bc_umi_pipeline_10x_spatial.py

import argparse
from multiprocessing import Pool
from matplotlib.backends.backend_pdf import PdfPages
import bc_umi_utils
import os

parser = argparse.ArgumentParser()
parser.add_argument("-c", "--cores", type=str)
parser.add_argument("-i", "--indir", type=str)
parser.add_argument("-s", "--sample", type=str)
parser.add_argument("-ma", "--max_anchors", type=int)
parser.add_argument("-mt", "--max_targets", type=int)
parser.add_argument("-r1", "--read1_struct", type=str)
parser.add_argument("-r2", "--read2_struct", type=str)
parser.add_argument("-l", "--limit", default=False, action="store_true")

args = parser.parse_args()

cores = args.cores
indir = args.indir
sample = args.sample
max_anchors = args.max_anchors
max_targets = args.max_targets
read1_struct = args.read1_struct
read2_struct = args.read2_struct
limit = args.limit

######################################################

bc_umi_utils.split_fastq_by_lines(indir, sample, 4e7)

######################################################

parts = bc_umi_utils.find_sub_fastq_parts(indir, sample)
args = [(indir, sample, part, limit, read1_struct, read2_struct) for part in parts]

pool = Pool(int(cores))
results = pool.starmap(bc_umi_utils.extract_bc_umi_dict, args)
pool.close()
pool.join()

######################################################

bc_umi_utils.aggregate_dicts(indir, sample, "anchors")
bc_umi_utils.aggregate_dicts(indir, sample, "targets")
bc_umi_utils.aggregate_stat_dicts(indir, sample, "adapter_edits")

######################################################

qc_pdf_file = f"{indir}/{sample}/{sample}_QC.pdf"

if os.path.isfile(qc_pdf_file):
    print(qc_pdf_file, " exists, skip")
else:
    qc_pdfs = PdfPages(qc_pdf_file)
    bc_umi_utils.whitelist_rankplot(indir, sample, "anchors", qc_pdfs, max_anchors)
    bc_umi_utils.whitelist_rankplot(indir, sample, "targets", qc_pdfs, max_targets)
    qc_pdfs.close()
"""
######################################################

args = [(indir, sample, part, limit) for part in parts]

pool = Pool(int(cores))
results = pool.starmap(bc_umi_utils.extract_quad_dict, args)
pool.close()
pool.join()

######################################################

# bc_umi_utils.save_barcode_batch_json(indir,sample)
bc_umi_utils.aggregate_barcode_batches(indir, sample)

######################################################

batches = sorted(
    [
        f.split("_")[-2]
        for f in os.listdir(f"{indir}/{sample}/split/")
        if "batch" in f and "part" not in f
    ]
)
len_batches = len(batches)
args = [(indir, sample, i) for i in range(1, len_batches + 1)]
[print(a) for a in args]

pool = Pool(int(cores))
results = pool.starmap(bc_umi_utils.make_count_sparse_mtx_batch, args)
pool.close()
pool.join()

######################################################

"""