# filter to species: -1: disabled, 0 (human), 1 (drosi), 2 (c.elegans), 3 (yeast)
species_filter = -1
# group of custom sources, currently set to protein-protein interaction databases
custom_source_group = ['MINT', 'IntAct', 'MIPS', 'DroID', 'MatrixDB', 'BioGRID', 'HPRD', 'DIP', 'CCSB']
# all proteins from ANY source DB (not intersection, but union!!)
# SELECT COUNT(DISTINCT id) FROM Protein WHERE id IN ( SELECT proteinId FROM ProteinToDatabase WHERE sourceDb IN ('MINT', 'IntAct', 'MIPS', 'MatrixDB', 'DroID', 'BioGRID', 'HPRD', 'HomoMINT', 'DIP', 'CCSB') );
c = ComppiInterface()
comppi_graph = c.buildGlobalComppi()
# filter to species if needed
if species_filter >= 0:
print("Filtering to species: {}".format(species_filter))
comppi_graph = c.filterGraph(comppi_graph, None, species_filter)
comppi_number_of_all_nodes = comppi_graph.number_of_nodes()
print("Number of all nodes: {}".format(comppi_number_of_all_nodes))
print()
# collect the proteins per source databases and localizations per source databases
loc_sources = {
'No Loc Source': []
}
prot_sources = {
'No Protein Source': []
}
node_db_mapping = c.getNodeSourceDbs()
for n, d in comppi_graph.nodes_iter(data=True):
'int_score'
])
# interactions
for n1, n2, e in egograph.edges_iter(data=True):
# note: same cells as in header
csvw.writerow([
n1,
egograph.node[n1]['name'],
n2,
egograph.node[n2]['name'],
e['weight']
])
print("Filtering the egograph...")
filtered_egograph = c.filterGraph(egograph, args.loc, 0) # graph, loc, species
print("Filtered Egograph: {} nodes, {} edges".format(
filtered_egograph.number_of_nodes(),
filtered_egograph.number_of_edges()
))
with open("egograph-filtered-n_{}-r_{}-l_{}.csv".format(args.node_id, args.radius, args.loc), "w") as fp:
csvw = csv.writer(fp, delimiter="\t", quoting=csv.QUOTE_MINIMAL)
# header
csvw.writerow([
'node_a_id',
'node_a_name',
'node_b_id',
'node_b_name',
'int_score'
]
# all proteins from ANY source DB (not intersection, but union!!)
# SELECT COUNT(DISTINCT id) FROM Protein WHERE id IN ( SELECT proteinId FROM ProteinToDatabase WHERE sourceDb IN ('MINT', 'IntAct', 'MIPS', 'MatrixDB', 'DroID', 'BioGRID', 'HPRD', 'HomoMINT', 'DIP', 'CCSB') );
c = ComppiInterface()
for spfilter in species_filter:
comppi_graph = c.buildGlobalComppi()
with open("comppi_data_overlap_log_sp_{}.txt".format(spfilter),
"w") as logfp:
# filter to species if needed
if spfilter >= 0:
print("Filtering to species: {}".format(spfilter), file=logfp)
comppi_graph = c.filterGraph(comppi_graph, None, spfilter)
comppi_number_of_all_nodes = comppi_graph.number_of_nodes()
print("Number of all nodes: {}".format(comppi_number_of_all_nodes),
file=logfp)
print("\n", file=logfp)
# collect the proteins per source databases and localizations per source databases
loc_sources = {'No Loc Source': []}
prot_sources = {'No Protein Source': []}
node_db_mapping = c.getNodeSourceDbs()
for n, d in comppi_graph.nodes_iter(data=True):
# nodes
curr_node_dbs = node_db_mapping.get(n)
if curr_node_dbs is None:
# group of custom sources, currently set to protein-protein interaction databases
custom_source_group = ['MINT', 'IntAct', 'MIPS', 'DroID', 'MatrixDB', 'BioGRID', 'HPRD', 'DIP', 'CCSB']
# all proteins from ANY source DB (not intersection, but union!!)
# SELECT COUNT(DISTINCT id) FROM Protein WHERE id IN ( SELECT proteinId FROM ProteinToDatabase WHERE sourceDb IN ('MINT', 'IntAct', 'MIPS', 'MatrixDB', 'DroID', 'BioGRID', 'HPRD', 'HomoMINT', 'DIP', 'CCSB') );
c = ComppiInterface()
for spfilter in species_filter:
comppi_graph = c.buildGlobalComppi()
with open("comppi_data_overlap_log_sp_{}.txt".format(spfilter), "w") as logfp:
# filter to species if needed
if spfilter >= 0:
print("Filtering to species: {}".format(spfilter), file=logfp)
comppi_graph = c.filterGraph(comppi_graph, None, spfilter)
comppi_number_of_all_nodes = comppi_graph.number_of_nodes()
print("Number of all nodes: {}".format(comppi_number_of_all_nodes), file=logfp)
print("\n", file=logfp)
# collect the proteins per source databases and localizations per source databases
loc_sources = {
'No Loc Source': []
}
prot_sources = {
'No Protein Source': []
}
node_db_mapping = c.getNodeSourceDbs()
for n, d in comppi_graph.nodes_iter(data=True):
