def fb2fa_concatenated(x, fasta_file, num_n=0):
"""Generates feature barcode fasta file.
Parameters
----------
x : str
The path and name of feature barcode file.
fasta_file : str
The path and name of generated fasta file.
num_n : int, optional
Number of Ns to use for separating seqeunces belonging to
the same feature.
Returns
-------
str
The path and name of generated fasta file.
"""
fb = dict()
with open_by_suffix(file_name=x, mode='r') as f:
for line in f:
i = line.rstrip().split('\t')
if i[0] not in fb:
fb[i[0]] = []
fb[i[0]].append(i[1])
with open_by_suffix(file_name=fasta_file, mode='w') as fo:
for i in fb:
fo.write('>' + i + '\n')
fo.write(('N' * num_n).join(fb[i]) + '\n')
return fasta_file
def correct_cell_barcodes(cb_file,
output_directory,
bus_file,
corrected_bus_file):
"""Corrects cell barcodes."""
CELL_BARCODE_FILE = output_directory / 'barcodes_no_suffix.tsv'
with open_by_suffix(file_name=str(cb_file), mode='r') as f:
with open_by_suffix(
file_name=str(CELL_BARCODE_FILE),
mode='w') as fo:
for line in f:
i = line.rstrip().split('-')[0]
fo.write(i + '\n')
logger.info('Number of whitelisted cell barcodes: '
+ f'{len([i for i in open(CELL_BARCODE_FILE)])}')
cmd = [
get_binary_path(binary_name='bustools'),
'correct',
'-w',
str(output_directory / 'barcodes_no_suffix.tsv'),
'-o',
str(corrected_bus_file),
str(bus_file)
]
outs, errs = run_executable(cmd)
logger.info(errs)
return corrected_bus_file
def generate_modified_fastq(read1_file,
read2_file,
cb_file,
read1_coords,
modified_read_file,
num_mismatches=1,
num_n_threshold=3):
"""Matches cell barcodes and generates modified fastq file."""
cell_barcodes = [
i.rstrip().split('-')[0] for i in open_by_suffix(cb_file, mode='r')
]
cb_index = create_index(barcodes=cell_barcodes,
num_mismatches=num_mismatches)
read_counter = [int(), int()]
with dnaio.open(file1=read1_file,
file2=read2_file,
fileformat='fastq',
mode='r') as f, dnaio.open(file1=modified_read_file,
fileformat='fastq',
mode='w') as f_out:
for rec in f:
read_counter[1] += 1
read1, read2 = rec
reads = (read1.name, read1.sequence, read1.qualities,
read2.sequence, read2.qualities)
out = match_cell_barcodes(reads=reads,
barcode_index=cb_index,
read_coords=read1_coords,
num_mismatches=num_mismatches,
num_n_threshold=num_n_threshold)
if out:
read_counter[0] += 1
read_name, read1_seq, _, read2_seq, read2_qual, bc, dist = out
read_info = '#'.join([read1_seq, bc, str(dist)])
read_name = ' '.join(
[read_name.split(' ')[0], 'RI:Z:' + read_info])
s2 = dnaio.Sequence(read_name, read2_seq, read2_qual)
f_out.write(s2)
return modified_read_file, read_counter
def fb2fa_kallisto(x, fasta_file, t2g_file):
"""Prepares fasta file, t2g file and returns k-mer length.
Parameters
----------
x : str
The path and name of feature barcode file.
The example content of the file:
CD3 CTCATTGTAACTCCT
CD4 TGTTCCCGCTCAACT
CD8a GCTGCGCTTTCCATT
CD11b GACAAGTGATCTGCA
CD14 TCTCAGACCTCCGTA
CD15 TCACCAGTACCTAGT
CD16 AAGTTCACTCTTTGC
CD19 CTGGGCAATTACTCG
CD20 TTCTGGGTCCCTAGA
CD25 TTTGTCCTGTACGCC
fasta_file: str
The path and name of generated fasta file. One mismatch at each
coordinate.
The example content of the file:
>CD3_CTCATTGTAACTCCT_0_A
ATCATTGTAACTCCT
>CD3_CTCATTGTAACTCCT_0_C
CTCATTGTAACTCCT
>CD3_CTCATTGTAACTCCT_0_G
GTCATTGTAACTCCT
>CD3_CTCATTGTAACTCCT_0_T
TTCATTGTAACTCCT
>CD3_CTCATTGTAACTCCT_1_A
CACATTGTAACTCCT
>CD3_CTCATTGTAACTCCT_1_C
CCCATTGTAACTCCT
>CD3_CTCATTGTAACTCCT_1_G
CGCATTGTAACTCCT
>CD3_CTCATTGTAACTCCT_1_T
CTCATTGTAACTCCT
>CD3_CTCATTGTAACTCCT_2_A
CTAATTGTAACTCCT
>CD3_CTCATTGTAACTCCT_2_C
CTCATTGTAACTCCT
t2g_file: str
The path and name of generated t2g file.
The example content of the file:
CD3_CTCATTGTAACTCCT_0_A CD3_CTCATTGTAACTCCT CD3_CTCATTGTAACTCCT
CD3_CTCATTGTAACTCCT_0_C CD3_CTCATTGTAACTCCT CD3_CTCATTGTAACTCCT
CD3_CTCATTGTAACTCCT_0_G CD3_CTCATTGTAACTCCT CD3_CTCATTGTAACTCCT
CD3_CTCATTGTAACTCCT_0_T CD3_CTCATTGTAACTCCT CD3_CTCATTGTAACTCCT
CD3_CTCATTGTAACTCCT_1_A CD3_CTCATTGTAACTCCT CD3_CTCATTGTAACTCCT
CD3_CTCATTGTAACTCCT_1_C CD3_CTCATTGTAACTCCT CD3_CTCATTGTAACTCCT
CD3_CTCATTGTAACTCCT_1_G CD3_CTCATTGTAACTCCT CD3_CTCATTGTAACTCCT
CD3_CTCATTGTAACTCCT_1_T CD3_CTCATTGTAACTCCT CD3_CTCATTGTAACTCCT
CD3_CTCATTGTAACTCCT_2_A CD3_CTCATTGTAACTCCT CD3_CTCATTGTAACTCCT
CD3_CTCATTGTAACTCCT_2_C CD3_CTCATTGTAACTCCT CD3_CTCATTGTAACTCCT
Returns
-------
int
Largest odd number of the minimal length of the feature barcodes.
