goenrich 0.4.1

GO enrichment with python -- pandas meets networkx

goenrich

Convenient GO enrichments from python. For use in python projects.

1. Builds the GO-ontology graph

2. Propagates GO-annotations up the graph

3. Performs enrichment test for all categories

4. Performs multiple testing correction

5. Allows for export to pandas for processing and graphviz for visualization

Supported ids: Uniport ACC, Entrez GeneID

Installation

Install package from pypi and download ontology

and needed annotations.

pip install goenrich
mkdir db
# Ontology
wget http://purl.obolibrary.org/obo/go/go-basic.obo -O db/go-basic.obo
# UniprotACC
wget http://geneontology.org/gene-associations/gene_association.goa_ref_human.gz -O db/gene_association.goa_ref_human.gz
# Entrez GeneID
wget ftp://ftp.ncbi.nlm.nih.gov/gene/DATA/gene2go.gz -O db/gene2go.gz

Run GO enrichment

import goenrich

# build the ontology
G = goenrich.obo.graph('db/go-basic.obo')

# use all associations as background
entry_id, category_id, background = goenrich.read.goa('db/gene_association.goa_ref_human.gz')
# use goenrich.read.gene2go('db/gene2go.gz') for entrez geneid
# extract some list of entries as query
query = set(background[entry_id].unique()[:20])

# set background
goenrich.enrich.set_background(G, background)

# run enrichment analysis, correct for multiple testing
pvalues = goenrich.enrich.calculate_pvalues(G, query, entry_id, category_id)
goenrich.enrich.multiple_testing_correction(G, pvalues)

# export to pandas or graphviz
df = goenrich.export.to_frame(G)
sig = goenrich.enrich.filter_significant(G)
R = goenrich.export.to_graphviz(G, sig, 'test.dot')

Generate png image using graphviz

dot -Tpng test.dot > test.png

Parameters

Some parameters to consider for analysis

enrich.calculate_pvalues:
  min_category_size = 2

enrich.multiple_testing_correction:
  alpha = 0.05
  method = ['benjamin-hochberg', 'bonferroni']

Licence

This work is licenced under the MIT licence

Contributions are welcome!