487 lines
23 KiB
Python
487 lines
23 KiB
Python
|
#!/usr/bin/python3
|
||
|
|
# -*- coding: UTF-8 -*-
|
||
|
|
|
||
|
|
import pandas as pd
|
||
|
|
from pandas import DataFrame
|
||
|
|
import numpy as np
|
||
|
|
import logging
|
||
|
|
import re
|
||
|
|
import sys
|
||
|
|
import os
|
||
|
|
import json
|
||
|
|
import glob
|
||
|
|
import openpyxl
|
||
|
|
from openpyxl import Workbook, load_workbook
|
||
|
|
from openpyxl.drawing.image import Image
|
||
|
|
|
||
|
|
if len(sys.argv) != 3:
|
||
|
|
print(" ".join(['usage:python', sys.argv[0], 'output_dir', 'name']))
|
||
|
|
sys.exit()
|
||
|
|
|
||
|
|
|
||
|
|
def snv_fusion_cnv(output_dir, name):
|
||
|
|
out_xlsx = "".join([output_dir, '/report/', name, '.check_new.xlsx'])
|
||
|
|
|
||
|
|
# genefunction
|
||
|
|
genefunction = {}
|
||
|
|
gf = open("/dataseq/jmdna/codes/reportbase/gene_function.txt", 'r', encoding='utf-8').readlines()
|
||
|
|
for line in gf[1:]:
|
||
|
|
gene = line.strip().split("\t")[0]
|
||
|
|
func = line.strip().split("\t")[1]
|
||
|
|
genefunction[gene.upper()] = func
|
||
|
|
genefunction['.'] = '.'
|
||
|
|
|
||
|
|
##drug_mechanism
|
||
|
|
drug_mechanism = {}
|
||
|
|
drug_fh = open("/dataseq/jmdna/codes/reportbase/target_drug.txt", 'r', encoding='utf-8').readlines()
|
||
|
|
for line in drug_fh[1:]:
|
||
|
|
disease = line.split("\t")[8]
|
||
|
|
mechanism = line.split("\t")[11]
|
||
|
|
drugs = line.split("\t")[0].split('|') + line.split("\t")[1].split('|')
|
||
|
|
if disease or mechanism:
|
||
|
|
for drug in drugs:
|
||
|
|
drug_mechanism[drug.upper()] = "\\\\".join([disease, mechanism]).strip()
|
||
|
|
|
||
|
|
'''
|
||
|
|
snvindel_sheet
|
||
|
|
'''
|
||
|
|
|
||
|
|
##input
|
||
|
|
filter_file = "".join([output_dir, '/report/', name, '.snp.indel.Somatic.annoall.hg19_multianno_filtered.txt'])
|
||
|
|
pos_file = "".join([output_dir, '/mutation/', name, '.snvindel.pos.dedup.txt'])
|
||
|
|
vus_file = "".join([output_dir, '/mutation/', name, '.snvindel.vus.txt'])
|
||
|
|
neg_file = "".join([output_dir, '/mutation/', name, '.snvindel.neg.txt'])
|
||
|
|
##filter_file
|
||
|
|
if os.path.getsize(filter_file) > 0:
|
||
|
|
snv = pd.read_table(filter_file, sep="\t")
|
||
|
|
cols = [index for index, row in snv[snv['可信'] == 0].iterrows()]
|
||
|
|
snv.drop(cols, inplace=True)
|
||
|
|
snv.insert(loc=24, column='ACMG_level', value=0)
|
||
|
|
snv.insert(loc=25, column='Deleterious', value=0)
|
||
|
|
snv.insert(loc=26, column='freq_high', value=0)
|
||
|
|
for index, row in snv.iterrows():
|
||
|
|
if re.search("Likely_pathogenic|drug", (row['CLNSIG']), re.I):
|
||
|
|
snv.loc[index, 'ACMG_level'] = '2'
|
||
|
|
elif re.search("pathogenic", (row['CLNSIG']), re.I) and not re.search("Conflicting", (row['CLNSIG']), re.I):
|
||
|
|
snv.loc[index, 'ACMG_level'] = '1'
