579 lines
24 KiB
Python
Executable File
579 lines
24 KiB
Python
Executable File
#! /usr/bin/env python3
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import argparse
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import glob
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import json
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import os
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import re
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from collections import defaultdict
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import openpyxl
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import pandas as pd
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from openpyxl.drawing.image import Image
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def database():
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"""
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增持匹配信息
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"""
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path = os.environ.get('DATABASE')
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if not path:
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raise UserWarning('未设置DATABASE环境变量')
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# gene function 基因功能描述信息
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gene_function_path = os.path.join(path, 'gene_function.txt')
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gene_function_df = pd.read_csv(gene_function_path, sep='\t')
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gene_function_df = gene_function_df.fillna('.')
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gene_function_df['genename'] = gene_function_df.apply(lambda x: x['基因'].upper(), axis=1)
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# return {基因: 基因功能翻译}
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gene_function = gene_function_df.set_index('genename')['基因功能翻译'].to_dict()
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# drug_mechanism 药物描述信息
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drug_mechanism_path = os.path.join(path, 'target_drug.txt')
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drug_mechanism_df = pd.read_csv(drug_mechanism_path, usecols=list(range(18)), sep='\t')
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drug_mechanism_df = drug_mechanism_df.fillna('.')
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drug_mechanism_df['drugs'] = drug_mechanism_df.apply(lambda x: '|'.join(
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[tm.upper() for tm in [x['药物名称-英文'], x['药物名称-中文']] if tm != '.']), axis=1)
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drug_mechanism_df['dec'] = drug_mechanism_df.apply(lambda x: "||".join(
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[tm for tm in [x['适应症（中文）'], x['作用机制-中文']] if tm != '.']), axis=1)
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# return {drugA: drugAdes\\\\drugAdes中文， drugA中文: drugAdes\\\\drugAdes中文，}
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drug_mechanism = {}
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for _, row in drug_mechanism_df.iterrows():
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drug_mechanism.update(dict(zip(row['drugs'].split('|'), [row['dec']] * len(row['drugs'].split('|')))))
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return dict(gene_function=gene_function, drug_mechanism=drug_mechanism)
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def check_file_exist_and_empty(file_path):
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# 检查文件是否存在
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if not os.path.exists(file_path):
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return f"文件 '{file_path}' 不存在"
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# 检查文件是否为空
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if os.path.getsize(file_path) == 0:
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return f"文件 '{file_path}' 是空文件"
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def tree():
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return defaultdict(tree)
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class PostProcess:
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"""
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excel处理
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"""
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def __init__(self, sample_name, normal_name, path, outpath):
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self.path = path
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self.outpath = outpath
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self.sample_name = sample_name
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self.normal_name = normal_name
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# self.needcol = self.need_col()
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self.database = database()
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self.drug_record = list()
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self.sheet = tree()
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@staticmethod
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def txt_2_excel(path):
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try:
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df = pd.read_csv(path, sep='\t')
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except (pd.errors.EmptyDataError, FileNotFoundError):
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return []
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return df.to_dict('records')
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@staticmethod
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def _add_columns(df: pd.DataFrame):
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"""
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添加必须列，经过转换得到新列，这个应该后期最好在注释的时候进行
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"""
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# 证据等级转换
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if 'Label' in df.columns and '证据等级' in df.columns:
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df['证据等级'] = df.apply(lambda x: 'C' if x['Label'] == '非适应症' else x['证据等级'], axis=1)
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# 突变的等级分类
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if '证据等级' in df.columns:
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df['AMP_mut_level'] = df['证据等级'].replace(['A', 'B', 'C', 'D'], ['I', 'I', 'II', 'II'])
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# 药物等级划分
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if 'Response_Type_C' in df.columns and '证据等级' in df.columns:
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df['Drug_Category'] = 'c'
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df.loc[df['Response_Type_C'].str.contains('敏感') & (df['证据等级'] == 'A'), 'Drug_Category'] = 'a'
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# df.loc[df['Response_Type_C'].str.startswith('敏感') & (df['证据等级'] == 'C'), 'Drug_Category'] = 'b'
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df.loc[df['Response_Type_C'].str.contains('敏感') & (df['Label'] == '非适应症'), 'Drug_Category'] = 'b'
