an external graph editing
software like Cytoscape. The following image is
produced by importing the GML file into Cytoscape using yFile orthogonal
layout and solid VizMapping. Note that the GML reader plugin may need to be
downloaded and installed in the plugins folder of Cytoscape:
python scripts/plot_go_term.py --term=GO:0008135 --gml
See map_to_slim.py for usage. As arguments it takes the gene ontology files:
- the current gene ontology file
gene_ontology.1_2.obo - the GOslim file to be used (e.g.
goslim_generic.oboor any other GOslim file)
The script either maps one GO term to it's GOslim terms, or protein products with multiple associations to all it's GOslim terms.
To determine the GOslim terms for a single GO term, you can use the following command:
python scripts/map_to_slim.py --term=GO:0008135 gene_ontology.1_2.obo goslim_generic.obo
To determine the GOslim terms for protein products with multiple associations:
python scripts/map_to_slim.py --association_file=data/association gene_ontology.1_2.obo goslim_generic.obo
Where the association file has the same format as used for
find_enrichment.py.
The implemented algorithm is described in more detail at the go-perl documenation of map2slim.