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sciRNAseq_count.py
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sciRNAseq_count.py
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# This script take as input a gtf file, a sam file input folder, a sample ID file, and the number of cores, then run
# the count exons and genes in parallel, and then output the report file and output file into the output file
#converted to python3 by ChatGPT
import itertools
import collections
import numpy as np
import pandas as pd
from multiprocessing import Pool
from multiprocessing import *
import HTSeq
import sys
from functools import partial
import logging
def sciRNAseq_count(sample, input_folder, exons, genes, gene_end, gene_annotat, sample_ID):
input_sam = f"{input_folder}/{sample}.sam"
report = f"{input_folder}/{sample}.report"
count_output = f"{input_folder}/{sample}.count"
counts = collections.Counter()
sam_file = input_sam
almnt_file = HTSeq.SAM_Reader(sam_file)
sam_name = sample
cell_ID = sample_ID.index(sample) + 1
perfect_inter_exon = 0
nearest_inter_exon = 0
perfect_combine_exon = 0
nearest_combine_exon = 0
perfect_inter_gene = 0
nearest_inter_gene = 0
perfect_combine_gene = 0
nearest_combine_gene = 0
print(f"Start read the input file: {sam_file}....")
for alnmt in almnt_file:
if not alnmt.aligned:
counts["_unmapped"] += 1
continue
if alnmt.iv.chrom not in genes.chrom_vectors:
counts["_unmapped"] += 1
continue
gene_id_intersect = set()
gene_id_combine = set()
inter_count = 0
for cigop in alnmt.cigar:
if cigop.type != "M":
continue
for iv,val in exons[cigop.ref_iv].steps():
gene_id_combine |= val
if inter_count == 0:
gene_id_intersect |= val
inter_count += 1
else:
gene_id_intersect &= val
if len(gene_id_intersect) == 1:
gene_id = list(gene_id_intersect)[0]
counts[gene_id] += 1
perfect_inter_exon += 1
elif len(gene_id_intersect) > 1:
gene_id = find_nearest_gene(alnmt.iv.end_d, gene_id_intersect, gene_end)
counts[gene_id] += 1
nearest_inter_exon += 1
else:
if len(gene_id_combine) == 1:
gene_id = list(gene_id_combine)[0]
counts[gene_id] += 1
perfect_combine_exon += 1
elif len(gene_id_combine) > 1:
gene_id = find_nearest_gene(alnmt.iv.end_d, gene_id_combine, gene_end)
counts[gene_id] += 1
nearest_combine_exon += 1
else:
gene_id_intersect = set()
gene_id_combine = set()
inter_count = 0
for cigop in alnmt.cigar:
if cigop.type != "M":
continue
for iv,val in genes[cigop.ref_iv].steps():
gene_id_combine |= val
if inter_count == 0:
gene_id_intersect |= val
inter_count += 1
else:
gene_id_intersect &= val
if len(gene_id_intersect) == 1:
gene_id = list(gene_id_intersect)[0] + "_intron"
counts[gene_id] += 1
perfect_inter_gene += 1
elif len(gene_id_intersect) > 1:
gene_id = find_nearest_gene(alnmt.iv.end_d, gene_id_intersect, gene_end) + "_intron"
counts[gene_id] += 1
nearest_inter_gene += 1
else:
if len(gene_id_combine) == 1:
gene_id = list(gene_id_combine)[0] + "_intron"
counts[gene_id] += 1
perfect_combine_gene += 1
elif len(gene_id_combine) > 1:
gene_id = find_nearest_gene(alnmt.iv.end_d, gene_id_combine, gene_end) + "_intron"
counts[gene_id] += 1
nearest_combine_gene += 1
else:
counts["_no_feature"] += 1
print("File name: ", sam_file)
print("1: Perfect intersect exon match: ", perfect_inter_exon)
print("2: Nearest intersect exon match: ", nearest_inter_exon)
print("3: Perfect combine exon match: ", perfect_combine_exon)
print("4: Nearest combine exon match: ", nearest_combine_exon)
print("5: Perfect intersect gene match: ", perfect_inter_gene)
print("6: Nearest intersect gene match: ", nearest_inter_gene)
print("7: Perfect combine gene match: ", perfect_combine_gene)
print("8: Nearest combine gene match: ", nearest_combine_gene)
print("9: ambiguous match for exons: ", counts["_ambiguous"])
print("10: ambiguous match for genes: ", counts["_ambiguous_intron"])
print("11: No match: ", counts["_no_feature"])
print("Sam file analysis finished~")
with open(report, 'w') as report:
report.write(f"1,{cell_ID},{perfect_inter_exon}\n")
report.write(f"2,{cell_ID},{nearest_inter_exon}\n")
report.write(f"3,{cell_ID},{perfect_combine_exon}\n")
report.write(f"4,{cell_ID},{nearest_combine_exon}\n")
report.write(f"5,{cell_ID},{perfect_inter_gene}\n")