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df.loc[df['Response_Type_C'].str.contains('耐药'), 'Drug_Category'] = 'd'
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# 胚系突变分类 通过 CLNSIG列区分突变等级
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if 'CLNSIG' in df.columns:
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df['ACMG_level'] = '3'
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df.loc[df['CLNSIG'].str.lower().str.startswith('likely_pathogenic'), 'ACMG_level'] = '2'
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df.loc[(df['CLNSIG'].str.lower().str.startswith('pathogenic')), 'ACMG_level'] = '1'
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return df
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@staticmethod
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def _add_cal_col(df: pd.DataFrame):
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try:
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# 统计 'MutationTaster_pred', 'FATHMM_pred', 'MetaLR_pred' 的D的数目
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tmdf2 = df[['MutationTaster_pred', 'FATHMM_pred', 'MetaLR_pred']]
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df['Deleterious'] = tmdf2.apply(lambda x: x.tolist().count('D'), axis=1)
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# 计算 这几列中最大的值
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# eval(str(x)) 科学计数法转换
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tmdf1 = df[
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['1000g2015aug_all', '1000g2015aug_eas', 'esp6500siv2_all', 'ExAC_nontcga_ALL', 'ExAC_nontcga_EAS',
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'gnomAD_genome_ALL', 'gnomAD_genome_EAS']].replace('.', 0).applymap(lambda x: eval(str(x)))
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df['freq_high'] = tmdf1.max(axis=1)
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except KeyError as e:
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print(e)
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return df
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def _add_gene_function(self, df: pd.DataFrame, colname='Gene_refGene'):
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if colname in df.columns:
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df['Gene_function'] = df.apply(lambda x: self.database['gene_function'].get(x[colname], ''),
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axis=1)
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return df
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def drug_parse(self, drugs: list):
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"""
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药物处理
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"""
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# 药物单独处理 sheetname
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# for drug in drugs['药物中文名'].to_list():
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for drug in drugs:
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for spdrug in re.split(" \+ |,", drug):
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self.drug_record.append(
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{'drug_name': spdrug, 'drug_detail': self.database['drug_mechanism'].get(spdrug, '')})
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def cms(self):
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"""
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样本信息
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"""
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# cms_files = glob.glob(os.path.join(self.path, 'qc', '*_post.json'))
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cms_info_need = []
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cms_file = os.path.join(self.path, 'qc', f'{self.sample_name}_post.json')
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check_file = check_file_exist_and_empty(cms_file)
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if check_file:
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return cms_info_need
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file_read = open(cms_file, 'r')
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try:
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cms_info = json.load(file_read)['data']
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except Exception as e:
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raise UserWarning('cms 文件加载有误！', e)
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if not cms_info:
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raise UserWarning('cms 内容为空！')
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file_read.close()
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df = pd.DataFrame(cms_info)
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df['normal'] = self.normal_name
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cms_info_need = df.to_dict('records')
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self.sheet['cms'] = cms_info_need
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def snv(self):
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filter_sum = os.path.join(self.path, 'mutation',
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f'{self.sample_name}.snp_indel.somatic.hg19_multianno.filter.sum.txt')
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filter_pos = os.path.join(self.path, 'mutation',
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f'{self.sample_name}.snp_indel.somatic.hg19_multianno.filter.sum.pos.txt')
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filter_vus = os.path.join(self.path, 'mutation',
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f'{self.sample_name}.snp_indel.somatic.hg19_multianno.filter.sum.vus.txt')
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filter_neg = os.path.join(self.path, 'mutation',
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f'{self.sample_name}.snp_indel.somatic.hg19_multianno.filter.sum.neg.txt')
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tmb_file = os.path.join(self.path, 'tmb', f'{self.sample_name}.tmb.txt')
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filter_sum_pos_res = list()
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# 从pos_files中获取药物信息
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pos_check = check_file_exist_and_empty(filter_pos)
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pos_dict = dict()
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if not pos_check:
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pos = pd.read_csv(filter_pos, sep='\t')
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# 添加常规列
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pos = self._add_columns(pos)
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# 添加基因功能
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pos = self._add_gene_function(pos)
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# 药物处理
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self.drug_parse(pos['DrugCn'].to_list())
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pos = pos.fillna('.')