"""
sequence_lengths = list()
sequence_names = list()
with open_by_suffix(file_name=x, mode='r') as f:
with open_by_suffix(file_name=fasta_file, mode='w') as fo:
for line in f:
i = line.rstrip().split('\t')
sequence = i[1]
sequence_lengths.append(len(sequence))
for ii in range(len(sequence)):
for iii in 'ACGT':
sequence_name = '_'.join(
[i[0], sequence, str(ii), iii])
sequence_mutated = sequence[0:ii] + \
iii + \
sequence[ii + 1:len(sequence)]
sequence_names.append(
'\t'.join(
[sequence_name] + [i[0] + '_' + sequence] * 2
)
)
fo.write(
'>' + sequence_name
+ '\n'
+ sequence_mutated
+ '\n'
)
with open_by_suffix(file_name=t2g_file, mode='w') as foo:
foo.write('\n'.join(sequence_names))
num_sequnces = set([i.split('\t')[1] for i in sequence_names])
logger.info(f'Number of feature barcodes: {len(num_sequnces)}')
kmer = min(sequence_lengths)
if kmer % 2 == 0:
kmer -= 1
logger.info(f'k-mer length: {kmer}')
return kmer
def filter_matching(matching_file,
filtered_matching_file,
cb_pos_start=0,
cb_num_mismatches=1,
cb_left_shift=1,
cb_right_shift=1,
cb_extra_seq=None,
cb_extra_seq_num_mismatches=None,
fb_pos_start=10,
fb_num_mismatches=1,
fb_left_shift=1,
fb_right_shift=1,
fb_extra_seq=None,
fb_extra_seq_num_mismatches=None):
"""Filters raw cell and feature barcode matching result."""
with open_by_suffix(file_name=matching_file) as f:
header_line = next(f).rstrip().split('\t')
logger.info('Header line: {}'.format(' '.join(header_line)))
logger.info(
f'Cell barcode maximum number of mismatches: {cb_num_mismatches}')
logger.info(('Feature barcode maximum number of mismatches: ' +
f'{fb_num_mismatches}'))
with open_by_suffix(file_name=filtered_matching_file, mode='w') as fo:
fo.write('\t'.join(header_line) + '\n')
read_counter = [int(), int()]
if len(header_line) == 6:
logger.info(
'Skipping arguments: ' +
'"cb_pos_start", "cb_left_shift", "cb_right_shift"')
logger.info(
'Skipping arguments: ' +
'"fb_pos_start", "fb_left_shift", "fb_right_shift"')
for line in f:
read_counter[1] += 1
i = line.rstrip().split('\t')
if cb_extra_seq and cb_extra_seq_num_mismatches:
cell_barcode_sequence_regex = \
compile_regex_ref_barcodes_single(
cb_extra_seq,
num_mismatches=cb_extra_seq_num_mismatches
)
else:
cell_barcode_sequence_regex = None
if len(header_line) == 8 or len(header_line) == 12:
cell_barcode_matching = i[:4]
cell_barcode_passed = is_matched(
x=cell_barcode_matching,
barcode_pos_start=cb_pos_start,
mismatching_threshold=cb_num_mismatches,
left_shift=cb_left_shift,
right_shift=cb_right_shift,
sequence_regex=cell_barcode_sequence_regex)
elif len(header_line) == 6:
if int(i[2]) <= cb_num_mismatches:
cell_barcode_passed = True
else:
cell_barcode_passed = False
if cell_barcode_passed:
if fb_extra_seq and fb_extra_seq_num_mismatches:
feature_barcode_sequence_regex = \
compile_regex_ref_barcodes_single(
fb_extra_seq,
num_mismatches=fb_extra_seq_num_mismatches
)
else:
feature_barcode_sequence_regex = None
if len(header_line) == 8 or len(header_line) == 12:
feature_barcode_matching = i[4:8]
feature_barcode_passed = is_matched(
x=feature_barcode_matching,
barcode_pos_start=fb_pos_start,
mismatching_threshold=fb_num_mismatches,
left_shift=fb_left_shift,
right_shift=fb_right_shift,
sequence_regex=feature_barcode_sequence_regex)
elif len(header_line) == 6:
if int(i[5]) <= fb_num_mismatches:
feature_barcode_passed = True
else:
feature_barcode_passed = False
if feature_barcode_passed:
fo.write(line)
read_counter[0] += 1
logger.info(f'Number of lines processed: {read_counter[1]:,}')
logger.info(f'Number of lines passed filters: {read_counter[0]:,}')
return filtered_matching_file
def main():
args = parse_args()
logger = get_logger(logger_name=__name__)
banner = """
█████▒▄▄▄▄ ▄▄▄
▓██ ▒▓█████▄ ▒████▄
▒████ ░▒██▒ ▄██▒██ ▀█▄
░▓█▒ ░▒██░█▀ ░██▄▄▄▄██
░▒█░ ░▓█ ▀█▓ ▓█ ▓██▒
▒ ░ ░▒▓███▀▒ ▒▒ ▓▒█░
░ ▒░▒ ░ ▒ ▒▒ ░
░ ░ ░ ░ ░ ▒
░ ░ ░
░
"""
logger.info(banner)
# print(banner)
logger.info(f'fba version: {__version__}')
logger.info('Initiating logging ...')
logger.info(
f'Python version: {sys.version_info.major}.{sys.version_info.minor}')
if not sys.version_info.major == 3 and sys.version_info.minor >= 6:
logger.critical('Please use Python >= 3.6')
sys.exit(1)
if (args.command == 'extract'):
logger.info('Using extract subcommand ...')
m = importlib.import_module(name='fba.levenshtein')
with open_by_suffix(file_name=args.output, mode='w') as f:
f.write('\t'.join(
[
'read1_seq',
'cell_barcode',
'cb_num_mismatches',
'read2_seq',
'feature_barcode',
'fb_num_mismatches'
]
) + '\n')
for out in m.extract_feature_barcoding_fastss(
read1_file=args.read1,
read2_file=args.read2,
cb_file=args.whitelist,
fb_file=args.feature_ref,
cb_num_mismatches=args.cell_barcode_mismatches,
fb_num_mismatches=args.feature_barcode_mismatches,
read1_coords=args.read1_coords,
read2_coords=args.read2_coords,
cb_num_n_threshold=args.cb_num_n_threshold,
fb_num_n_threshold=args.fb_num_n_threshold
):
f.write(out + '\n')
logger.info('Done.')
elif (args.command == 'map'):
logger.info('Using map subcommand ...')