report.write(f"6,{cell_ID},{nearest_inter_gene}\n")
report.write(f"7,{cell_ID},{perfect_combine_gene}\n")
report.write(f"8,{cell_ID},{nearest_combine_gene}\n")
report.write(f"9,{cell_ID},{counts['_ambiguous']}\n")
report.write(f"10,{cell_ID},{counts['_ambiguous_intron']}\n")
report.write(f"11,{cell_ID},{counts['_no_feature']}\n")
with open(count_output, 'w') as count_output:
for gene in counts:
if (gene in ["_unmapped", "_ambiguous", "_ambiguous_intron", "_no_feature"]):
continue
else:
line = f"{gene_annotat.loc[gene,4]},{cell_ID},{counts[gene]}\n"
count_output.write(line)
return 0
def find_nearest_gene(al_end, gene_id_intersect, gene_end):
gene_id_end = {}
for gene in gene_id_intersect:
if gene in gene_end:
gene_id_end[gene] = (abs(np.array(list(gene_end[gene])) - al_end)).min()
else:
print("****************Found one gene without transcript annotation*****************", "Gene name: ", gene)
gene_end_min = np.min(list(gene_id_end.values()))
count = 0
for gene in gene_id_end:
if (gene_id_end[gene] < gene_end_min + 100):
count += 1
gene_id = gene
if count > 1:
gene_id = "_ambiguous"
return gene_id
def sciRNA_count_parallel(gtf_file, input_folder, sample_ID, core_number):
gtf_file = HTSeq.GFF_Reader(gtf_file, end_included=True)
gene_annotat_file = f"{input_folder}/gene_name_annotate.txt"
cell_annotat_file = f"{input_folder}/cell_annotate.txt"
report_annotate_file = f"{input_folder}/report_annotate.txt"
gene_annotat = open(gene_annotat_file, "w")
cell_annotat = open(cell_annotat_file, "w")
report_annotate = open(report_annotate_file, "w")
exons = HTSeq.GenomicArrayOfSets( "auto", stranded=True )
genes = HTSeq.GenomicArrayOfSets( "auto", stranded=True )
gene_end = {}
exon_n = 0
gene_n = 0
transcript_n = 0
gene_count = 0
print("Start generating exon genomic arrays....")
print("Start generating gene genomic arrays....")
print("Start calculating transcript end of genes....")
for feature in gtf_file:
if feature.type == "exon":
exon_n += 1
exons[ feature.iv ] += feature.attr["gene_id"]
elif feature.type == "gene":
gene_n +=1
genes[ feature.iv ] += feature.attr["gene_id"]
gene_count += 1
message = f"{feature.attr['gene_id']},{feature.attr['gene_type']},exon,{feature.attr['gene_name']},{gene_count}\n"
gene_annotat.write(message)
gene_count += 1
message = f"{feature.attr['gene_id']}_intron,{feature.attr['gene_type']},intron,{feature.attr['gene_name']}_intron,{gene_count}\n"
gene_annotat.write(message)
elif feature.type == "transcript":
transcript_n += 1
if feature.attr["gene_id"] in gene_end.keys():
gene_end[ feature.attr["gene_id"] ].add(feature.iv.end_d)
else:
gene_end[ feature.attr["gene_id"] ] = set()
gene_end[ feature.attr["gene_id"] ].add(feature.iv.end_d)
print("Detected gene number: ", gene_n)
print("Detected transcript number: ", transcript_n)
print("Detected exon number: ", exon_n)
gene_annotat.close()
gene_annotat = pd.read_csv(gene_annotat_file, header=None)
gene_annotat.index = gene_annotat[0]
sample_ID = list(pd.read_csv(sample_ID, header=None)[0])
cell_count = 0
for i in sample_ID:
cell_count += 1
message = f"{i},{cell_count}\n"
cell_annotat.write(message)
cell_annotat.close()
report_annotate.write("1, Perfect intersect exon match\n")
report_annotate.write("2, Nearest intersect exon match\n")
report_annotate.write("3, Perfect combine exon match\n")
report_annotate.write("4, Nearest combine exon match\n")
report_annotate.write("5, Perfect intersect gene match\n")
report_annotate.write("6, Nearest intersect gene match\n")
report_annotate.write("7, Perfect combine gene match\n")
report_annotate.write("8, Nearest combine gene match\n")
report_annotate.write("9, ambiguous match for exons\n")
report_annotate.write("10, ambiguous match for genes\n")
report_annotate.write("11, No match\n")
report_annotate.close()
p = Pool(processes = int(core_number))
func = partial(sciRNAseq_count, input_folder=input_folder, exons=exons, genes=genes, gene_end=gene_end, gene_annotat=gene_annotat, sample_ID=sample_ID)
result = p.map(func, sample_ID)
p.close()
p.join()
print("All analysis done~")
if __name__ == "__main__":
gtf_file = sys.argv[1]
input_folder = sys.argv[2]
sample_ID = sys.argv[3]
core_number = sys.argv[4]
sciRNA_count_parallel(gtf_file, input_folder, sample_ID, core_number)