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grouped_df = pos.groupby('AAChange_refGene')
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# 对每个分组进行操作
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for group_name, group_data in grouped_df:
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if any(group_data['AMP_mut_level'] == 'I'):
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pos.loc[pos['AAChange_refGene'] == group_name, 'AMP_mut_level'] = 'I'
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pos_dict = pos.set_index(['Chr', 'Start', 'End'])['AMP_mut_level'].to_dict()
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filter_sum_pos_res = pos.to_dict('records')
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# 从vus_file中获取vus等级分类 'III' 级
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vus_dict = dict()
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vus_check = check_file_exist_and_empty(filter_vus)
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if not vus_check:
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vus = pd.read_csv(filter_vus, sep='\t')
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vus['AMP_mut_level'] = 'III'
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vus_dict = vus.set_index(['Chr', 'Start', 'End'])['AMP_mut_level'].to_dict()
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# 从neg_file中获取neg等级分类 'IIII' 级
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neg_dict = dict()
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neg_check = check_file_exist_and_empty(filter_neg)
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if not neg_check:
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neg = pd.read_csv(filter_neg, sep='\t')
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neg['AMP_mut_level'] = 'IIII'
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neg_dict = neg.set_index(['Chr', 'Start', 'End'])['AMP_mut_level'].to_dict()
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filter_sum_df = pd.DataFrame()
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filter_sum_check = check_file_exist_and_empty(filter_sum)
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if not filter_sum_check:
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filter_sum_df = pd.read_csv(filter_sum, sep='\t')
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level_dict = dict()
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level_dict.update(pos_dict)
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level_dict.update(vus_dict)
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level_dict.update(neg_dict)
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filter_sum_df['AMP_mut_level'] = filter_sum_df.set_index(['Chr', 'Start', 'End']).index.map(level_dict)
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cols = list(filter_sum_df.columns)
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tmb_file_check = check_file_exist_and_empty(tmb_file)
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if not tmb_file_check:
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tmb_df = pd.read_csv(tmb_file, sep='\t')
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key_cols = ['Chr', 'Start', 'End']
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filter_sum_df = filter_sum_df.set_index(key_cols)
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tmb_df = tmb_df.set_index(key_cols)
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# 在filter_sum_df中的process列中追加字符串";tmb"，对应tmb_df中的行 并且 是非 1，2类突变
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filter_sum_df['process'] = filter_sum_df.index.map(
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lambda x: filter_sum_df.at[x, 'process'] + ';tmb' if x in tmb_df.index and filter_sum_df.at[
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x, 'AMP_mut_level'] not in ['I', 'II'] else filter_sum_df.at[x, 'process'])
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# 找到tmb_df中不在filter_sum_df中的行，并将这些新的行添加到filter_sum_df中
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new_rows = tmb_df[~tmb_df.index.isin(filter_sum_df.index)]
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filter_sum_df = pd.concat([filter_sum_df, new_rows])
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# 重置索引
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filter_sum_df = filter_sum_df.reset_index()
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# 按之前列排
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filter_sum_df = filter_sum_df[cols]
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filter_sum_df = filter_sum_df.fillna('.')
