def _get_pval_uncorr(self, study, log=sys.stdout):
"""Calculate the uncorrected pvalues for study items."""
log.write(
"Calculating uncorrected p-values using Fisher's exact test\n")
results = []
go2studyitems = get_terms(study, self.assoc, self.obo_dag)
pop_n, study_n = self.pop_n, len(study)
allterms = set(go2studyitems.keys() + self.go2popitems.keys())
for term in allterms:
study_items = go2studyitems.get(term, set())
study_count = len(study_items)
pop_items = self.go2popitems.get(term, set())
pop_count = len(pop_items)
p = fisher.pvalue_population(study_count, study_n, pop_count,
pop_n)
one_record = GOEnrichmentRecord(GO=term,
p_uncorrected=p.two_tail,
study_items=study_items,
pop_items=pop_items,
ratio_in_study=(study_count,
study_n),
ratio_in_pop=(pop_count, pop_n))
results.append(one_record)
return results
def calc_qval(study_n, pop_n, pop, assoc, term_pop, obo_dag, T=500):
"""Generate p-value distribution for FDR based on resampling."""
import fisher
from goatools.ratio import count_terms
print(("Generate p-value distribution for FDR "
"based on resampling (this might take a while)"),
file=sys.stderr)
distribution = []
for i in range(T):
new_study = random.sample(pop, study_n)
new_term_study = count_terms(new_study, assoc, obo_dag)
smallest_p = 1
for term, study_count in list(new_term_study.items()):
pop_count = term_pop[term]
p = fisher.pvalue_population(study_count, study_n, pop_count,
pop_n)
if p.two_tail < smallest_p:
smallest_p = p.two_tail
distribution.append(smallest_p)
if i % 10 == 0:
print("Sample {0} / {1}: p-value {2}".\
format(i, T, smallest_p), file=sys.stderr)
return distribution
def calc_qval(study_n, pop_n,
pop, assoc, term_pop, obo_dag, T=500):
"""Generate p-value distribution for FDR based on resampling."""
import fisher
from goatools.ratio import count_terms
print(("Generate p-value distribution for FDR "
"based on resampling (this might take a while)"), file=sys.stderr)
distribution = []
for i in range(T):
new_study = random.sample(pop, study_n)
new_term_study = count_terms(new_study, assoc, obo_dag)
smallest_p = 1
for term, study_count in list(new_term_study.items()):
pop_count = term_pop[term]
p = fisher.pvalue_population(study_count,
study_n,
pop_count,
pop_n)
if p.two_tail < smallest_p:
smallest_p = p.two_tail
distribution.append(smallest_p)
if i % 10 == 0:
print("Sample {0} / {1}: p-value {2}".\
format(i, T, smallest_p), file=sys.stderr)
return distribution
def _get_pval_uncorr(self, study, log=sys.stdout):
"""Calculate the uncorrected pvalues for study items."""
log.write("Calculating uncorrected p-values using Fisher's exact test\n")
results = []
go2studyitems = get_terms(study, self.assoc, self.obo_dag)
pop_n, study_n = self.pop_n, len(study)
allterms = set(go2studyitems.keys() + self.go2popitems.keys())
for term in allterms:
study_items = go2studyitems.get(term, set())
study_count = len(study_items)
pop_items = self.go2popitems.get(term, set())
pop_count = len(pop_items)
p = fisher.pvalue_population(study_count, study_n,
pop_count, pop_n)
one_record = GOEnrichmentRecord(
GO=term,
p_uncorrected=p.two_tail,
study_items=study_items,
pop_items=pop_items,
ratio_in_study=(study_count, study_n),
ratio_in_pop=(pop_count, pop_n))
results.append(one_record)
return results
def calc_qval(study_count,
study_n,
pop_count,
pop_n,
pop,
assoc,
term_pop,
obo_dag,
T=500):
