import networkx as nx
import sys
import scipy.stats
import os
from utils import yeast_name_resolver
groups = {
'wt1' : ['WT1'],
'wt2' : ['WT2'],
'wt3' : ['WT3'],
'hu' : ['HU80', 'HU120', 'HU160'],
'rap' : ['RAP60', 'RAP140', 'RAP220', 'RAP300', 'RAP380', 'RAP460', 'RAP540', 'RAP620', 'RAP700']
}
res = yeast_name_resolver.NameResolver()
def main(gpath):
G = nx.read_gpickle(gpath)
nodes = sorted(G.nodes())
node_ix = dict(zip(nodes, np.arange(len(nodes))))
abd_df = read_chong('../data-sources/yeast/chong2015/mmc2.xls')
num_features = len(abd_df.columns) - 1
F = np.zeros((len(nodes), num_features))
common_nodes = set(nodes) & set(abd_df['gene'])
print("Common nodes between yeast net and chong dataset: %d" % len(common_nodes))
import numpy as np
import networkx as nx
import os
import sys
from pygosemsim import graph, download, annotation, term_set
import pandas as pd
import feature_preprocessing.pairwise_go_semsim
import utils.yeast_name_resolver as nr
myers2006path = "../data-sources/myers2006.csv"
res = nr.NameResolver()
gpath = "../generated-data/ppc_yeast"
def main():
#download.obo("go-basic")
#download.gaf("sgd")
G = nx.read_gpickle(gpath)
nodes = sorted(G.nodes())
node_ix = dict(zip(nodes, range(len(nodes))))
G = graph.from_resource("go-basic")
df = pd.read_csv(myers2006path, sep="\t")
df['namespace'] = [G.nodes[n]['namespace'] if n in G else None for n in df['GO ID']]
print(df)
ix = (df['namespace'] == 'biological_process') & (df['# of S. cerevisiae annotations (direct and indirect)'] > 3)
df = df[ix]
def main(organism, output):
reader = read_mitab_file
if organism == 'yeast':
genes_to_remove = ['yar062w yar062w', 'yir044c yir044c']
resolver = yeast_name_resolver.NameResolver()
admissible_genes = resolver.get_genes()
gc_only = True
extractor = lambda s: yeast_extract_locus_tag(s, resolver,
admissible_genes)
taxid = 559292
elif organism == 'pombe':
genes_to_remove = []
gene_names = '../data-sources/pombe/PomBase2UniProt.csv'
gene_names_df = pd.read_csv(gene_names,
sep='\t',
header=None,
names=['locus', 'common'])
admissible_genes = set(
[str(e).lower() for e in gene_names_df['locus']])
gc_only = False
extractor = lambda s: pombe_extract_locus_tag(s, admissible_genes)
taxid = 284812
elif organism == 'human':
genes_to_remove = []
gene_names = '../data-sources/human/gene_names'
gene_names_df = pd.read_csv(gene_names, sep='\t')
admissible_genes = set(
[str(e).lower() for e in gene_names_df['Approved symbol']])
gc_only = False
extractor = lambda s: pombe_extract_locus_tag(s, admissible_genes)
taxid = 9606
elif organism == "dro":
genes_to_remove = []
with open('../tmp/dro_gene_map.json', 'r') as f:
en_fbgn = json.load(f)
gc_only = False
extractor = lambda s: dro_extract_locus_tag(s, en_fbgn)
admissible_genes = set([e.lower() for e in en_fbgn.values()])
taxid = 7227
reader = lambda file_path, copres_G, extractor, taxid: read_mitab_file(
file_path, copres_G, extractor, taxid, '#ID Interactor A',
'ID Interactor B')
copresp_G = nx.Graph()
copresp_G.add_nodes_from(admissible_genes)
for file_path in coprespfiles:
print("Processing %s" % file_path)
reader(file_path, copresp_G, extractor, taxid)
copresp_G.remove_nodes_from(genes_to_remove)
components = sorted(nx.connected_components(copresp_G),
key=len,
reverse=True)
print("Full graph:")
print(nx.info(copresp_G))
# write network to disk
if gc_only:
copresp_G = copresp_G.subgraph(components[0])
print(nx.info(copresp_G))
# write network to disk
nx.write_gpickle(copresp_G, output)
nx.write_gml(copresp_G, output + '.gml')