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filter_sum_res = filter_sum_df.to_dict('records')
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self.sheet['target_mut'] = filter_sum_res
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self.sheet['target_drug'] = filter_sum_pos_res
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def fusion(self):
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filter_sum_pos = os.path.join(self.path, 'fusion',
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f'{self.sample_name}.fusion.hg19_multianno.filter.pos.txt')
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filter_sum_pos_check = check_file_exist_and_empty(filter_sum_pos)
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filter_sum_pos_res = list()
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pos_dict = dict()
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if not filter_sum_pos_check:
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filter_sum_pos_df = pd.read_csv(filter_sum_pos, sep='\t')
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# 添加常规列
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filter_sum_pos_df = self._add_columns(filter_sum_pos_df)
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# 添加基因功能
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filter_sum_pos_df = self._add_gene_function(filter_sum_pos_df, colname='ref_gene')
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# 药物处理
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self.drug_parse(filter_sum_pos_df['DrugCn'].to_list())
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filter_sum_pos_df['Validated'] = 1
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filter_sum_pos_df = filter_sum_pos_df.fillna('.')
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grouped_df = filter_sum_pos_df.groupby(['POS', 'REF', 'ALT'])
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# 对每个分组进行操作
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for group_name, group_data in grouped_df:
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pos, ref, alt = group_name
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if any(group_data['AMP_mut_level'] == 'I'):
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filter_condition = (filter_sum_pos_df['POS'] == pos) & \
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(filter_sum_pos_df['REF'] == ref) & \
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(filter_sum_pos_df['ALT'] == alt)
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filter_sum_pos_df.loc[filter_condition, 'AMP_mut_level'] = 'I'
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pos_dict = filter_sum_pos_df.set_index(['POS', 'REF', 'ALT'])['AMP_mut_level'].to_dict()
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filter_sum_pos_res = filter_sum_pos_df.to_dict('records')
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filter_sum = os.path.join(self.path, 'fusion',
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f'{self.sample_name}.fusion.hg19_multianno.filter.txt')
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filter_sum_check = check_file_exist_and_empty(filter_sum)
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filter_sum_res = list()
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if not filter_sum_check:
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filter_sum_df = pd.read_csv(filter_sum, sep='\t')
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filter_sum_df['Validated'] = 1
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level_dict = dict()
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level_dict.update(pos_dict)
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filter_sum_df['AMP_mut_level'] = filter_sum_df.set_index(['POS', 'REF', 'ALT']).index.map(level_dict)
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filter_sum_df = filter_sum_df.fillna('.')
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filter_sum_res = filter_sum_df.to_dict('records')
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self.sheet['fusion_mut'] = filter_sum_res
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self.sheet['fusion_drug'] = filter_sum_pos_res
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def cnv(self):
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filter_sum_pos = os.path.join(self.path, 'cnv',
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f'{self.sample_name}.rmdup.cns.filter.pos.txt')
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filter_sum_pos_check = check_file_exist_and_empty(filter_sum_pos)
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filter_sum_pos_res = list()
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pos_dict = dict()
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if not filter_sum_pos_check:
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filter_sum_pos_df = pd.read_csv(filter_sum_pos, sep='\t')
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# 添加常规列
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filter_sum_pos_df = self._add_columns(filter_sum_pos_df)
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# 添加基因功能
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filter_sum_pos_df = self._add_gene_function(filter_sum_pos_df, colname='ref_gene')
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# 药物处理
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self.drug_parse(filter_sum_pos_df['DrugCn'].to_list())
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filter_sum_pos_df = filter_sum_pos_df.fillna('.')