# print(("Generate p-value distribution for FDR "
# "based on resampling (this might take a while)"), file=sys.stderr)
# Print statements are not so nice for notebooks.
distribution = []
for i in range(T):
new_study = random.sample(pop, study_n)
new_term_study = count_terms(new_study, assoc, obo_dag)
smallest_p = 1
for term, study_count in list(new_term_study.items()):
pop_count = term_pop[term]
p = fisher.pvalue_population(study_count, study_n, pop_count,
pop_n)
if p.two_tail < smallest_p:
smallest_p = p.two_tail
distribution.append(smallest_p)
# if i % 10 == 0:
# print("Sample {0} / {1}: p-value {2}".\
# format(i, T, smallest_p), file=sys.stderr)
# Not so nice to have so many print lines on a notebook
return distribution
def rank(targets, pathways, allmembers, outname):
P = len(allmembers)
realtargets = targets.intersection(allmembers)
Pn = len(realtargets)
Pr = float(Pn) / P
# score it all
out = open(outname, 'w')
for uid, name, members in pathways:
pmembers = members.intersection(realtargets)
pmember_names = [converter.handler.to_symbol(uid) for uid in pmembers]
if pmembers == None or pmember_names == None:
continue
if None in pmember_names:
pmember_names.remove(None)
if None in pmembers:
pmembers.remove(None)
C = len(members)
Cn = len(pmembers)
score = fisher.pvalue_population(Cn, C, Pn, P).two_tail
log_score = math.log(score, 10)
if (float(Cn) / C) < Pr:
log_score = -log_score
#print((uid,C,Cn,log_score, name, ','.join(pmembers), ','.join(pmember_names)))
out.write("%s\t%s\t%s\t%s\t%s\t%s\t%s\n" %
(uid, C, Cn, log_score, name, ','.join(pmembers),
','.join(pmember_names)))
out.close()
def run_study(self, study):
results = self.results
term_study = count_terms(study, self.assoc, self.obo_dag)
pop_n, study_n = len(self.pop), len(study)
# Init study_count and pop_count to handle empty sets
study_count = pop_count = 0
for term, study_count in list(term_study.items()):
pop_count = self.term_pop[term]
p = fisher.pvalue_population(study_count, study_n,
pop_count, pop_n)
one_record = GOEnrichmentRecord(
id=term,
p_uncorrected=p.two_tail,
ratio_in_study=(study_count, study_n),
ratio_in_pop=(pop_count, pop_n))
results.append(one_record)
# Calculate multiple corrections
pvals = [r.p_uncorrected for r in results]
all_methods = ("bonferroni", "sidak", "holm", "fdr")
bonferroni, sidak, holm, fdr = None, None, None, None
for method in self.methods:
if method == "bonferroni":
bonferroni = Bonferroni(pvals, self.alpha).corrected_pvals
elif method == "sidak":
sidak = Sidak(pvals, self.alpha).corrected_pvals
elif method == "holm":
holm = HolmBonferroni(pvals, self.alpha).corrected_pvals
elif method == "fdr":
# get the empirical p-value distributions for FDR
p_val_distribution = calc_qval(study_count, study_n,
pop_count, pop_n,
self.pop, self.assoc,
self.term_pop, self.obo_dag)
fdr = FDR(p_val_distribution,
results, self.alpha).corrected_pvals
else:
raise Exception("multiple test correction methods must be "
"one of %s" % all_methods)
all_corrections = (bonferroni, sidak, holm, fdr)
for method, corrected_pvals in zip(all_methods, all_corrections):
self.update_results(method, corrected_pvals)
results.sort(key=lambda r: r.p_uncorrected)
self.results = results
for rec in results:
# get go term for description and level
rec.find_goterm(self.obo_dag)
return results
def run_study(self, study):
results = self.results
term_study = count_terms(study, self.assoc, self.obo_dag)
pop_n, study_n = len(self.pop), len(study)
# Init study_count and pop_count to handle empty sets
study_count = pop_count = 0
for term, study_count in list(term_study.items()):