m = importlib.import_module(name=f'fba.{args.command}')
matrix_featurecount = m.map_feature_barcoding(
read1_file=args.read1,
read2_file=args.read2,
cb_file=args.whitelist,
fb_file=args.feature_ref,
read1_coords=args.read1_coords,
num_mismatches=args.cell_barcode_mismatches,
num_n_threshold=args.cb_num_n_threshold,
num_n_ref=args.num_n_ref,
umi_pos_start=args.umi_pos_start,
umi_length=args.umi_length,
umi_deduplication_method=args.umi_deduplication_method,
umi_deduplication_threshold=args.umi_mismatches,
mapq=args.mapq,
output_directory=args.output_directory,
num_threads=args.threads,
aligner=args.aligner
)
matrix_featurecount.to_csv(path_or_buf=args.output,
compression='infer')
logger.info('Done.')
elif (args.command == 'filter'):
logger.info('Using filter subcommand ...')
m = importlib.import_module(name=f'fba.{args.command}')
_ = m.filter_matching(
matching_file=args.input,
filtered_matching_file=args.output,
cb_pos_start=args.cell_barcode_pos_start,
cb_num_mismatches=args.cell_barcode_mismatches,
cb_left_shift=args.cell_barcode_left_shift,
cb_right_shift=args.cell_barcode_right_shift,
cb_extra_seq=args.cell_barcode_extra_seq,
cb_extra_seq_num_mismatches=args.cell_barcode_extra_seq_mismatches,
fb_pos_start=args.feature_barcode_pos_start,
fb_num_mismatches=args.feature_barcode_mismatches,
fb_left_shift=args.feature_barcode_left_shift,
fb_right_shift=args.feature_barcode_right_shift,
fb_extra_seq=args.cell_barcode_extra_seq,
fb_extra_seq_num_mismatches=args.feature_barcode_extra_seq_mismatches) # noqa
logger.info(f'Filtered feature barcoding result: {_}')
logger.info('Done.')
elif (args.command == 'count'):
logger.info('Using count subcommand ...')
m = importlib.import_module(name=f'fba.{args.command}')
matrix_featurecount = m.generate_matrix(
matching_file=args.input,
umi_pos_start=args.umi_pos_start,
umi_length=args.umi_length,
umi_deduplication_method=args.umi_deduplication_method,
umi_deduplication_threshold=args.umi_mismatches
)
matrix_featurecount.to_csv(
path_or_buf=args.output,
compression='infer'
)
logger.info('Done.')
elif (args.command == 'demultiplex'):
logger.info('Using demultiplex subcommand ...')
m = importlib.import_module(name=f'fba.{args.command}')
_ = m.demultiplex_feature_barcoding(
matrix_featurecount_file=args.input,
output_directory=args.output_directory,
q=args.quantile,
initial_clustering_methold=args.clustering_method,
visualization=args.visualization,
embeding_method=args.visualization_method,
seed=42
)
logger.info('Done.')
elif (args.command == 'qc'):
logger.info('Using qc subcommand ...')
m = importlib.import_module(name=f'fba.{args.command}')
import pandas as pd
from pathlib import Path
if not isinstance(args.num_reads, int):
if args.num_reads.isdigit():
num_reads = int(args.num_reads)
elif args.num_reads.upper() == 'NONE':
num_reads = None
else:
sys.exit(1)
else:
num_reads = args.num_reads
if args.read1:
_ = m.summarize_sequence_content(
read1_file=args.read1,
read2_file=args.read2,
num_reads=num_reads,
output_directory=args.output_directory
)
OUTPUT_FILE = 'feature_barcoding_output.tsv.gz'
OUTPUT_FILE = str(Path(args.output_directory) / OUTPUT_FILE)
with open_by_suffix(file_name=OUTPUT_FILE, mode='w') as f:
f.write('\t'.join(
[
'read1_seq',
'cell_barcode',
'cb_matching_pos',
'cb_matching_description',
'read2_seq',
'feature_barcode',
'fb_matching_pos',
'fb_matching_description'
]
) + '\n')
n = importlib.import_module(name='fba.regex')
for out in n.extract_feature_barcoding_regex(
read1_file=args.read1,
read2_file=args.read2,
cb_file=args.whitelist,
fb_file=args.feature_ref,
cb_num_mismatches=args.cell_barcode_mismatches,
fb_num_mismatches=args.feature_barcode_mismatches,
cb_num_n_threshold=args.cb_num_n_threshold,
fb_num_n_threshold=args.fb_num_n_threshold,
read1_coords=args.read1_coords,
read2_coords=args.read2_coords,
num_threads=args.threads,
chunk_size=args.chunk_size,
num_reads=num_reads):
f.write(out + '\n')
_ = m.summarize_barcode_positions(
matching_file=OUTPUT_FILE,
output_directory=args.output_directory)
else:
logger.info('Bulk mode enabled: '
'only feature barcodes on reads 2 are analyzed')
if not args.read2_coords:
logger.critical('Please specify "-r2_coords" in bulk mode')
sys.exit(1)
logger.info(
'Skipping arguments: "-1", "-w", "-cb_m", "-r1_coords"'
)
fb_frequency = m.analyze_bulk(
read_file=args.read2,
read_coords=args.read2_coords,
fb_file=args.feature_ref,
num_mismatches=args.feature_barcode_mismatches,
num_n_threshold=args.fb_num_n_threshold,
num_reads=num_reads
)
Path(args.output_directory).mkdir(exist_ok=True)
OUTPUT_FILE = 'feature_barcode_frequency.csv'
OUTPUT_FILE = str(Path(args.output_directory) / OUTPUT_FILE)
logger.info(f'Output file: {OUTPUT_FILE}')
fb_frequency = pd.DataFrame.from_dict(
data=fb_frequency,
orient='index',
columns=['num_reads']).sort_values(
by='num_reads',
ascending=False
)
fb_frequency['percentage'] = fb_frequency['num_reads'] / sum(
fb_frequency['num_reads'])
fb_frequency.to_csv(path_or_buf=OUTPUT_FILE)
logger.info('Done.')
elif (args.command == 'kallisto_wrapper'):
logger.info('Using kallisto_wrapper subcommand ...')
m = importlib.import_module(name='fba.kallisto')
matrix_featurecount = m.run_kallisto(
read1_file=args.read1,
read2_file=args.read2,
cb_file=args.whitelist,
fb_file=args.feature_ref,
technology=args.technology, # '10xv3',
output_directory=args.output_directory, # 'kallisto',
num_threads=args.threads)
matrix_featurecount.to_csv(
path_or_buf=args.output,
compression='infer'
)
def generate_matrix(matching_file,
umi_pos_start=16,
umi_length=12,
umi_deduplication_method='directional',
umi_deduplication_threshold=1):
"""Generates a matrix based on matching results.
