# kallisto.py
import numpy as np
import pandas as pd
import scipy.io
from pathlib import Path
from fba.utils import (
open_by_suffix,
get_binary_path,
get_logger,
run_executable
)
logger = get_logger(logger_name=__name__)
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
def main():
args = parse_args()
logger = get_logger(logger_name=__name__)
banner = """
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"""
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'
)