def cge20(metlin, hmdb, metfrag):
cge20 = path_dict['cge00020']
observed = ((metlin | hmdb | metfrag) & features) - cofactors
reverse_path_dict = read.reverse_dict(path_dict)
print observed & cge20
for compound in observed & cge20:
print compound, len(reverse_path_dict[compound])
def gen(self, test_name):
pathways, features, path_dict, evidence, met_evidence = self.tests[
test_name]()
rev_path_dict = read.reverse_dict(path_dict)
evidence = {e: 1 for e in evidence}
if met_evidence is None:
met_evidence = dict()
return pathways, features, path_dict, rev_path_dict, evidence, met_evidence
import math
ONE, ZERO = (0.99, 0.01)
data_path = '../data/'
observation_file = data_path + 'HilNeg 0324 -- Data.csv'
cofactors = read.get_cofactors(data_path + 'cofactors')
path_dict = read.get_model(data_path + 'model2.csv', cofactors=cofactors)
pathways = path_dict.keys()
features = read.get_metabolites(path_dict)
evidence = read.metlin(observation_file)
evidence |= read.hmdb(observation_file)
evidence -= cofactors
features -= cofactors
evidence &= features
reverse_path_dict = read.reverse_dict(path_dict)
metfrag = read.metfrag(observation_file)
metfrag_evidence = read.dict_of_set(
read.metfrag_with_scores(observation_file, keep_zero_scores=False),
metfrag & features - cofactors - evidence)
evidence = {e: 1 for e in evidence}
rate_prior = 0.5
ap = {p: Gamma('p_' + p, rate_prior, 1) for p in pathways}
bmp = {
p: {
feat: Gamma('b_{' + p + ',' + feat + '}', ap[p], 1)
for feat in path_dict[p]
}
for p in pathways
def summarize_compound(compound):
path_dict = read.get_model(data_path + 'model2.csv')
reverse_path_dict = read.reverse_dict(path_dict)
print "Compound", compound, "has pathways:", reverse_path_dict[compound]
def stats(pathway, metlin, hmdb, metfrag=set()):
cge = path_dict[pathway]
observed = ((metlin | hmdb | metfrag) & features) - cofactors
reverse_path_dict = read.reverse_dict(path_dict)
for compound in observed & cge:
print compound, len(reverse_path_dict[compound])
def summarize_compound(compound):
path_dict = read.get_model(data_path + 'model2.csv')
reverse_path_dict = read.reverse_dict(path_dict)
print("Compound", compound, "has pathways:")
for path in sorted(reverse_path_dict[compound]):
print(path)