115 lines
5.0 KiB
Python
Executable File
115 lines
5.0 KiB
Python
Executable File
#!/usr/bin/python3
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# -*- coding: UTF-8 -*-
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import os
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import re
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import sys
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import pandas as pd
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if len(sys.argv) != 3:
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print(" ".join(['usage:python3', sys.argv[0], 'output_dir', 'name']))
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sys.exit()
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output_dir = sys.argv[1]
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name = sys.argv[2]
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snv_file = os.path.join(output_dir, 'mutation', f'{name}.somatic.hg19_multianno.filter.sum.pos.txt')
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snv_file_new = os.path.join(output_dir, 'mutation', f'{name}.somatic.hg19_multianno.filter.sum.pos.dedup.txt')
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fusion_file = os.path.join(output_dir, 'fusion', f'{name}.fusion.hg19_multianno.filter.fusion.pos.txt')
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fusion_file_new = os.path.join(output_dir, 'fusion', f'{name}.fusion.hg19_multianno.filter.fusion.pos.dedup.txt')
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cnv_file = os.path.join(output_dir, 'cnv', f'{name}.rmdup.cns.filter.pos.txt')
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cnv_file_new = os.path.join(output_dir, 'cnvkit', f'{name}.rmdup.cns.filter.pos.dedup.txt')
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# gm_snv_file = os.path.join(output_dir, '/mutation/', name, '.snvindel.Germline.pos.txt')
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# gm_snv_file_new = os.path.join(output_dir, '/mutation/', name, '.snvindel.Germline.pos.dedup.txt')
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open(snv_file_new, "w")
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open(fusion_file_new, "w")
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open(cnv_file_new, "w")
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# open(gm_snv_file_new, "w")
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##Evidence_Source_C及标签排序
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df_mapping_1 = pd.DataFrame({
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'Evidence_Source_C': ['FDA', 'NMPA', 'NCCN', '临床III期', '临床II期', '临床I期', '临床试验', '回顾性研究', '个案', '临床前研究'],
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})
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sort_mapping_1 = df_mapping_1.reset_index().set_index('Evidence_Source_C')
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df_mapping_2 = pd.DataFrame({'标签': ['适应症', '非适应症', '.']})
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sort_mapping_2 = df_mapping_2.reset_index().set_index('标签')
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##snvindel处理
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snv_size = os.path.getsize(snv_file)
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if snv_size > 0:
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data = pd.read_table(snv_file, sep="\t")
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data['level1'] = data['Evidence_Source_C'].map(sort_mapping_1['index'])
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data['level2'] = data['标签'].map(sort_mapping_2['index'])
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data.sort_values(by=['AAChange.refGene', 'level2', 'level1'], ascending=True, inplace=True)
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data.drop(['level1', 'level2'], axis=1, inplace=True)
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info = {}
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for index, row in data.iterrows():
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if re.search(r'敏感', row['Response_Type_C']):
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if row['标签'] == '适应症':
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info[row['AAChange.refGene'] + row['Drug']] = '1'
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else:
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if (row['AAChange.refGene'] + row['Drug']) in info.keys():
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data.drop([index], inplace=True)
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data.insert(0, '可信', 1)
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data.to_csv(snv_file_new, index=False, sep='\t')
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# ##germline snv/indel处理
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# gm_snv_size = os.path.getsize(gm_snv_file)
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# if gm_snv_size > 0:
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# data = pd.read_table(gm_snv_file, sep="\t")
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# data['level1'] = data['Evidence_Source_C'].map(sort_mapping_1['index'])
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# data['level2'] = data['标签'].map(sort_mapping_2['index'])
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# data.sort_values(by=['AAChange.refGene', 'level2', 'level1'], ascending=True, inplace=True)
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# data.drop(['level1', 'level2'], axis=1, inplace=True)
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# info = {}
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# for index, row in data.iterrows():
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# if re.search(r'敏感', row['Response_Type_C']):
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# if row['标签'] == '适应症':
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# info[row['AAChange.refGene'] + row['Drug']] = '1'
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# else:
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# if (row['AAChange.refGene'] + row['Drug']) in info.keys():
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# data.drop([index], inplace=True)
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# data.insert(0, '可信', 1)
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# data.to_csv(gm_snv_file_new, index=False, sep='\t')
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##fusion处理
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fusion_size = os.path.getsize(fusion_file)
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if fusion_size > 0:
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data = pd.read_table(fusion_file, sep="\t")
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data['level1'] = data['Evidence_Source_C'].map(sort_mapping_1['index'])
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data['level2'] = data['标签'].map(sort_mapping_2['index'])
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data.sort_values(by=['FUSION', 'level2', 'level1'], ascending=True, inplace=True)
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data.drop(['level1', 'level2'], axis=1, inplace=True)
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info = {}
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for index, row in data.iterrows():
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if re.search(r'敏感', row['Response_Type_C']):
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if row['标签'] == '适应症':
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info[row['FUSION'] + row['Drug']] = '1'
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else:
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if (row['FUSION'] + row['Drug']) in info.keys():
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data.drop([index], inplace=True)
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data.insert(0, '可信', 1)
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data.to_csv(fusion_file_new, index=False, sep='\t')
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##cnv处理
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cnv_size = os.path.getsize(cnv_file)
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if cnv_size > 0:
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data = pd.read_table(cnv_file, sep="\t")
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data['level1'] = data['Evidence_Source_C'].map(sort_mapping_1['index'])
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data['level2'] = data['标签'].map(sort_mapping_2['index'])
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data.sort_values(by=['Gene_Symbol', 'level2', 'level1'], ascending=True, inplace=True)
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data.drop(['level1', 'level2'], axis=1, inplace=True)
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info = {}
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for index, row in data.iterrows():
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if re.search(r'敏感', row['Response_Type_C']):
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if row['标签'] == '适应症':
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info[row['Gene_Symbol'] + row['Drug']] = '1'
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else:
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if (row['Gene_Symbol'] + row['Drug']) in info.keys():
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data.drop([index], inplace=True)
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data.insert(0, '可信', 1)
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data.to_csv(cnv_file_new, index=False, sep='\t')
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