Convenient GO enrichments from python. For use in python projects.
- Builds the GO-ontology graph
- Propagates GO-annotations up the graph
- Performs enrichment test for all categories
- Performs multiple testing correction
- Allows for export to
pandasfor processing andgraphvizfor visualization
Supported ids: Uniport ACC, Entrez GeneID
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.gzimport goenrich
# build the ontology
G = goenrich.obo.graph('db/go-basic.obo')
# use all entrez geneid associations form gene2go as background
# use goenrich.read.goa('db/gene_association.goa_ref_human.gz') for uniprot
background = goenrich.read.gene2go('db/gene2go.gz')
goenrich.enrich.set_background(G, background, 'GeneID', 'GO_ID')
# extract some list of entries as example query
query = set(background['GeneID'].unique()[:20])
# run analysis and obtain results
result = goenrich.enrich.analyze(G, query)
# for additional export to graphviz just specify the gvfile argument
# the show argument keeps the graph reasonably small
result = goenrich.enrich.analyze(G, query, gvfile='example.dot', show='top20')The first few rows of the resulting table are:
| name | x | p | q | namespace | |
|---|---|---|---|---|---|
| term | |||||
| GO:0044877 | macromolecular complex binding | 2 | 3.422658e-02 | 0.034227 | molecular_function |
| GO:0000149 | SNARE binding | 2 | 1.041071e-05 | 0.000092 | molecular_function |
| GO:1901700 | response to oxygen-containing compound | 2 | 1.088637e-02 | 0.014640 | biological_process |
| GO:0050801 | ion homeostasis | 2 | 1.653091e-03 | 0.003393 | biological_process |
| GO:0051353 | positive regulation of oxidoreductase activity | 2 | 2.439696e-07 | 0.000010 | biological_process |
Generate png image using graphviz
dot -Tpng example.dot > example.png
Parameters can all be passed to enrich.analyze as shown below
go_options = {
'multiple-testing-correction' : 'bonferroni',
'alpha' : 0.05,
'node_filter' : lambda x : x.get('significant', False)
}
goenrich.enrich.analyze(G, query, **go_options)
# export results to graphviz
goenrich.enrich.analyze(G, query, gvfile='example.dot', **go_options)Here is an overview over the available parmeters
read.*:
experimental = True # don't consider inferred annotations
enrich.analyze:
node_filter = lambda node : 'p' in node
show = 'top20' # works for any 'topNUM'
enrich.calculate_pvalues:
min_hit_size = 2
min_category_size = 3
max_category_size = 500
max_category_depth = 5
enrich.multiple_testing_correction:
alpha = 0.05
method = 'benjamin-hochberg' # also supported : 'bonferroni'
export.to_frame:
node_filter = lambda node: True
export.to_graphviz:
graph_label = None # if None it is replaced by multiple testing info
This work is licenced under the MIT licence
Contributions are welcome!