Parameters
----------
matching_file : str
The path and name of matching result file.
umi_length : int, optional
The length of UMI on read 1 after cell barcode. The default is 12.
umi_pos_start : int, optional
The starting coordiate of UMI on read 1. If the input matching result
is from the regex method of extract subcommand, the staring
coordinate will be auto determined.
umi_deduplication_method : str, optional
The UMI dedupliation method used in UMI-tools
(Smith, T., et al. (2017). Genome Res. 27, 491–499.).
See https://cgatoxford.wordpress.com/2015/08/14/unique-molecular-identifiers-the-problem-the-solution-and-the-proof
umi_deduplication_threshold : int, optional
The mismatch tolerance for UMI deduplication.
Returns
-------
DataFrame
A pandas DataFrame of feature count. The columns are cells and
the rows are features.
""" # noqa
logger.info(f'UMI-tools version: {umi_tools_version}')
matrix_featurecount = defaultdict(dict)
line_counter = int()
with open_by_suffix(file_name=matching_file) as f:
header_line = next(f)
if len(header_line.split('\t')) == 6:
if umi_pos_start:
logger.info(
f'UMI starting position on read 1: {umi_pos_start}'
)
else:
logger.critical(
'Need to specify UMI starting position on read 1: -us'
)
raise ValueError('need to specify UMI starting position')
else:
logger.info('UMI start position on read 1 auto-detected, '
'overriding -us')
logger.info(f'UMI length: {umi_length}')
logger.info('UMI-tools deduplication threshold: '
f'{umi_deduplication_threshold}')
logger.info('UMI-tools deduplication method: '
f'{umi_deduplication_method}')
logger.info('Header line: {}'.format(
header_line.rstrip().replace('\t', ' ')))
for line in f:
i = line.rstrip().split('\t')
line_counter += 1
read_seq = i[0]
cell_barcode = i[1]
if len(header_line.split('\t')) == 6:
feature_barcode = i[4]
else:
feature_barcode = i[5]
umi_pos_start = [int(ii) for ii in i[2].split(':')][1]
umi_pos_end = umi_pos_start + umi_length
if len(read_seq) >= umi_pos_end:
umi_seq = read_seq[
umi_pos_start:umi_pos_end].upper().encode()
if feature_barcode not in matrix_featurecount[cell_barcode]:
matrix_featurecount[cell_barcode][feature_barcode] = list()
matrix_featurecount[cell_barcode][
feature_barcode].append(umi_seq)
logger.info(f'Number of lines processed: {line_counter:,}')
cell_barcodes = sorted(matrix_featurecount.keys())
feature_barcodes = sorted(
set([ii
for i in matrix_featurecount
for ii in matrix_featurecount[i]])
)
logger.info(f'Number of cell barcodes detected: {len(cell_barcodes):,}')
logger.info(f'Number of features detected: {len(feature_barcodes):,}')
clusterer = UMIClusterer(cluster_method=umi_deduplication_method)
for i in matrix_featurecount:
for ii in feature_barcodes:
umis = matrix_featurecount[i].setdefault(ii, 0)
if umis:
matrix_featurecount[i][ii] = len(
clusterer(Counter(umis),
threshold=umi_deduplication_threshold)
)
matrix_featurecount = {i: [matrix_featurecount[i][ii]
for ii in feature_barcodes]
for i in cell_barcodes}
matrix_featurecount = pd.DataFrame.from_dict(matrix_featurecount,
orient='columns')
matrix_featurecount.index = feature_barcodes
logger.info('Total UMIs after deduplication: '
f'{matrix_featurecount.values.sum():,}')
logger.info('Median number of UMIs per cell: '
f'{np.median(matrix_featurecount.sum(axis=0)):,}')
return matrix_featurecount
def extract_feature_barcoding_fastss(read1_file,
read2_file,
cb_file,
fb_file,
read1_coords,
read2_coords,
cb_num_mismatches,
fb_num_mismatches,
cb_num_n_threshold=3,
fb_num_n_threshold=3):
"""Extracts feature barcodes."""
with open_by_suffix(file_name=cb_file) as f:
cell_barcodes = [i.split('-')[0].rstrip() for i in f]
with open_by_suffix(file_name=fb_file) as f:
feature_barcodes = {
i.rstrip().split('\t')[-1]: i.rstrip().replace('\t', '_')
for i in f
}
logger.info(f'Number of reference cell barcodes: {len(cell_barcodes):,}')
logger.info(
f'Number of reference feature barcodes: {len(feature_barcodes):,}'
)
logger.info('Read 1 coordinates to search: [' +
', '.join([str(i) for i in read1_coords]) + ')')
logger.info('Read 2 coordinates to search: [' +
', '.join([str(i) for i in read2_coords]) + ')')
logger.info(
f'Cell barcode maximum number of mismatches: {cb_num_mismatches}')
logger.info(
f'Feature barcode maximum number of mismatches: {fb_num_mismatches}')
logger.info(
f'Read 1 maximum number of N allowed: {cb_num_n_threshold}')
logger.info(
f'Read 2 maximum number of N allowed: {fb_num_n_threshold}')
cb_index = create_index(barcodes=cell_barcodes,
num_mismatches=cb_num_mismatches)
fb_index = create_index(barcodes=feature_barcodes.keys(),
num_mismatches=fb_num_mismatches)
logger.info('Matching ...')
