def validate_tp_set(k, t, tp_set, reference_name, reference_set, universe, latex_f=None, p_values=None):
test_tp_drawn, num_in_ref, num_not_in_ref, num_in_tp, p_value = test_enrichment(tp_set, reference_set, universe)
assert len(tp_set) == num_in_tp
assert len(reference_set) == num_in_ref
if None != p_values:
p_values.append(p_value)
if p_value < get_p_value_threshold_for(reference_name):
logging.info(
"TP %3d; %s; % 4d/%4d in % 5d/%5d; %e; %s",
k,
t,
test_tp_drawn,
num_in_tp,
num_in_ref,
num_in_ref + num_not_in_ref,
p_value,
reference_name,
)
if latex_f:
print >> latex_f, "%3d & %s & %5d & %80s & %5d & %-5d & %.1e" % (
k,
t,
num_in_tp,
reference_name,
test_tp_drawn,
num_in_ref,
p_value,
)
def validate_tp_set(k,
t,
tp_set,
reference_name,
reference_set,
universe,
latex_f=None,
p_values=None):
test_tp_drawn, num_in_ref, num_not_in_ref, num_in_tp, p_value = test_enrichment(
tp_set, reference_set, universe)
assert len(tp_set) == num_in_tp
assert len(reference_set) == num_in_ref
if None != p_values:
p_values.append(p_value)
if p_value < get_p_value_threshold_for(reference_name):
logging.info('TP %3d; %s; % 4d/%4d in % 5d/%5d; %e; %s', k, t,
test_tp_drawn, num_in_tp, num_in_ref,
num_in_ref + num_not_in_ref, p_value, reference_name)
if latex_f:
print >> latex_f, '%3d & %s & %5d & %80s & %5d & %-5d & %.1e' % (
k, t, num_in_tp, reference_name, test_tp_drawn, num_in_ref,
p_value)
def as_set(s):
if set == type(s):
return s
else:
return set(s)
def as_str_set(s):
return as_set(str(t) for t in s)
if '__main__' == __name__:
from gene_set_enrichment import test_enrichment
kegg_pathways = get_kegg_pathways()
for i, tp in enumerate(transcriptional_programs):
threshold = 0.05
target_universe = as_str_set(tp.genes)
factor_universe = as_str_set(tp.factors)
target_set = as_str_set(tp.tp_targets)
factor_set = as_str_set(tp.tp_factors)
for name, pathway in kegg_pathways:
pathway_target_set = as_str_set(pathway)
pathway_target_set.intersection_update(target_universe)
pathway_factor_set = as_str_set(pathway)
pathway_factor_set.intersection_update(factor_universe)
targets_p_value = test_enrichment(target_set, pathway_target_set, target_universe) * len(kegg_pathways)
factors_p_value = test_enrichment(factor_set, pathway_factor_set, factor_universe) * len(kegg_pathways)
if targets_p_value < threshold or factors_p_value < threshold:
print i, name, targets_p_value, factors_p_value