|
||
|
|
else:
|
||
|
|
snv.loc[index, 'ACMG_level'] = '3'
|
||
|
|
snv.loc[index, "Deleterious"] = (
|
||
|
|
snv.loc[index, ['MutationTaster_pred', 'FATHMM_pred', 'MetaLR_pred']].tolist().count("D"))
|
||
|
|
snv.loc[index, "freq_high"] = ((snv.loc[
|
||
|
|
index, ['1000g2015aug_all', '1000g2015aug_eas', 'esp6500siv2_all', 'ExAC_nontcga_ALL',
|
||
|
|
'ExAC_nontcga_EAS', 'gnomAD_genome_ALL', 'gnomAD_genome_EAS']]).replace('.', '0')).max()
|
||
|
|
snv_1 = snv.iloc[:, list(range(14)) + [15, 17, 18, 20, 23, 24, 25, 26, 111, 112, 113]]
|
||
|
|
else:
|
||
|
|
snv_1 = pd.DataFrame(columns=[])
|
||
|
|
|
||
|
|
##pos_file
|
||
|
|
if os.path.getsize(pos_file) > 0:
|
||
|
|
pos = pd.read_table(pos_file, sep="\t")
|
||
|
|
pos = pos.iloc[:, [7, 10, 18, 23, 24, 25, 29, 30, 31, 32]]
|
||
|
|
pos_1 = pd.DataFrame(
|
||
|
|
columns=['AAChange.refGene', 'OKBSIG', 'AMP_evidence_level', 'AMP_mut_level', 'Indication', 'Drug',
|
||
|
|
'Response_Type', 'Evidence_Source', 'EfficacyEvidence', 'Drug_Detail', 'Gene_function',
|
||
|
|
'Drug_Category'])
|
||
|
|
pos = list(pos.groupby(['Gene.refGene', 'AAChange.refGene', 'fun_change']))
|
||
|
|
for i in pos:
|
||
|
|
for index, row in i[1].iterrows():
|
||
|
|
drugs = row['药物中文名'].replace(" + ", ",")
|
||
|
|
drugs = list(set(drugs.split(",")))
|
||
|
|
drug_mm = ''
|
||
|
|
for drug in drugs:
|
||
|
|
if drug.upper() in drug_mechanism.keys():
|
||
|
|
drug_mm += '[[' + drug + ']]' + drug_mechanism[drug.upper()]
|
||
|
|
i[1].loc[index, ['Drug_Detail']] = drug_mm
|
||
|
|
if row['标签'] == '非适应症':
|
||
|
|
row['证据等级'] = 'C'
|
||
|
|
if (re.search("敏感", row['Response_Type_C']) and row['证据等级'] == 'A'):
|
||
|
|
i[1].loc[index, ['Drug_Category']] = 'a'
|
||
|
|
elif re.search("敏感", row['Response_Type_C']) and row['证据等级'] == 'C':
|
||
|
|
i[1].loc[index, ['Drug_Category']] = 'b'
|
||
|
|
elif re.search("耐药", row['Response_Type_C']):
|
||
|
|
i[1].loc[index, ['Drug_Category']] = 'd'
|
||
|
|
else:
|
||
|
|
i[1].loc[index, ['Drug_Category']] = 'c'
|
||
|
|
i[1]['AMP_mut_level'] = i[1]['证据等级'].replace(['A', 'B', 'C', 'D'], ['I', 'I', 'II', 'II'])
|
||
|
|
pos_1.loc[len(pos_1)] = [i[0][1], i[0][2], '|'.join(list(i[1]['证据等级'])),
|
||
|
|
'|'.join(list(i[1]['AMP_mut_level'])), '|'.join(list(i[1]['疾病中文名'])),
|
||
|
|
'|'.join(list(i[1]['药物中文名'])), \
|
||
|
|
'|'.join(list(i[1]['Response_Type_C'])), '|'.join(list(i[1]['Evidence_Source_C'])),
|
||
|
|
'|'.join(list(i[1]['EfficacyEvidence'])), '|'.join(list(i[1]['Drug_Detail'])),
|
||
|
|
genefunction[i[0][0].upper()], '|'.join(list(i[1]['Drug_Category']))]
|
||
|
|
else:
|
||
|
|
pos_1 = pd.DataFrame(columns=[])
|
||
|
|
|
||
|
|
##vus_file
|
||
|
|
if os.path.getsize(vus_file) > 0:
|
||
|
|
vus = pd.read_table(vus_file, sep="\t")
|
||
|
|
vus_1 = vus.iloc[:, [9, 17]]
|
||
|
|
