def normalize_in_logspace(dist, in_log_space=True):
if not in_log_space:
log_dist = np.log(dist, dtype=np.float64)
return np.exp(log_dist - logsumexp_scipy(log_dist))
else:
logging.debug('Likelihood before normalization\n{}'.format(dist))
log_dist = np.array(dist, dtype=np.float64)
return np.exp(log_dist - logsumexp_scipy(log_dist))
def add_sample_density(self, sample_id, density, conv=1e-40):
sample_idx = self._time_points.index(sample_id)
density = np.asarray(density, dtype=np.float32) + conv
log_density = np.log(density, dtype=np.float32)
self._hist[sample_idx] = density
self._logprior[sample_idx] = log_density - logsumexp_scipy(log_density)
self._loghist[sample_idx] = log_density - logsumexp_scipy(log_density)
logging.debug('Added density for cluster {} for sample {}'.format(
self._identifier, sample_id))
def _load_clusters(self, cluster_info_file):
logging.debug('Loading clusters from {} file'.format(cluster_info_file))
cluster_ccf = {}
means = {}
ccf_headers = ['postDP_ccf_' + str(i / 100.0) for i in range(0, 101, 1)]
with open(cluster_info_file, 'r') as reader:
for line in reader:
values = line.strip().split('\t')
if line.startswith('Patient_ID'):
header = dict((item, idx) for idx, item in enumerate(values))
else:
sample_id = values[header['Sample_ID']]
cluster_id = int(values[header['Cluster_ID']])
cluster_mean = float(values[header['postDP_ccf_mean']])
ccf = np.array([float(values[header[i]]) for i in ccf_headers], dtype=np.float64)
ccf = np.clip(ccf, a_min=1e-20, a_max=None)
ccf = np.log(ccf, dtype=np.float64)
ccf = np.exp(ccf - logsumexp_scipy(ccf))
if cluster_id not in cluster_ccf:
cluster_ccf[cluster_id] = {}
means[cluster_id] = []
means[cluster_id].append(cluster_mean)
cluster_ccf[cluster_id][sample_id] = ccf
for cluster_id in cluster_ccf:
# decide whether cluster should be removed
# if density < 0.1 across all samples add it to remove clusters, to be removed from BuildTree algorithm
if self.low_ccf_check(means[cluster_id]):
self._removed_clusters.append(cluster_id)
logging.debug('Removed cluster {} '.format(cluster_id))
return cluster_ccf
def _normalize_in_logspace(dist, in_log_space=True):
logging.debug('Distribution before normalization\n{}'.format(dist))
if in_log_space:
log_dist = np.array(dist, dtype=np.float64)
else:
logging.debug(
'Converting distribution to log space before normalization')
log_dist = np.log(dist, dtype=np.float64)
return np.exp(log_dist - logsumexp_scipy(log_dist))
def _load_mutations(self, mut_info_file):
logging.debug('Loading mutations from {} file'.format(mut_info_file))
ccf_headers = ['preDP_ccf_' + str(i / 100.0) for i in range(0, 101, 1)]
with open(mut_info_file, 'r') as reader:
for line in reader:
values = line.strip().split('\t')
if line.startswith('Patient_ID'):
header = dict((item, idx) for idx, item in enumerate(values))
elif values[header['Variant_Type']] != 'CNV':
# TODO: for reshuffling need to keep all mutations and clusters
cluster_id = int(values[header['Cluster_Assignment']])
if cluster_id not in self._removed_clusters:
chromosome = values[header['Chromosome']]
position = values[header['Start_position']]
ref = values[header['Reference_Allele']]
alt = values[header['Tumor_Seq_Allele']]
sample_id = values[header['Sample_ID']]
ccf_1d = [float(values[header[i]]) for i in ccf_headers]
ccf_1d = np.clip(np.array(ccf_1d, dtype=np.float64), a_min=1e-20, a_max=None)
ccf_1d = np.log(ccf_1d, dtype=np.float64)
ccf_1d = np.exp(ccf_1d - logsumexp_scipy(ccf_1d))
var_type = values[header['Variant_Type']]
mutation_str = ':'.join([chromosome, position, ref, alt])
if cluster_id not in self._cluster_mutations:
self._cluster_mutations[cluster_id] = {}
if mutation_str not in self._cluster_mutations[cluster_id]:
self._cluster_mutations[cluster_id][mutation_str] = {}
if sample_id not in self._samples_mutations:
self._samples_mutations[sample_id] = []
t_ref_count = self._get_count(values[header['t_ref_count']])
t_alt_count = self._get_count(values[header['t_alt_count']])
mutation = SomaticEvents.SomMutation(chromosome, position, ref, alt, ccf_1d,
ref_cnt=t_ref_count,
alt_cnt=t_alt_count,
gene=values[header['Hugo_Symbol']],
prot_change=values[header['Protein_change']],
mut_category=values[header['Variant_Classification']],
from_sample=sample_id,
type_=var_type)
self._cluster_mutations[cluster_id][mutation_str][sample_id] = mutation
self._samples_mutations[sample_id].append(mutation_str)
self._clusters[cluster_id].add_mutation(mutation)
logging.info('Mutation {} loaded from sample {}'.format(mutation_str, sample_id))
def _make_nd_histogram(hist_array, conv=1e-40):
hist = np.asarray(hist_array, dtype=np.float32) + conv
return np.apply_along_axis(lambda z: z - logsumexp_scipy(z), 1,
np.log(hist))
