goenrich package

Submodules

goenrich.enrich module

goenrich.enrich.analyze(O, query, background_attribute, **kwargs)

run enrichment analysis for query

>>> O = goenrich.obo.ontology('db/go-basic.obo')
>>> gene2go = goenrich.read.gene2go('db/gene2go.gz')
>>> values = {k: set(v) for k,v in gene2go.groupby('GO_ID')['GeneID']}
>>> goenrich.enrich.propagate(O, values, 'gene2go')
>>> df = goenrich.enrich.analyze(O, query, ...)

Parameters:

• O – Ontology graph after backgroud was set
• query – array like of ids

Returns:

pandas.DataFrame with results

goenrich.enrich.calculate_pvalues(nodes, query, background_attribute, M, min_category_size=3, max_category_size=500, max_category_depth=5, **kwargs)

calculate pvalues for all categories in the graph

Parameters:

• nodes – nodes dictionary from the ontology graph after background was set
• query – set of identifiers for which the p value is calculated
• background_attribute – node attribute assoc. with the background set
• M – background size, total number of genes in the data
• min_category_size – categories smaller than this number are ignored
• max_category_size – categories larger than this number are ignored
• max_category_depth – categories lower in the hierarchy (more specific) will be ignored

Returns:

pvalues, x, n

goenrich.enrich.induced_subgraph(O, terms)

Extracts a subgraph from O including the provided terms and all higher hierarchy

>>> df = goenrich.enrich.analyze(O, ...)
>>> G = goenrich.induced_subgraph(O, df[df.rejected]['terms'])

Parameters:

• O – ontology graph
• terms – a list of terms to extract

goenrich.enrich.multiple_testing_correction(ps, alpha=0.05, method='benjamini-hochberg', **kwargs)

correct pvalues for multiple testing and add corrected q value

Parameters:

• ps – list of pvalues
• alpha – significance level default : 0.05
• method – multiple testing correction method [bonferroni|benjamini-hochberg]

Returns (q, rej):  

two lists of q-values and rejected nodes

goenrich.enrich.propagate(O, values, attribute)

Propagate values trough the hierarchy

>>> O = goenrich.obo.ontology('db/go-basic.obo')
>>> gene2go = goenrich.read.gene2go('db/gene2go.gz')
>>> values = {k: set(v) for k,v in gene2go.groupby('GO_ID')['GeneID']}
>>> goenrich.enrich.propagate(O, values, 'gene2go')

Uses topological sorting of the vertices. Since degrees are usually low performance is almost linear time.

Parameters:

• O – ontology graph
• values – mapping of nodes to set of ids
• attribute – name of the attribute

goenrich.export module

goenrich.export.to_frame(nodes, **kwargs)

export node attributes and key-values pairs to pd.DataFrame

>>> to_frame(nodes, term = terms, pvalues = ps, ...)

Parameters:

• nodes – list of dictionaries with node attributes
• **kwargs –

  additional columns

Returns:

a pd.DataFrame

goenrich.export.to_graphviz(G, gvfile, graph_label='', **kwargs)

export graph of signifcant findings to dot file. A png can be generated from the commandline using graphviz

>>> import subprocess
>>> subprocess.call(['dot', '-Tpng', 'filpath.dot', '>', 'filepath.png'])

Parameters:

• G – the graph to be exported
• gvfile – file or filepath
• graph_label – For empty label pass graph_label=’‘.

goenrich.obo module

goenrich.obo.ontology(file)

read ontology from file :param file: file path of file handle

goenrich.read module

parsers for different go-annotation formats

goenrich.read.gene2go(filename, experimental=False, tax_id=9606, **kwds)

read go-annotation file

Parameters:

• filename – protein or gene identifier column
• experimental – use only experimentally validated annotations
• tax_id – filter according to taxon

goenrich.read.goa(filename, experimental=True, **kwds)

read go-annotation file

Parameters:

• filename – protein or gene identifier column
• experimental – use only experimentally validated annotations

goenrich.read.sgd(filename, experimental=False, **kwds)

read yeast genome database go-annotation file

Parameters:

• filename – protein or gene identifier column
• experimental – use only experimentally validated annotations

Module contents