def reinfer_dpm():
import tp_dpm
factors, factor_indices, targets, target_indices, dpm_input, dpm, summariser, ensembl_names = tp_dpm.create_and_infer_dpm(
)
convergence_test, history = tp_dpm.infer_dpm(dpm,
summariser,
min_iters=options.min_iters,
max_iters=options.max_iters)
tp_dpm.output_summary(summariser)
tp_dpm.plot_programs_info(summariser)
return factors, factor_indices, targets, target_indices, dpm_input, dpm, summariser, ensembl_names
def reinfer_dpm():
import tp_dpm
factors, factor_indices, targets, target_indices, dpm_input, dpm, summariser, ensembl_names = tp_dpm.create_and_infer_dpm()
convergence_test, history = tp_dpm.infer_dpm(dpm, summariser, min_iters=options.min_iters, max_iters=options.max_iters)
tp_dpm.output_summary(summariser)
tp_dpm.plot_programs_info(summariser)
return factors, factor_indices, targets, target_indices, dpm_input, dpm, summariser, ensembl_names
def threshold_tps():
factor_universe, factor_indices, target_universe, target_indices, dpm_input, dpm, summariser, ensembl_names = tp_dpm.create_and_infer_dpm()
transcriptional_programs = [
basic_tp.tp_from_dpm_summary(summariser, factor_universe, target_universe, k)
for k in xrange(summariser.statistics.num_topics_used)
]
for tp in transcriptional_programs:
tp.write_files(ensembl_names)
return transcriptional_programs, factor_universe, target_universe
