def symatlas():
logging.info('Analysing SymAtlas expression data.')
probes_to_genes = SA.probes_to_genes()
dataset, tissues, probes, fold_changes = SA.expression_data()
highly_expressed = SA.fold_change_above_median(fold_changes, fold_change=options.symatlas_fold_change_threshold)
highly_expressed_probes = SA.match_probe_sets(highly_expressed.T, probes)
highly_expressed_genes = [
set(probes_to_genes[p] for p in hep if p in probes_to_genes and probes_to_genes[p])
for hep in highly_expressed_probes
]
if False:
import pylab as P
P.figure()
P.bar(range(len(tissues)), map(len, highly_expressed_probes))
P.xlim(max=len(tissues))
transcriptional_programs, factor_universe, target_universe = tp_threshold.threshold_tps()
tester = gene_set_enrichment.TpEnrichmentTester(factor_universe, target_universe, p_value_threshold=1e-3)
for tissue, tissue_genes in zip(tissues, highly_expressed_genes):
logging.debug('Testing %d transcriptional programs\' factors for enrichment in genes over-expressed in tissue %s', len(transcriptional_programs), tissue)
for tp, (test_drawn, test_size, test_complement_size, draws, p_value) in tester.test_transcriptional_program_factors(transcriptional_programs, tissue_genes):
logging.info(
'TP:%4d; %4d in program; %4d/%5d over-expressed in % -32s; %4d in intersection; p-value=%e',
tp.k, draws, test_size, test_complement_size+test_size, tissue, test_drawn, p_value
)
for tp, (test_drawn, test_size, test_complement_size, draws, p_value) in tester.test_transcriptional_program_targets(transcriptional_programs, tissue_genes):
logging.info(
'TP:%4d; %4d in program; %4d/%5d over-expressed in % -32s; %4d in intersection; p-value=%g',
tp.k, draws, test_size, test_complement_size+test_size, tissue, test_drawn, p_value
)
def get_symatlas_sets():
logging.info('Getting SymAtlas validation sets.')
import biopsy.data.symatlas as SA
probes_to_genes = SA.probes_to_genes()
dataset, tissues, probes, fold_changes = SA.expression_data()
highly_expressed = SA.fold_change_above_median(fold_changes, fold_change=options.symatlas_fold_change_threshold)
highly_expressed_probes = SA.match_probe_sets(highly_expressed.T, probes)
highly_expressed_genes = [
set(probes_to_genes[p] for p in hep if p in probes_to_genes and probes_to_genes[p])
for hep in highly_expressed_probes
]
return dict(zip(('symatlas-%s' % t for t in tissues), highly_expressed_genes))
def get_symatlas_sets():
logging.info('Getting SymAtlas validation sets.')
import biopsy.data.symatlas as SA
probes_to_genes = SA.probes_to_genes()
dataset, tissues, probes, fold_changes = SA.expression_data()
highly_expressed = SA.fold_change_above_median(
fold_changes, fold_change=options.symatlas_fold_change_threshold)
highly_expressed_probes = SA.match_probe_sets(highly_expressed.T, probes)
highly_expressed_genes = [
set(probes_to_genes[p] for p in hep
if p in probes_to_genes and probes_to_genes[p])
for hep in highly_expressed_probes
]
return dict(
zip(('symatlas-%s' % t for t in tissues), highly_expressed_genes))