vus_1.insert(loc=2, column='AMP_mut_level', value='III')
|
||
|
|
vus_1 = vus_1.rename(columns={'fun_change': 'OKBSIG'})
|
||
|
|
else:
|
||
|
|
vus_1 = pd.DataFrame(columns=[])
|
||
|
|
|
||
|
|
##neg_file
|
||
|
|
if os.path.getsize(neg_file) > 0:
|
||
|
|
neg = pd.read_table(neg_file, sep="\t")
|
||
|
|
neg_1 = neg.iloc[:, [9, 17]]
|
||
|
|
neg_1.insert(loc=2, column='AMP_mut_level', value='IIII')
|
||
|
|
neg_1 = neg_1.rename(columns={'fun_change': 'OKBSIG'})
|
||
|
|
else:
|
||
|
|
neg_1 = pd.DataFrame(columns=[])
|
||
|
|
|
||
|
|
snvindel_sheet = pd.DataFrame(
|
||
|
|
columns=['可信', 'Chr', 'Start', 'End', 'Ref', 'Alt', 'AAChange.refGene', 'mutant_frequency', 'total_reads',
|
||
|
|
'mutant_reads', 'strand_bias', 'Otherinfo10', 'Func.refGene', 'Gene.refGene', 'ExonicFunc.refGene',
|
||
|
|
'avsnp150', 'cosmic91', 'CLNDN', 'CLNSIG', 'ACMG_level', 'Deleterious', 'freq_high', 'OKBSIG',
|
||
|
|
'AMP_evidence_level', 'AMP_mut_level', 'Indication', 'Drug', 'Response_Type', 'Evidence_Source',
|
||
|
|
'EfficacyEvidence', 'Drug_Detail', 'Gene_function', 'Drug_Category', 'Otherinfo11', 'Otherinfo12',
|
||
|
|
'Otherinfo13'])
|
||
|
|
pos_vus_neg = pd.concat([pos_1, vus_1, neg_1])
|
||
|
|
snv_pos_vus_neg = snv_1.merge(pos_vus_neg, how='left', on='AAChange.refGene')
|
||
|
|
snvindel_sheet = pd.concat([snvindel_sheet, snv_pos_vus_neg])
|
||
|
|
snvindel_sheet.rename(columns={"可信": "Validated"})
|
||
|
|
snvindel_sheet = snvindel_sheet.replace(np.nan, '.')
|
||
|
|
snvindel_sheet.rename(columns={"可信": "Validated"}, inplace=True)
|
||
|
|
|
||
|
|
'''
|
||
|
|
fusion_sheet
|
||
|
|
'''
|
||
|
|
|
||
|
|
fusion_pos_file = "".join([output_dir, '/fusion/', name, '.fusion.pos.dedup.txt'])
|
||
|
|
fusion_vus_file = "".join([output_dir, '/fusion/', name, '.fusion.vus.txt'])
|
||
|
|
if os.path.getsize(fusion_pos_file) > 0:
|
||
|
|
fusion_pos = pd.read_table(fusion_pos_file, sep="\t")
|
||
|
|
else:
|
||
|
|
fusion_pos = pd.DataFrame(columns=[])
|
||
|
|
|
||
|
|
if os.path.getsize(fusion_vus_file) > 0:
|
||
|
|
fusion_vus = pd.read_table(fusion_vus_file, sep="\t")
|
||
|
|
fusion_vus.insert(loc=0, column='可信', value=1)
|
||
|
|
else:
|
||
|
|
fusion_vus = pd.DataFrame(columns=[])
|
||
|
|
fusion_pos_vus = pd.concat([fusion_pos, fusion_vus])
|
||
|
|
fusion_sheet = pd.DataFrame(
|
||
|
|
columns=['Validated', 'CHROM1', 'POS1', 'CHROM2', 'POS2', 'GENE1', 'GENE2', 'FUSION', 'Support_reads(PE:SR)',
|
||
|
|
'Depth', 'FREQ1', 'FREQ2', 'OKBSIG', 'AMP_evidence_level', \
|
||
|
|
'AMP_mut_level', 'Indication', 'Drug', 'Response_Type', 'Evidence_Source', 'Efficacy_Evidence',
|
||
|
|
'Drug_Detail', 'Gene_function', 'Drug_Category', 'INFO', 'FORMAT', 'Sample'])
|
||
|
|
|
||
|
|
if not fusion_pos_vus.empty:
|
||
|
|
fusion_pos_vus = fusion_pos_vus.replace(np.nan, '.')