with dnaio.open(file1=read1_file,
file2=read2_file,
fileformat='fastq',
mode='r') as f:
read_counter = [int(), int()]
for rec in f:
read1, read2 = rec
read_counter[1] += 1
if read_counter[1] % 10_000_000 == 0:
logger.info(f'Read pairs processed: {read_counter[1]:,}')
out = match_barcodes_paired_fastss(
read_seqs=(read1.sequence, read1.qualities,
read2.sequence, read2.qualities),
cb_index=cb_index,
fb_index=fb_index,
feature_barcodes=feature_barcodes,
read1_coords=read1_coords,
read2_coords=read2_coords,
cb_num_mismatches=cb_num_mismatches,
fb_num_mismatches=fb_num_mismatches,
cb_num_n_threshold=cb_num_n_threshold,
fb_num_n_threshold=fb_num_n_threshold
)
if out:
read_counter[0] += 1
yield '\t'.join(out)
def map_feature_barcoding(read1_file,
read2_file,
cb_file,
fb_file,
read1_coords,
num_mismatches=1,
num_n_threshold=3,
num_n_ref=0,
umi_pos_start=16,
umi_length=12,
umi_deduplication_method='directional',
umi_deduplication_threshold=1,
mapq=10,
output_directory='barcode_mapping',
num_threads=None,
aligner='bwa'):
"""Maps feature barcoding. """
output_directory = Path(output_directory)
output_directory.mkdir(exist_ok=True)
FB_FASTA_FILE = str(output_directory / 'feature_ref.fasta')
FEATURE_BARCODE_REF = str(output_directory / 'feature_ref')
ALIGNMENT_FILE = str(output_directory / 'aligned.bam')
fasta_file = fb2fa_concatenated(x=fb_file,
fasta_file=FB_FASTA_FILE,
num_n=num_n_ref)
if aligner == 'bowtie2':
FEATURE_BARCODE_INDEX_LOG = str(output_directory / 'bowtie2-build.log')
UNALIGNED_BAM_FILE = str(output_directory / 'unaligned.bam')
ALIGNMENT_LOG = str(output_directory / 'bowtie2.log')
logger.info(f'bowtie2 version: {parse_bowtie2_version()}')
if version.parse(parse_bowtie2_version()) < version.parse('2.4.0'):
logger.critical('Please use bowtie2 >= 2.4.0')
sys.exit(1)
feature_barcode_ref, _ = build_bt2_index(
fasta_file=fasta_file, index_base=FEATURE_BARCODE_REF)
elif aligner == 'bwa':
FEATURE_BARCODE_INDEX_LOG = str(output_directory / 'bwa-index.log')
MODIFIED_READ_FILE = str(output_directory / 'modified.fq.gz')
ALIGNMENT_LOG = str(output_directory / 'bwa.log')
logger.info(f'bwa version: {parse_bwa_version()}')
if version.parse(parse_bwa_version()) < version.parse('0.7.0'):
logger.critical('Please use bwa >= 0.7.0')
sys.exit(1)
fasta_file, _ = build_bwa_index(fasta_file=fasta_file)
logger.info(f'samtools version: {parse_samtools_version()}')
with open_by_suffix(file_name=FEATURE_BARCODE_INDEX_LOG, mode='w') as f:
f.write(_)
num_cb = len([i for i in open_by_suffix(cb_file)])
logger.info(f'Number of reference cell barcodes: {num_cb:,}')
logger.info('Read 1 coordinates to search: [' +
', '.join([str(i) for i in read1_coords]) + ')')
logger.info(f'Cell barcode maximum number of mismatches: {num_mismatches}')
logger.info(f'Read 1 maximum number of N allowed: {num_n_threshold}')
logger.info('Matching cell barcodes, read 1 ...')
if aligner == 'bowtie2':
unaligned_bam_file, read_counter = generate_unaligned_bam(
read1_file=read1_file,
read2_file=read2_file,
cb_file=cb_file,
fb_file=fb_file,
unaligned_bam_file=UNALIGNED_BAM_FILE,
read1_coords=read1_coords,
num_mismatches=num_mismatches,
num_n_threshold=num_n_threshold,
num_n_ref=num_n_ref)
elif aligner == 'bwa':
modified_read_file, read_counter = generate_modified_fastq(
read1_file=read1_file,
read2_file=read2_file,
cb_file=cb_file,
read1_coords=read1_coords,
modified_read_file=MODIFIED_READ_FILE,
num_mismatches=num_mismatches,
num_n_threshold=num_n_threshold)
logger.info(f'number of read pairs processed: {read_counter[1]:,}')
logger.info('Number of read pairs w/ valid cell barcodes: '
f'{read_counter[0]:,}')
num_fb = len(set([i.split('\t')[0] for i in open_by_suffix(fb_file)]))
logger.info(f'Number of reference features: {num_fb:,}')
if not num_threads:
num_threads = cpu_count()
logger.info(f'Number of threads: {num_threads}')
logger.info('Aligning read 2 ...')
if aligner == 'bowtie2':
alignment_file, _ = align_reads_bowtie2(
unaligned_bam_file=unaligned_bam_file,
index_base=feature_barcode_ref,
alignment_file=ALIGNMENT_FILE,
temp_prefix=next(_get_candidate_names()),
num_threads=num_threads)
elif aligner == 'bwa':
alignment_file, _ = align_reads_bwa(
modified_read_file=modified_read_file,
index_base=fasta_file,
alignment_file=ALIGNMENT_FILE,
temp_prefix=next(_get_candidate_names()),
num_threads=num_threads)
pysam.index(alignment_file, alignment_file + '.bai')
with open_by_suffix(file_name=ALIGNMENT_LOG, mode='w') as f:
f.write(_)
logger.info(f'\n{_.rstrip()}')
logger.info('Generating matrix (UMI deduplication) ...')
logger.info(f'UMI-tools version: {umi_tools_version}')
logger.info(f'Mapping quality threshold: {mapq}')
logger.info(f'UMI starting position on read 1: {umi_pos_start}')
logger.info(f'UMI length: {umi_length}')
logger.info('UMI-tools deduplication threshold: '
f'{umi_deduplication_threshold}')
logger.info('UMI-tools deduplication method: '
f'{umi_deduplication_method}')
matrix_featurecount = generate_matrix_from_alignment(
alignment_file=alignment_file,
umi_pos_start=umi_pos_start,
umi_length=umi_length,
umi_deduplication_method='directional',
umi_deduplication_threshold=umi_deduplication_threshold)
logger.info(
f'Number of cell barcodes detected: {matrix_featurecount.shape[1]:,}')
logger.info(
f'Number of features detected: {matrix_featurecount.shape[0]:,}')
logger.info('Total UMIs after deduplication: '
f'{matrix_featurecount.values.sum():,}')
logger.info('Median number of UMIs per cell: '
f'{np.median(matrix_featurecount.sum(axis=0)):,}')
return matrix_featurecount
def generate_unaligned_bam(read1_file,
read2_file,
cb_file,
fb_file,
unaligned_bam_file,
read1_coords,
num_mismatches=1,
num_n_threshold=3,
num_n_ref=0):
"""Matches cell barcodes and generates unaligned bam.
Parameters
----------
read1_file : str
The path and name of read 1 file.
read2_file : str
The path and name of read 2 file.
cb_file : str
The path and name of cell barcode file.
fb_file : str
The path and name of feature barcode file.
unaligned_bam_file : str
The path and name of unaligned file.
read1_coords : tuple or list
The positions of read 1 to compare against cell barcodes.
num_mismatches : int, optional
Maximum levenshtein distance allowd.
num_n_threshold : int, optional
Maximum Ns allowd for read 1. Read 1 with more Ns than this
threshold will be skipped.
num_n_ref : int, optional
Number of Ns to use for separating seqeunces belonging to
the same feature. Needed for correctly constructing bam header.
