sep = net.separate()
# lists of all receptors/druggable proteins/kinases/tfs/disease related proteins
console(':: Loading lists of all receptors/druggable proteins/kinases/'\
'tfs/disease related proteins')
net.lists['rec'] = uniqList(flatList([net.mapper.map_name(rec, 'genesymbol', 'uniprot') \
for rec in dataio.get_hpmr()]))
net.lists['dgb'] = uniqList(flatList([net.mapper.map_name(dgb, 'genesymbol', 'uniprot') \
for dgb in dataio.get_dgidb()]))
net.lists['kin'] = uniqList(flatList([net.mapper.map_name(kin, 'genesymbol', 'uniprot') \
for kin in dataio.get_kinases()]))
net.lists['tfs'] = uniqList(flatList([net.mapper.map_name(tf, 'ensg', 'uniprot') \
for tf in dataio.get_tfcensus()['ensg']]))
net.lists['dis'] = uniqList(flatList([\
net.mapper.map_name(dis['genesymbol'], 'genesymbol', 'uniprot') \
for dis in dataio.get_disgenet()]))
# defining the proteome as the set of all human swissprot ids
console(':: Loading the human proteome')
proteome = dataio.all_uniprots(swissprot='yes')
fi = open(fisherFile, 'w')
# Fisher's exact test for enrichment of disease related proteins
# in OmniPath compared to their ratio in the whole proteome
console(':: Fisher\'s exact test for enrichment of disease related proteins in the network'\
'compared to their abundance in the proteome')
contDisg = np.array([[len(proteome), net.graph.vcount()],
def test_get_tfcensus(self):
t = dataio.get_tfcensus()
assert 'FOXD4L6' in t['hgnc']