|
||
|
|
fusion = list(fusion_pos_vus.groupby(
|
||
|
|
['可信', '#CHROM', 'POS', 'CHROM2', 'POS2', 'GENE1', 'GENE2', 'FUSION', 'FREQ1', 'FREQ2', 'fun_change',
|
||
|
|
'INFO', 'FORMAT', name, 'Gene_Symbol']))
|
||
|
|
for i in fusion:
|
||
|
|
for index, row in i[1].iterrows():
|
||
|
|
drugs = row['药物中文名'].replace(" + ", ",")
|
||
|
|
drugs = list(set(drugs.split(",")))
|
||
|
|
drug_mm = ''
|
||
|
|
for drug in drugs:
|
||
|
|
if drug.upper() in drug_mechanism.keys():
|
||
|
|
drug_mm += '[[' + drug + ']]' + drug_mechanism[drug.upper()]
|
||
|
|
i[1].loc[index, ['Drug_Detail']] = drug_mm
|
||
|
|
if row['标签'] == '非适应症':
|
||
|
|
row['证据等级'] = 'C'
|
||
|
|
if (re.search("敏感", row['Response_Type_C']) and row['证据等级'] == 'A'):
|
||
|
|
i[1].loc[index, ['Drug_Category']] = 'a'
|
||
|
|
elif re.search("敏感", row['Response_Type_C']) and row['证据等级'] == 'C':
|
||
|
|
i[1].loc[index, ['Drug_Category']] = 'b'
|
||
|
|
elif re.search("耐药", row['Response_Type_C']):
|
||
|
|
i[1].loc[index, ['Drug_Category']] = 'd'
|
||
|
|
elif row['Response_Type_C'] == '.':
|
||
|
|
i[1].loc[index, ['Drug_Category']] = '.'
|
||
|
|
else:
|
||
|
|
i[1].loc[index, ['Drug_Category']] = 'c'
|
||
|
|
i[1]['AMP_mut_level'] = i[1]['证据等级'].replace(['A', 'B', 'C', 'D'], ['I', 'I', 'II', 'II'])
|
||
|
|
fusion_sheet.loc[len(fusion_sheet)] = list(i[0][0:8]) + [i[0][13].split(":")[1],
|
||
|
|
i[0][13].split(":")[7]] + list(i[0][8:11]) + [
|
||
|
|
'|'.join(list(i[1]['证据等级'])),
|
||
|
|
'|'.join(list(i[1]['AMP_mut_level'])), \
|
||
|
|
'|'.join(list(i[1]['疾病中文名'])), '|'.join(list(i[1]['药物中文名'])),
|
||
|
|
'|'.join(list(i[1]['Response_Type_C'])),
|
||
|
|
'|'.join(list(i[1]['Evidence_Source_C'])),
|
||
|
|
'|'.join(list(i[1]['EfficacyEvidence'])), \
|
||
|
|
'|'.join(list(i[1]['Drug_Detail'])),
|
||
|
|
genefunction[i[0][14].upper()],
|
||
|
|
'|'.join(list(i[1]['Drug_Category']))] + list(i[0][11:14])
|
||
|
|
fusion_sheet = fusion_sheet.replace(np.nan, '.')