Returns
-------
str
The path and name of unaligned file.
"""
cell_barcodes = [
i.rstrip().split('-')[0] for i in open_by_suffix(cb_file, mode='r')
]
cb_index = create_index(barcodes=cell_barcodes,
num_mismatches=num_mismatches)
# create bam header
feature_barcodes = dict()
with open_by_suffix(file_name=fb_file, mode='r') as f:
for line in f:
i = line.rstrip().split('\t')
if i[0] not in feature_barcodes:
feature_barcodes[i[0]] = []
feature_barcodes[i[0]].append(i[1])
feature_barcodes = [{
'LN': len(('N' * num_n_ref).join(feature_barcodes[i])),
'SN': i
} for i in feature_barcodes]
rg = {
'ID': 'fba',
'LB': 'null',
'PL': 'illumina',
'PU': 'null',
'SM': 'null'
}
pg = {
'ID': 'fba',
'PN': 'fba',
'VN': __version__,
'CL': ' '.join(sys.argv)
}
fb_bam_header = {
'HD': {
'VN': '1.6'
},
'SQ': feature_barcodes,
'RG': [rg],
'PG': [pg]
}
def _get_sequence(read1_file, read2_file):
"""Gets sequences and qualities."""
with dnaio.open(file1=read1_file,
file2=read2_file,
fileformat='fastq',
mode='r') as f:
for rec in f:
read1, read2 = rec
yield read1.name, read1.sequence, read1.qualities, \
read2.sequence, read2.qualities
read_counter = [int(), int()]
with pysam.AlignmentFile(unaligned_bam_file, 'wb',
header=fb_bam_header) as outf:
for i in _get_sequence(read1_file, read2_file):
read_counter[1] += 1
out = match_cell_barcodes(reads=i,
barcode_index=cb_index,
read_coords=read1_coords,
num_mismatches=num_mismatches,
num_n_threshold=num_n_threshold)
if out:
read_counter[0] += 1
outf.write(compose_aln(out))
return unaligned_bam_file, read_counter
def analyze_bulk(read_file,
read_coords,
fb_file,
num_mismatches=1,
num_n_threshold=3,
num_reads=None):
"""Searches feature barcodes on reads 2 and generates matrix.
Parameters
----------
read_file : str
The path and name of read 2 file.
read2_coords : tuple or list
The positions on read 2 to search.
fb_file : str
The path and name of feature barcoding file.
num_mismatches : int, optional
Maximum levenshtein distance allowed.
num_n_threshoold : int, optional
Maximum Ns allowed for reads.
num_reads ; int, optional
Number of reads to analyze.
Returns
-------
dict
Count and frequency of each feature barcode in the provided fastq file.
"""
with open_by_suffix(file_name=fb_file) as f:
feature_barcodes = {
i.rstrip().split('\t')[-1]: i.rstrip().replace('\t', '_')
for i in f
}
fb_index = create_index(barcodes=feature_barcodes.keys(),
num_mismatches=num_mismatches)
feature_barcode_count = {i: int() for i in feature_barcodes}
logger.info('Number of reference feature barcodes: '
f'{len(feature_barcode_count):,}')
logger.info('Read 2 coordinates to search: [' +
', '.join([str(i) for i in read_coords]) + ')')
logger.info(
f'Feature barcode maximum number of mismatches: {num_mismatches}')
logger.info(f'Read 2 maximum number of N allowed: {num_n_threshold}')
if num_reads:
logger.info(f'Number of read pairs to analyze: {num_reads:,}')
else:
logger.info('Number of read pairs to analyze: all')
def _get_sequence(read_file):
"""Gets sequences."""
with dnaio.open(file1=read_file,
file2=None,
fileformat='fastq',
mode='r') as f:
for read in f:
yield read.sequence, read.qualities
_reads = islice(_get_sequence(read_file), 0, num_reads)
logger.info('Matching ...')
read_counter = int()
for read_seq, read_qual in _reads:
read_counter += 1
if read_counter % 10_000_000 == 0:
logger.info(f'Reads processed: {read_counter:,}')
if read_seq.count('N') <= num_n_threshold:
x2, y2 = read_coords
fb_queries = query_index(read_seq[x2:y2],
barcode_index=fb_index,
num_mismatches=num_mismatches)
fb_matched = select_query(fb_queries, read_seq[x2:y2],
read_qual[x2:y2])
if fb_matched:
feature_barcode_count[fb_matched[0]] += 1
def summarize_barcode_positions(matching_file, output_directory='qc'):
"""Summarizes barcode positions for reads 1 and reads 2.
Parameters
----------
matching_file : str
The path and name of matching result.
output_directory : str, optional
The path and name for the output directory.
Returns
-------
str
The path and name for the output directory.
"""
logger.info('Summarizing barcode coordinates ...')
