class ProbabilisticModel(object):
def __init__(self, allelenumber_max):
self.allelenumber_max = allelenumber_max
self.priors_parser = PriorParser()
self.data = Data()
self._init_components()
def read_priors(self, priors_filename):
raise NotImplemented
def read_data(self, filename_base):
self.data.read_data(filename_base)
def preprocess(self):
raise NotImplemented
def run(self, idx_restart, restart_parameters, max_iters, stop_value):
trainer = self.model_trainer_class(self.priors, self.data, idx_restart,
restart_parameters,
self.config_parameters, max_iters,
stop_value)
trainer.train()
self.model_parameters = trainer.model_parameters
self.log_likelihood = trainer.log_likelihood
def write_parameters(self, filename_base):
self.model_parameters.write_parameters(filename_base)
def _init_components(self):
raise NotImplemented
class ProbabilisticModel(object):
def __init__(self, allelenumber_max):
self.allelenumber_max = allelenumber_max
self.priors_parser = PriorParser()
self.data = Data()
self._init_components()
def read_priors(self, priors_filename):
raise NotImplemented
def read_data(self, filename_base):
self.data.read_data(filename_base)
def preprocess(self):
raise NotImplemented
def run(self, idx_restart, restart_parameters, max_iters, stop_value):
trainer = self.model_trainer_class(self.priors, self.data, idx_restart,
restart_parameters, self.config_parameters, max_iters, stop_value)
trainer.train()
self.model_parameters = trainer.model_parameters
self.log_likelihood = trainer.log_likelihood
def write_parameters(self, filename_base):
self.model_parameters.write_parameters(filename_base)
def _init_components(self):
raise NotImplemented
def convert(self):
seg_num = self.segments.num
process_num = self.process_num
if process_num > seg_num:
process_num = seg_num
pool = Pool(processes = process_num)
args_list = []
for j in range(0, seg_num):
seg_name = self.segments[j][0]
chrom = self.segments[j][1]
start = self.segments[j][2]
end = self.segments[j][3]
args_tuple = (seg_name, chrom, start, end, self.normal_bam_filename,
self.tumor_bam_filename, self.reference_genome_filename,
self.min_depth, self.min_bqual, self.min_mqual)
args_list.append(args_tuple)
counts_tuple_list = pool.map(process_by_segment, args_list)
paired_counts = []
BAF_counts = []
for counts_tuple_j in counts_tuple_list:
paired_counts_j, BAF_counts_j = counts_tuple_j
paired_counts.append(paired_counts_j)
BAF_counts.append(BAF_counts_j)
BAF_heatmap = BAFHeatMap(BAF_counts)
BAF_heatmap.write_heatmap(self.filename_base)
data = Data(self.segments, paired_counts)
data.tumor_LOH_test(self.WES_flag)
data.write_data(self.filename_base)
def __init__(self, allelenumber_max):
self.allelenumber_max = allelenumber_max
self.priors_parser = PriorParser()
self.data = Data()
self._init_components()
def __init__(self, allelenumber_max):
self.allelenumber_max = allelenumber_max
self.priors_parser = PriorParser()
self.data = Data()
self._init_components()