|
||
|
|
|
||
|
|
'''
|
||
|
|
cnv_sheet
|
||
|
|
'''
|
||
|
|
|
||
|
|
cnv_pos_file = "/home/jm001/test/reference_standard/lung85gene/Tissue/BKDL202603539-1a/cnvkit/BKDL202603539-1a.cnv.pos.dedup.txt"
|
||
|
|
cnv_sheet = pd.DataFrame(
|
||
|
|
columns=['Validated', 'Chromosome', 'Start', 'End', 'Gene', 'Depth', 'Probes', 'Copy_number', 'OKBSIG',
|
||
|
|
'Gene_Symbol', 'AMP_evidence_level', 'AMP_mut_level', \
|
||
|
|
'Indication', 'Drug', 'Response_Type', 'Evidence_Source', 'Efficacy_Evidence', 'Drug_Detail',
|
||
|
|
'Gene_Function', 'Drug_Category'])
|
||
|
|
if os.path.getsize(cnv_pos_file) > 0:
|
||
|
|
cnv_pos = pd.read_table(cnv_pos_file, sep="\t")
|
||
|
|
cnv = list(cnv_pos.groupby(
|
||
|
|
['可信', 'chromosome', 'start', 'end', 'gene', 'depth', 'probes', 'cn', 'fun_change', 'Gene_Symbol']))
|
||
|
|
for i in cnv:
|
||
|
|
for index, row in i[1].iterrows():
|
||
|
|
drugs = row['药物中文名'].replace(" + ", ",")
|
||
|
|
drugs = list(set(drugs.split(",")))
|
||
|
|
drug_mm = ''
|
||
|
|
for drug in drugs:
|
||
|
|
if drug.upper() in drug_mechanism.keys():
|
||
|
|
drug_mm += '[[' + drug + ']]' + drug_mechanism[drug.upper()]
|
||
|
|
i[1].loc[index, ['Drug_Detail']] = drug_mm
|
||
|
|
if row['标签'] == '非适应症':
|
||
|
|
row['证据等级'] = 'C'
|
||
|
|
if (re.search("敏感", row['Response_Type_C']) and row['证据等级'] == 'A'):
|
||
|
|
i[1].loc[index, ['Drug_Category']] = 'a'
|
||
|
|
elif re.search("敏感", row['Response_Type_C']) and row['证据等级'] == 'C':
|
||
|
|
i[1].loc[index, ['Drug_Category']] = 'b'
|
||
|
|
elif re.search("耐药", row['Response_Type_C']):
|
||
|
|
i[1].loc[index, ['Drug_Category']] = 'd'
|
||
|
|
elif row['Response_Type_C'] == '.':
|
||
|
|
i[1].loc[index, ['Drug_Category']] = '.'
|
||
|
|
else:
|
||
|
|
i[1].loc[index, ['Drug_Category']] = 'c'
|
||
|
|
i[1]['AMP_mut_level'] = i[1]['证据等级'].replace(['A', 'B', 'C', 'D'], ['I', 'I', 'II', 'II'])
|
||
|
|
cnv_sheet.loc[len(cnv_sheet)] = list(i[0][0:10]) + ['|'.join(list(i[1]['证据等级'])),
|
||
|
|
'|'.join(list(i[1]['AMP_mut_level'])), \
|
||
|
|
'|'.join(list(i[1]['疾病中文名'])),
|
||
|
|
'|'.join(list(i[1]['药物中文名'])),
|
||
|
|
'|'.join(list(i[1]['Response_Type_C'])),
|
||
|
|
'|'.join(list(i[1]['Evidence_Source_C'])),
|
||
|
|
'|'.join(list(i[1]['EfficacyEvidence'])), \
|
||
|
|
'|'.join(list(i[1]['Drug_Detail'])),
|
||
|
|
genefunction[i[0][9].upper()],
|
||
|
|
'|'.join(list(i[1]['Drug_Category']))]
|
||
|
|
else:
|
||
|
|
cnv_pos = pd.DataFrame(columns=[])
|
||
|
|
|
||
|
|
with pd.ExcelWriter(out_xlsx) as writer:
|
||
|
|
snvindel_sheet.to_excel(writer, sheet_name="snvindel", index=False)
|
||
|
|
fusion_sheet.to_excel(writer, sheet_name="fusion", index=False)
|
||
|
|
cnv_sheet.to_excel(writer, sheet_name="cnv", index=False)
|
||
|
|
|
||
|
|
##加入cnvkit/*.cnv.png
|
||
|
|
wb = openpyxl.load_workbook(filename=out_xlsx)
|
||
|
|
ws = wb['cnv']
|
||
|
|
mr = ws.max_row
|
||
|
|
cell = 'C' + str(mr + 4)
|
||
|
|
cnv_pic = "".join([output_dir, '/cnvkit/', name, '.cnv.png'])