logger.info(f'Output directory: {output_directory}')
# read1
Path(output_directory).mkdir(exist_ok=True)
R1_BC_STARTING_FILE = \
Path(output_directory) / 'Read1_barcodes_starting.csv'
R1_BC_ENDING_FILE = \
Path(output_directory) / 'Read1_barcodes_ending.csv'
R1_BC_STARTING_ENDING_PLOT = \
Path(output_directory) / 'Pyplot_read1_barcodes_starting_ending.pdf'
# read2
R2_BC_STARTING_FILE = \
Path(output_directory) / 'Read2_barcodes_starting.csv'
R2_BC_ENDING_FILE = \
Path(output_directory) / 'Read2_barcodes_ending.csv'
R2_BC_STARTING_ENDING_PLOT = \
Path(output_directory) / 'Pyplot_read2_barcodes_starting_ending.pdf'
# summary
CB_MISMATCHES_FILE = \
Path(output_directory) / 'Read1_barcodes_mismatches.csv'
FB_MISMATCHES_FILE = \
Path(output_directory) / 'Read2_barcodes_mismatches.csv'
MATCHED_BC_RATIO_FILE = Path(
output_directory) / 'matched_barcode_ratio.csv'
#
with open_by_suffix(file_name=matching_file) as f:
next(f)
first_line = next(f)
read1_length = len(first_line.split('\t')[0])
read2_length = len(first_line.split('\t')[4])
# barcode starts and ends
barcode_counter = [int(), int()]
cb_matching_pos = list()
cb_matching_description = list()
cb_mismatches = list()
fb_matching_pos = list()
fb_matching_description = list()
fb_mismatches = list()
with open_by_suffix(file_name=matching_file) as f:
next(f)
for line in f:
i = line.rstrip().split('\t')
barcode_counter[1] += 1
if (i[2] not in {'no_match', 'n_skipping'}
and i[5] not in {'no_match', 'NA'}):
barcode_counter[0] += 1
cb_matching_pos.append(i[2])
cb_matching_description.append(i[3])
_ = [int(ii) for ii in i[2].split(':')]
cb_mismatches.append(
len(i[1]) - (_[1] - _[0]) +
sum([int(ii) for ii in i[3].split(':')]))
fb_matching_pos.append(i[6])
fb_matching_description.append(i[7])
_ = [int(ii) for ii in i[6].split(':')]
fb_mismatches.append(
len(i[5]) - (_[1] - _[0]) +
sum([int(ii) for ii in i[7].split(':')]))
barcode_counter.append(barcode_counter[0] / barcode_counter[1])
with open_by_suffix(file_name=MATCHED_BC_RATIO_FILE, mode='w') as f:
f.write(','.join(['valid', 'total', 'ratio']) + '\n' +
','.join([str(i) for i in barcode_counter]) + '\n')
cb_mismatches = pd.Series(cb_mismatches).value_counts().to_frame(
name='count')
cb_mismatches['ratio'] = cb_mismatches['count'] / \
sum(cb_mismatches['count'])
cb_mismatches.sort_index().to_csv(CB_MISMATCHES_FILE)
fb_mismatches = pd.Series(fb_mismatches).value_counts().to_frame(
name='count')
fb_mismatches['ratio'] = fb_mismatches['count'] / \
sum(fb_mismatches['count'])
fb_mismatches.sort_index().to_csv(FB_MISMATCHES_FILE)
# cell barcode
cb_s = [int(i.split(':')[0]) for i in cb_matching_pos]
cb_e = [int(i.split(':')[1]) - 1 for i in cb_matching_pos]
cb_start_dist = pd.Series(cb_s).value_counts().to_frame(
name='count').reindex(list(range(read1_length))).fillna(0).astype(
np.int64)
cb_start_dist.to_csv(R1_BC_STARTING_FILE)
cb_end_dist = pd.Series(cb_e).value_counts().to_frame(
name='count').reindex(list(range(read1_length))).fillna(0).astype(
np.int64)
cb_end_dist.to_csv(R1_BC_ENDING_FILE)
fig, ax = plt.subplots(nrows=1,
ncols=1,
figsize=(max(2.8, read1_length / 15), 2.5))
plot_barcode_startend(s=cb_start_dist['count'] /
sum(cb_start_dist['count']),
e=cb_end_dist['count'] / sum(cb_end_dist['count']),
bases=cb_start_dist.index.values,
title='Distribution of cell barcode positions',
ax=ax)
plt.tight_layout()
fig.savefig(fname=R1_BC_STARTING_ENDING_PLOT,
transparent=None,
bbox_inches='tight')
# feature barcode
fb_s = [int(i.split(':')[0]) for i in fb_matching_pos]
fb_e = [int(i.split(':')[1]) - 1 for i in fb_matching_pos]
fb_start_dist = pd.Series(fb_s).value_counts().to_frame(
name='count').reindex(list(range(read2_length))).fillna(0).astype(
np.int64)
fb_start_dist.to_csv(R2_BC_STARTING_FILE)
fb_end_dist = pd.Series(fb_e).value_counts().to_frame(
name='count').reindex(list(range(read2_length))).fillna(0).astype(
np.int64)
fb_end_dist.to_csv(R2_BC_ENDING_FILE)
fig, ax = plt.subplots(nrows=1,
ncols=1,
figsize=(max(2.8, read2_length / 15), 2.5))
plot_barcode_startend(s=fb_start_dist['count'] /
sum(fb_start_dist['count']),
e=fb_end_dist['count'] / sum(fb_end_dist['count']),
bases=fb_start_dist.index.values,
title='Distribution of feature barcode positions',
ax=ax)
plt.tight_layout()
fig.savefig(fname=R2_BC_STARTING_ENDING_PLOT,
transparent=None,
bbox_inches='tight')
return output_directory
def extract_feature_barcoding_regex(read1_file,
read2_file,
cb_file,
fb_file,
cb_num_mismatches,
fb_num_mismatches,
cb_num_n_threshold=3,
fb_num_n_threshold=3,
read1_coords=None,
read2_coords=None,
num_threads=None,
chunk_size=1000,
num_reads=None):
"""Extracts feature barcodes."""
logger.info(f'regex version: {regex.__version__}')
with open_by_suffix(file_name=cb_file) as f:
cell_barcodes = [i.split('-')[0].rstrip() for i in f]
with open_by_suffix(file_name=fb_file) as f:
feature_barcodes = {
i.rstrip().split('\t')[1]: i.split('\t')[0] for i in f
}
logger.info(f'Number of reference cell barcodes: {len(cell_barcodes):,}')
logger.info('Number of refernece feature barcodes: '
f'{len(feature_barcodes):,}')
if read1_coords:
logger.info('Read 1 coordinates to search: [' +
', '.join([str(i) for i in read1_coords]) + ')')
if read2_coords:
logger.info('Read 2 coordinates to search: [' +
', '.join([str(i) for i in read2_coords]) + ')')
logger.info(
f'Cell barcode maximum number of mismatches: {cb_num_mismatches}')
logger.info(
f'Feature barcode maximum number of mismatches: {fb_num_mismatches}')
logger.info(
f'Read 1 maximum number of N allowed: {cb_num_n_threshold}')
logger.info(
f'Read 2 maximum number of N allowed: {fb_num_n_threshold}'
)
if num_reads:
logger.info(f'Number of read pairs to analyze: {num_reads:,}')
if chunk_size > num_reads:
chunk_size = num_reads
else:
logger.info('Number of read pairs to analyze: all')
cell_barcodes_compiled_exact = compile_regex_ref_barcodes_exact(
barcodes=cell_barcodes
)
feature_barcodes_compiled_exact = compile_regex_ref_barcodes_exact(
barcodes=feature_barcodes.keys()
)
if cb_num_mismatches:
cell_barcodes_compiled_fuzzy = compile_regex_ref_barcodes_fuzzy(
barcodes=cell_barcodes,
num_mismatches=cb_num_mismatches
)
else:
cell_barcodes_compiled_fuzzy = None
if fb_num_mismatches:
feature_barcodes_compiled_fuzzy = compile_regex_ref_barcodes_fuzzy(
barcodes=feature_barcodes,
num_mismatches=fb_num_mismatches
)
else:
feature_barcodes_compiled_fuzzy = None
def get_sequence(read1_file, read2_file,
read1_coords=read1_coords, read2_coords=read2_coords):
"""Gets sequences."""
