def __init__(self, config, barcode_map=None):
VariantSeqLib.__init__(self, config)
BarcodeSeqLib.__init__(self, config, barcodevariant=True)
try:
if 'map file' in config['barcodes']:
self.barcode_map = BarcodeMap(config['barcodes']['map file'])
else:
self.barcode_map = None
self.set_filters(config['filters'], {'min quality' : 0,
'avg quality' : 0,
'chastity' : False,
'max mutations' : len(self.wt_dna)})
except KeyError as key:
raise EnrichError("Missing required config value %s" % key,
self.name)
if self.barcode_map is None: # not in local config
if barcode_map is None: # not provided on object creation
raise EnrichError("Barcode map not specified", self.name)
else:
self.barcode_map = barcode_map
self.counts['barcodes_unmapped'] = None
self.filter_unmapped = True
def __init__(self, config, barcode_map=None):
VariantSeqLib.__init__(self, config)
BarcodeSeqLib.__init__(self, config, barcodevariant=True)
try:
if 'map file' in config['barcodes']:
self.barcode_map = BarcodeMap(config['barcodes']['map file'])
else:
self.barcode_map = None
self.set_filters(
config['filters'], {
'min quality': 0,
'avg quality': 0,
'chastity': False,
'max mutations': len(self.wt_dna)
})
except KeyError as key:
raise EnrichError("Missing required config value %s" % key,
self.name)
if self.barcode_map is None: # not in local config
if barcode_map is None: # not provided on object creation
raise EnrichError("Barcode map not specified", self.name)
else:
self.barcode_map = barcode_map
self.counts['barcodes_unmapped'] = None
self.filter_unmapped = True
def calculate(self):
"""
Counts the barcodes using :py:meth:`BarcodeSeqLib.count` and combines them into
variant counts using the :py:class:`BarcodeMap`.
"""
BarcodeSeqLib.calculate(self) # count the barcodes
self.df_dict['variants'] = dict()
logging.info(
"Converting barcodes to variants [{name}]".format(name=self.name))
if self.filter_unmapped:
map_mask = self.df_dict['barcodes'].index.isin(self.barcode_map)
self.df_dict['barcodes_unmapped'] = self.df_dict['barcodes'][
-map_mask]
self.df_dict['barcodes'] = self.df_dict['barcodes'][map_mask]
del map_mask
logging.info(
"Writing counts for {n} unique unmapped barcodes to disk [{name}]"
.format(n=len(self.df_dict['barcodes_unmapped']),
name=self.name))
self.dump_data(keys=['barcodes_unmapped']) # save memory
# count variants associated with the barcodes
for bc, count in self.df_dict['barcodes'].iterrows():
count = count['count']
variant = self.barcode_map[bc]
mutations = self.count_variant(variant, copies=count)
if mutations is None: # variant has too many mutations
self.filter_stats['max mutations'] += count
self.filter_stats['total'] += count
if self.report_filtered:
self.report_filtered_variant(variant, count)
if bc not in self.barcode_map.bc_variant_strings:
self.barcode_map.bc_variant_strings[bc] = FILTERED_VARIANT
else:
if mutations not in self.barcode_map.variants:
self.barcode_map.variants[mutations] = set()
self.barcode_map.variants[mutations].update([bc])
self.barcode_map.bc_variant_strings[bc] = mutations
self.df_dict['variants'] = \
pd.DataFrame.from_dict(self.df_dict['variants'],
orient="index", dtype="int32")
if len(self.df_dict['variants']) == 0:
raise EnrichError("Failed to count variants", self.name)
self.df_dict['variants'].columns = ['count']
self.df_dict['variants'].sort('count', ascending=False, inplace=True)
logging.info(
"Retained counts for {n} variants ({u} unique) [{name}]".format(
n=self.df_dict['variants']['count'].sum(),
u=len(self.df_dict['variants'].index),
name=self.name))
if self.aligner is not None:
logging.info("Aligned {n} variants [{name}]".format(
n=self.aligner.calls, name=self.name))
self.aligner_cache = None
self.report_filter_stats()
def calculate(self):
"""
Counts the barcodes using :py:meth:`BarcodeSeqLib.count` and combines them into
variant counts using the :py:class:`BarcodeMap`.
"""
BarcodeSeqLib.calculate(self) # count the barcodes
self.counts['variants'] = dict()
if self.filter_unmapped:
map_mask = self.counts['barcodes'].index.isin(self.barcode_map)
self.counts['barcodes_unmapped'] = self.counts['barcodes'][
-map_mask]
self.counts['barcodes'] = self.counts['barcodes'][map_mask]
del map_mask
# count variants associated with the barcodes
for bc, count in self.counts['barcodes'].iterrows():
count = count['count']
variant = self.barcode_map[bc]
mutations = self.count_variant(variant, copies=count)
if mutations is None: # variant has too many mutations
self.filter_stats['max mutations'] += count
self.filter_stats['total'] += count
if self.verbose:
self.report_filtered_variant(variant, count)
else:
if mutations not in self.barcode_map.variants:
self.barcode_map.variants[mutations] = list()
if bc not in self.barcode_map.variants[mutations]:
self.barcode_map.variants[mutations].append(bc)
self.counts['variants'] = \
pd.DataFrame.from_dict(self.counts['variants'],
orient="index", dtype="int32")
if len(self.counts['variants']) == 0:
raise EnrichError("Failed to count variants", self.name)
self.counts['variants'].columns = ['count']
logging.info("Counted %d variants (%d unique) [%s]" % \
(self.counts['variants']['count'].sum(), len(self.counts['variants'].index), self.name))
if self.aligner is not None:
logging.info("Aligned %d variants [%s]" %
(self.aligner.calls, self.name))
self.report_filter_stats()
def calculate(self):
"""
Counts the barcodes using :py:meth:`BarcodeSeqLib.count` and combines them into
variant counts using the :py:class:`BarcodeMap`.
"""
BarcodeSeqLib.calculate(self) # count the barcodes
self.counts['variants'] = dict()
if self.filter_unmapped:
map_mask = self.counts['barcodes'].index.isin(self.barcode_map)
self.counts['barcodes_unmapped'] = self.counts['barcodes'][-map_mask]
self.counts['barcodes'] = self.counts['barcodes'][map_mask]
del map_mask
# count variants associated with the barcodes
for bc, count in self.counts['barcodes'].iterrows():
count = count['count']
variant = self.barcode_map[bc]
mutations = self.count_variant(variant, copies=count)
if mutations is None: # variant has too many mutations
self.filter_stats['max mutations'] += count
self.filter_stats['total'] += count
if self.verbose:
self.report_filtered_variant(variant, count)
else:
if mutations not in self.barcode_map.variants:
self.barcode_map.variants[mutations] = list()
if bc not in self.barcode_map.variants[mutations]:
self.barcode_map.variants[mutations].append(bc)
self.counts['variants'] = \
pd.DataFrame.from_dict(self.counts['variants'],
orient="index", dtype="int32")
if len(self.counts['variants']) == 0:
raise EnrichError("Failed to count variants", self.name)
self.counts['variants'].columns = ['count']
logging.info("Counted %d variants (%d unique) [%s]" % \
(self.counts['variants']['count'].sum(), len(self.counts['variants'].index), self.name))
if self.aligner is not None:
logging.info("Aligned %d variants [%s]" % (self.aligner.calls, self.name))
self.report_filter_stats()