|
||
|
|
image = Image(cnv_pic)
|
||
|
|
ws.add_image(image, cell)
|
||
|
|
wb.save(out_xlsx)
|
||
|
|
|
||
|
|
|
||
|
|
class PostProcess:
|
||
|
|
"""
|
||
|
|
excel处理
|
||
|
|
"""
|
||
|
|
|
||
|
|
def __init__(self, path, outpath):
|
||
|
|
self.path = path
|
||
|
|
self.outpath = outpath
|
||
|
|
self.neeecol = self.need_col()
|
||
|
|
|
||
|
|
def need_col(self):
|
||
|
|
"""
|
||
|
|
读取所需列
|
||
|
|
"""
|
||
|
|
path = os.path.join(os.path.dirname(__file__), 'columns.csv')
|
||
|
|
cols = pd.read_csv(path)
|
||
|
|
cols = cols.fillna('')
|
||
|
|
cols_record = cols.to_dict('list')
|
||
|
|
for sheet in cols_record:
|
||
|
|
cols_record[sheet] = [x for x in cols_record[sheet] if x]
|
||
|
|
return cols_record
|
||
|
|
|
||
|
|
def msi(self):
|
||
|
|
"""
|
||
|
|
Process msi result files
|
||
|
|
"""
|
||
|
|
msi_files = glob.glob(os.path.join(self.path, 'MSI', '*.msi'))
|
||
|
|
msi_res = dict()
|
||
|
|
if msi_files:
|
||
|
|
df = pd.read_csv(msi_files[0], sep='\t')
|
||
|
|
res = df.to_dict('records')[0]
|
||
|
|
msi_res['msi_count'] = res['Total_Number_of_Sites']
|
||
|
|
msi_res['msi_value'] = res['%']
|
||
|
|
if msi_res['msi_value'] >= 0.3:
|
||
|
|
msi_res['msi_result'] = 'MSI-H'
|
||
|
|
msi_res['msi_predict'] = '对免疫检查点抑制剂可能敏感'
|
||
|
|
else:
|
||
|
|
msi_res['msi_result'] = 'MSS'
|
||
|
|
msi_res['msi_predict'] = '对免疫检查点抑制剂可能不敏感'
|
||
|
|
return [msi_res]
|
||
|
|
|
||
|
|
def chemo(self):
|
||
|
|
"""
|
||
|
|
化疗
|
||
|
|
"""
|
||
|
|
chemo_files = glob.glob(os.path.join(self.path, 'chemo', '*chemo.res.txt'))
|
||
|
|
chemo_res = []
|
||
|
|
if chemo_files:
|
||
|
|
df = pd.read_csv(chemo_files[0], sep='\t')
|
||
|
|
df = df.fillna('.')
|
||
|
|
chemo_res = df.to_dict('records')
|
||
|
|
return chemo_res
|
||
|
|
|
||
|
|
def heredity(self):
|
||
|
|
"""
|
||
|
|
遗传
|
||
|
|
"""
|
||
|
|
heredi_files = glob.glob(os.path.join(self.path, 'mutation', '*Germline*filtered.txt'))
|
||
|
|
heredires = []
|
||
|
|
if heredi_files:
|
||
|
|
df = pd.read_csv(heredi_files[0], sep='\t')
|
||
|
|
df = df.fillna('.')
|
||
|
|
tmdf1 = df[
|
||
|
|
['1000g2015aug_all', '1000g2015aug_eas', 'esp6500siv2_all', 'ExAC_nontcga_ALL', 'ExAC_nontcga_EAS',
|
||
|
|
'gnomAD_genome_ALL', 'gnomAD_genome_EAS']].replace('.', 0).applymap(lambda x: eval(str(x)))
|
||
|
|
df['freq_high'] = tmdf1.max(axis=1)
|
||
|
|
tmdf2 = df[['MutationTaster_pred', 'FATHMM_pred', 'MetaLR_pred']]
|
||
|
|
df['Deleterious'] = tmdf2.apply(lambda x: x.tolist().count('D'), axis=1)
|
||
|
|
df_need = df[self.neeecol.get('HCS', [])]
|
||
|
|
try:
|
||
|
|
heredires = df_need.to_dict('records')
|
||
|
|
except KeyError as e:
|
||
|
|
raise UserWarning('表头设置和配置文件不对应', e)
|
||
|
|
return heredires
|
||
|
|
|
||
|
|
def MMR(self):
|
||
|
|
"""
|
||
|
|
MMR
|
||
|
|
"""
|
||
|
|
mmr_files = glob.glob(os.path.join(self.path, 'MMR', '*mmr.pre.txt'))
|
||
|
|
mmr = []
|
||
|
|
if mmr_files:
|
||
|
|
df = pd.read_csv(mmr_files[0], sep='\t')
|
||
|
|
df = df.fillna('.')