with dnaio.open(file1=read1_file,
file2=read2_file,
fileformat='fastq',
mode='r') as f:
for rec in f:
read1, read2 = rec
read1_seq = read1.sequence
read2_seq = read2.sequence
if read1_coords:
r1_start, r1_end = read1_coords
r1 = read1_seq[r1_start: min(r1_end, len(read1_seq))]
else:
r1 = read1_seq
if read2_coords:
r2_start, r2_end = read2_coords
r2 = read2_seq[r2_start: min(r2_end, len(read2_seq))]
else:
r2 = read2_seq
yield r1, r2, read1_seq, read2_seq
def _restore_orig_seq(x,
read1_coords=read1_coords,
read2_coords=read2_coords):
"""Formats matching output, restores original seqs and coordinates."""
"""
['TGATCTTAGAACACGT', 'TGATCTTAGAACACGT', '0:16', '2:0:0', 'GGGGGGGGGGGGGGGGAGGGGGCCGGAAAAGAACCCCGAGAGGCCAGCGCCAAACAAAAAAGAACAAAAAAGAGGAAAAAAAAAAAAAAA', 'no_match', 'NA', 'NA', 'TGATCTTAGAACACGTCAGGGTCCTGAA', 'GGGGGGGGGGGGGGGGAGGGGGCCGGAAAAGAACCCCGAGAGGCCAGCGCCAAACAAAAAAGAACAAAAAAGAGGAAAAAAAAAAAAAAA']
['TCTCAGCGTATAGTCC', 'TCTCAGCGTATAGTCC', '0:16', '2:0:0', 'AGCGGGCGCATGTTCCCGCTCAACTATACGAACGGCTTTAAGGCCGGTCCTAGCAACCTGAAGGCTTAGGACTATACGCTGAGACTGTCT', 'TGTTCCCGCTCAACT', '10:25', '0:0:0', 'TCTCAGCGTATAGTCCTAAGCCTTCAGG', 'AGCGGGCGCATGTTCCCGCTCAACTATACGAACGGCTTTAAGGCCGGTCCTAGCAACCTGAAGGCTTAGGACTATACGCTGAGACTGTCT']
['CGATCGGGTGTGCGCT', 'no_match', 'NA', 'NA', 'CGATCGGCAGTGCGCTCACCTATTAGCGGCTAAGGCGATCTTGAGAGAGCGCACACCCGATCGCTGTCTCTTATACACATCTGACGCTGC', 'NA', 'NA', 'NA', 'CGATCGGGTGTGCGCTCTCTCAAGATCG', 'CGATCGGCAGTGCGCTCACCTATTAGCGGCTAAGGCGATCTTGAGAGAGCGCACACCCGATCGCTGTCTCTTATACACATCTGACGCTGC']
['CAACAGTGTAACTAAG', 'CAACAGTGTAACTAAG', '0:16', '2:0:0', 'GGGCAATGTAGCTGCGCTTTCCATTCGAGGCCGGGATTTAAGGCCGGTCCTAGCAANNCGGCTACCCTCTTAGTTACACTGTNGCTGTCT', 'n_skipping', 'NA', 'NA', 'CAACAGTGTAACTAAGAGGGTAGCCGTA', 'GGGCAATGTAGCTGCGCTTTCCATTCGAGGCCGGGATTTAAGGCCGGTCCTAGCAANNCGGCTACCCTCTTAGTTACACTGTNGCTGTCT']
""" # noqa
# read1
if read1_coords:
r1_start, r1_end = read1_coords
if ':' in x[2]:
x[2] = ':'.join(
[str(int(i) + r1_start) for i in x[2].split(':')]
)
x[0] = x[-2][:r1_start].lower() + x[0] + x[-2][r1_end:].lower()
# read2
if read2_coords:
r2_start, r2_end = read2_coords
if ':' in x[6]:
x[6] = ':'.join(
[str(int(i) + r2_start) for i in x[6].split(':')]
)
x[4] = x[-1][:r2_start].lower() + x[4] + x[-1][r2_end:].lower()
if x[5] not in {'no_match', 'n_skipping', 'NA'}:
x[5] = feature_barcodes[x[5]] + '_' + x[5]
return '\t'.join(x[:-2])
if not num_threads:
num_threads = cpu_count()
logger.info(f'Number of threads: {num_threads}')
if num_threads > 1:
logger.info(f'Chunk size: {chunk_size:,}')
logger.info('Matching ...')
_reads = islice(
get_sequence(read1_file, read2_file),
0,
num_reads
)
read_counter = int()
if num_threads == 1:
for r1, r2, read1_seq, read2_seq in _reads:
read_counter += 1
if read_counter % chunk_size == 0:
logger.info(f'Read pairs processed: {read_counter:,}')
out = match_barcodes_paired(
read_seqs=(r1, r2, read1_seq, read2_seq),
cb_compiled_exact=cell_barcodes_compiled_exact,
fb_compiled_exact=feature_barcodes_compiled_exact,
cb_compiled_fuzzy=cell_barcodes_compiled_fuzzy,
fb_compiled_fuzzy=feature_barcodes_compiled_fuzzy,
cb_num_n_threshold=cb_num_n_threshold,
fb_num_n_threshold=fb_num_n_threshold
)
out = _restore_orig_seq(x=out,
read1_coords=read1_coords,
read2_coords=read2_coords)
yield out
else:
items = list(islice(_reads, chunk_size))
with Pool(processes=num_threads) as p:
while items:
read_counter += len(items)
outs = p.starmap(
match_barcodes_paired,
zip(items,
repeat(cell_barcodes_compiled_exact),
repeat(feature_barcodes_compiled_exact),
repeat(cell_barcodes_compiled_fuzzy),
repeat(feature_barcodes_compiled_fuzzy),
repeat(cb_num_n_threshold),
repeat(fb_num_n_threshold),
)
)
outs = [
_restore_orig_seq(x=i,
read1_coords=read1_coords,
read2_coords=read2_coords) for i in outs
]
logger.info(f'Read pairs processed: {read_counter:,}')
yield '\n'.join(outs)
items = list(islice(_reads, chunk_size))