pop_count = self.term_pop[term]
p = fisher.pvalue_population(study_count, study_n, pop_count,
pop_n)
one_record = GOEnrichmentRecord(id=term,
p_uncorrected=p.two_tail,
ratio_in_study=(study_count,
study_n),
ratio_in_pop=(pop_count, pop_n))
results.append(one_record)
# Calculate multiple corrections
pvals = [r.p_uncorrected for r in results]
all_methods = ("bonferroni", "sidak", "holm", "fdr")
bonferroni, sidak, holm, fdr = None, None, None, None
for method in self.methods:
if method == "bonferroni":
bonferroni = Bonferroni(pvals, self.alpha).corrected_pvals
elif method == "sidak":
sidak = Sidak(pvals, self.alpha).corrected_pvals
elif method == "holm":
holm = HolmBonferroni(pvals, self.alpha).corrected_pvals
elif method == "fdr":
# get the empirical p-value distributions for FDR
p_val_distribution = calc_qval(study_count, study_n, pop_count,
pop_n, self.pop, self.assoc,
self.term_pop, self.obo_dag)
fdr = FDR(p_val_distribution, results,
self.alpha).corrected_pvals
else:
raise Exception("multiple test correction methods must be "
"one of %s" % all_methods)
all_corrections = (bonferroni, sidak, holm, fdr)
for method, corrected_pvals in zip(all_methods, all_corrections):
self.update_results(method, corrected_pvals)
results.sort(key=lambda r: r.p_uncorrected)
self.results = results
for rec in results:
# get go term for description and level
rec.find_goterm(self.obo_dag)
return results
def calc_qval(study_count, study_n, pop_count, pop_n, pop, assoc, term_pop, obo_dag):
print >>sys.stderr, ("generating p-value distribution for FDR " "calculation (this might take a while)")
T = 1000 # number of samples
distribution = []
for i in xrange(T):
new_study = random.sample(pop, study_n)
new_term_study = go_enrichment.count_terms(new_study, assoc, obo_dag)
smallest_p = 1
for term, study_count in new_term_study.items():
pop_count = term_pop[term]
p = fisher.pvalue_population(study_count, study_n, pop_count, pop_n)
if p.two_tail < smallest_p:
smallest_p = p.two_tail
distribution.append(smallest_p)
print >>sys.stderr, i, smallest_p
return distribution
def calc_qval(study_count, study_n, pop_count, pop_n, pop, assoc, term_pop, obo_dag):
print >>sys.stderr, "generating p-value distribution for FDR calculation " \
"(this might take a while)"
T = 1000 # number of samples
distribution = []
for i in xrange(T):
new_study = random.sample(pop, study_n)
new_term_study = go_enrichment.count_terms(new_study, assoc, obo_dag)
smallest_p = 1
for term, study_count in new_term_study.items():
pop_count = term_pop[term]
p = fisher.pvalue_population(study_count, study_n, pop_count, pop_n)
if p.two_tail < smallest_p: smallest_p = p.two_tail
distribution.append(smallest_p)
print >>sys.stderr, i, smallest_p
return distribution
def _get_pval_uncorr(self, study):
"""Calculate the uncorrected pvalues for study items."""
results = []
term_study = count_terms(study, self.assoc, self.obo_dag)
pop_n, study_n = self.pop_n, len(study)
allterms = set(term_study.keys() + self.term_pop.keys())
for term in allterms:
study_count = term_study.get(term, 0)
pop_count = self.term_pop.get(term, 0)
p = fisher.pvalue_population(study_count, study_n,
pop_count, pop_n)
one_record = GOEnrichmentRecord(
id=term,
p_uncorrected=p.two_tail,
ratio_in_study=(study_count, study_n),
ratio_in_pop=(pop_count, pop_n))
results.append(one_record)
return results
def __init__(self, set_count, pop_count, set_size, pop_size, data):
for k, v in locals().iteritems():
setattr(self, k, v)
self.pvalue = fisher.pvalue_population(set_count, set_size, pop_count,
pop_size).right_tail