|
||
|
|
tmdf1 = df[
|
||
|
|
['1000g2015aug_all', '1000g2015aug_eas', 'esp6500siv2_all', 'ExAC_nontcga_ALL', 'ExAC_nontcga_EAS',
|
||
|
|
'gnomAD_genome_ALL', 'gnomAD_genome_EAS']].replace('.', 0).applymap(lambda x: eval(str(x)))
|
||
|
|
df['freq_high'] = tmdf1.max(axis=1)
|
||
|
|
tmdf2 = df[['MutationTaster_pred', 'FATHMM_pred', 'MetaLR_pred']]
|
||
|
|
df['Deleterious'] = tmdf2.apply(lambda x: x.tolist().count('D'), axis=1)
|
||
|
|
df_need = df[self.neeecol.get('HCS', [])]
|
||
|
|
try:
|
||
|
|
mmr = df_need.to_dict('records')
|
||
|
|
except KeyError as e:
|
||
|
|
raise UserWarning('表头设置和配置文件不对应', e)
|
||
|
|
return mmr
|
||
|
|
|
||
|
|
def hotspot(self):
|
||
|
|
hotspot_files = glob.glob(
|
||
|
|
os.path.join(self.path, 'mutation', 'hotspot', '*hotspot.snp.indel.filter.anno.hg19_multianno.txt'))
|
||
|
|
if hotspot_files:
|
||
|
|
return self.txt_2_excel(hotspot_files[0])
|
||
|
|
|
||
|
|
def splicing(self):
|
||
|
|
splicing_files = glob.glob(
|
||
|
|
os.path.join(self.path, 'mutation', '*.target.splicing.txt'))
|
||
|
|
if splicing_files:
|
||
|
|
return self.txt_2_excel(splicing_files[0])
|
||
|
|
|
||
|
|
def indication(self):
|
||
|
|
indication_files = glob.glob(
|
||
|
|
os.path.join(self.path, 'mutation', '*indication.txt'))
|
||
|
|
if indication_files:
|
||
|
|
return self.txt_2_excel(indication_files[0])
|
||
|
|
|
||
|
|
def longindel(self):
|
||
|
|
longindel_files = glob.glob(
|
||
|
|
os.path.join(self.path, 'fusion', '*.longindel.pos.txt'))
|
||
|
|
if longindel_files:
|
||
|
|
return self.txt_2_excel(longindel_files[0])
|
||
|
|
|
||
|
|
def cms(self):
|
||
|
|
"""
|
||
|
|
样本信息
|
||
|
|
"""
|
||
|
|
cms_files = glob.glob(os.path.join(self.path, 'qc', '*_post.json'))
|
||
|
|
cms_info_need = []
|
||
|
|
if cms_files:
|
||
|
|
file_read = open(cms_files[0], 'r')
|
||
|
|
cms_info = json.load(file_read)['data']
|
||
|
|
file_read.close()
|
||
|
|
df = pd.DataFrame(cms_info)
|
||
|
|
df_need = df[self.neeecol.get('sample_info', [])]
|
||
|
|
try:
|
||
|
|
cms_info_need = df_need.to_dict('records')
|
||
|
|
except KeyError as e:
|
||
|
|
raise UserWarning('表头设置和配置文件不对应', e)
|
||
|
|
return cms_info_need
|
||
|
|
|
||
|
|
def qc(self):
|
||
|
|
qc_files = glob.glob(os.path.join(self.path, 'qc', '*_post.json'))
|
||
|
|
qc_res = []
|
||
|
|
if qc_files:
|
||
|
|
df = pd.read_csv(qc_files[0], sep='\t', header=None)
|
||
|
|
df = df.set_index(0).T
|
||
|
|
qc_res = df.to_dict('records')
|
||
|
|
return qc_res
|
||
|
|
|
||
|
|
#
|
||
|
|
# def snv(self):
|
||
|
|
# # filter file
|
||
|
|
# filter_files = glob.glob(os.path.join(self.path, 'report', '*snp.indel.Somatic.annoall.hg19_multianno_filtered.txt'))
|
||
|
|
# if filter_files:
|
||
|
|
# snv = pd.read_csv(filter_files[0], sep="\t")
|
||
|
|
# def sign_drug_Category(x):
|
||
|
|
# if '敏感' in x['Response_Type_C'] and x['证据等级'] == 'A':
|
||
|
|
# return 'a'
|
||
|
|
# elif '敏感' in x['Response_Type_C'] and x['证据等级'] == 'C':
|
||
|
|
# return 'b'
|
||
|
|
# elif '耐药' in x['Response_Type_C']:
|
||
|
|
# return 'd'
|
||
|
|
# else:
|
||
|
|
# return 'c'
|
||
|
|
# # pos_file 处理
|
||
|
|
# pos_files = glob.glob(os.path.join(self.path, 'mutation', '*snvindel.pos.txt'))
|
||
|
|
# if pos_files:
|
||
|
|
# pos = pd.read_csv(pos_files[0], sep='\t')
|
||
|
|
# pos['证据等级'] = pos.apply(lambda x: 'C' if x['标签'] == '非适应症' else x['证据等级'], axis=1)
|
||
|
|
# pos['Drug_Category'] = pos.apply(sign_drug_Category, axis=1)
|
||
|
|
# pos['AMP_mut_level'] = pos['证据等级'].replace(['A', 'B', 'C', 'D'], ['I', 'I', 'II', 'II'])
|
||
|
|
# agg_list = ['证据等级', 'AMP_mut_level', '疾病中文名', '药物中文名', '证据等级', 'Response_Type_C', 'Evidence_Source_C',
|
||
|
|
# 'EfficacyEvidence', 'Drug_Category']
|
||
|
|
# agg_dict = {column: ','.join for column in agg_list}
|
||
|
|
# pos_group =pos.groupby(['Gene.refGene','AAChange.refGene','fun_change']).agg(agg_dict, axis=1)
|
||
|
|
|
||
|
|
def txt_2_excel(self, path):
|
||
|
|
try:
|
||
|
|
df = pd.read_csv(path, sep='\t')
|
||
|
|
except pd.errors.EmptyDataError:
|
||
|
|
return []
|
||
|
|
return df.to_dict('records')
|
||
|
|
|
||
|
|
def collect(self):
|
||
|
|
writer = pd.ExcelWriter(self.outpath, mode='a', engine='openpyxl')
|
||
|
|
sheet = {
|
||
|
|
'MSI': self.msi(),
|
||
|
|
'chemo': self.chemo(),
|
||
|
|
'HCS': self.heredity(),
|
||
|
|
'sample_info': self.cms(),
|
||
|
|
'MMR': self.MMR(),
|
||
|
|
'hotspot': self.hotspot(),
|
||
|
|
'MET': self.splicing(),
|
||
|
|
'indication': self.indication(),
|
||
|
|
'longindel': self.longindel(),
|
||
|
|
'qc': self.qc()
|
||
|
|
}
|
||
|
|
# 遍历CSV文件列表
|
||
|
|
for sheet_name in sheet:
|
||
|
|
# 读取CSV文件为DataFrame
|
||
|
|
df = pd.DataFrame(sheet[sheet_name])
|
||
|
|
df.to_excel(writer, sheet_name=sheet_name, index=False)
|
||
|
|
# 保存并关闭Excel写入器
|
||
|
|
writer.close()
|
||
|
|
|
||
|
|
|
||
|
|
if __name__ == '__main__':
|
||
|
|
snv_fusion_cnv(sys.argv[1], sys.argv[2])
|
||
|
|
# 未加日志，未添加路径
|
||
|
|
out_xlsx = "".join([sys.argv[1], '/report/', sys.argv[2], '.check_new.xlsx'])
|
||
|
|
postprocess = PostProcess(sys.argv[1], out_xlsx)
|
||
|
|
postprocess.collect()
|