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filter_sum_pos_df['Validated'] = 1
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grouped_df = filter_sum_pos_df.groupby(['chromosome', 'start', 'end', 'ref_gene'])
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# 对每个分组进行操作
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for group_name, group_data in grouped_df:
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chromosome, start, end, ref_gene = group_name
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if any(group_data['AMP_mut_level'] == 'I'):
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filter_condition = (filter_sum_pos_df['chromosome'] == chromosome) & \
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(filter_sum_pos_df['start'] == start) & \
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(filter_sum_pos_df['end'] == end) & \
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(filter_sum_pos_df['ref_gene'] == ref_gene)
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filter_sum_pos_df.loc[filter_condition, 'AMP_mut_level'] = 'I'
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filter_sum_pos_res = filter_sum_pos_df.to_dict('records')
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pos_dict = filter_sum_pos_df.set_index(['chromosome', 'start', 'end', 'ref_gene'])[
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'AMP_mut_level'].to_dict()
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filter_sum = os.path.join(self.path, 'cnv',
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f'{self.sample_name}.rmdup.cns.filter.txt')
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filter_sum_check = check_file_exist_and_empty(filter_sum)
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filter_sum_res = list()
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if not filter_sum_check:
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filter_sum_df = pd.read_csv(filter_sum, sep='\t')
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filter_sum_df['Validated'] = 1
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level_dict = dict()
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level_dict.update(pos_dict)
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filter_sum_df['AMP_mut_level'] = filter_sum_df.set_index(
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['chromosome', 'start', 'end', 'ref_gene']).index.map(level_dict)
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filter_sum_df = filter_sum_df.fillna('.')
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filter_sum_res = filter_sum_df.to_dict('records')
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self.sheet['cnv_mut'] = filter_sum_res
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self.sheet['cnv_drug'] = filter_sum_pos_res
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def msi(self):
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"""
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||
Process msi result files
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||
"""
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||
msi_file = os.path.join(self.path, 'msi', f'{self.sample_name}.msi.txt')
|
||
msi_check = check_file_exist_and_empty(msi_file)
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||
msi_res = dict()
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if not msi_check:
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df = pd.read_csv(msi_file, 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:
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msi_res['msi_result'] = 'MSS'
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||
msi_res['msi_predict'] = '对免疫检查点抑制剂可能不敏感'
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self.sheet['msi'] = [msi_res]
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def chemo(self):
|
||
"""
|
||
化疗
|
||
"""
|
||
# 化疗文件 .chemo.comb.txt .drug.infos.txt .drug.res.txt
|
||
res_file = os.path.join(self.path, 'chemo', f'{self.sample_name}.drug.res.txt')
|
||
info_file = os.path.join(self.path, 'chemo', f'{self.sample_name}.drug.infos.txt')
|
||
comb_file = os.path.join(self.path, 'chemo', f'{self.sample_name}.chemo.comb.txt')
|
||
res_check = check_file_exist_and_empty(res_file)
|
||
info_check = check_file_exist_and_empty(info_file)
|
||
comb_check = check_file_exist_and_empty(comb_file)
|
||
chemo_res = dict()
|
||
|
||
check_list = [res_check, info_check, comb_check]
|
||
file_list = [res_file, info_file, comb_file]
|
||
for index, name in enumerate(['chemo_res', 'chemo_info', 'chemo_comb']):
|
||
if not check_list[index]:
|
||
df = pd.read_csv(file_list[index], sep='\t')
|
||
df = df.fillna('.')
|
||
self.sheet[name] = df.to_dict('records')
|
||
chemo_res[name] = df.to_dict('records')
|
||
else:
|
||
print(check_list[index])
|
||
# raise UserWarning('%s 目录下缺%s 文件' % (self.path, name))
|
||
|
||
def germline(self):
|
||
germline_file = os.path.join(self.path, 'mutation',
|
||
f'{self.sample_name}.snp_indel.germline.hg19_multianno.filter.txt')
|
||
germlineres = []
|
||
germline_check = check_file_exist_and_empty(germline_file)
|
||
if not germline_check:
|
||
df = pd.read_csv(germline_file, sep='\t')
|
||
df = df.fillna('.')
|
||
germlineres = df.to_dict('records')
|
||
self.sheet['germline'] = germlineres
|
||
|
||
def heredity(self):
|
||
"""
|
||
遗传
|
||
"""
|
||
heredi_file = os.path.join(self.path, 'hereditary', f'{self.sample_name}.hereditary.pre.txt')
|
||
heredires = []
|
||
heredi_check = check_file_exist_and_empty(heredi_file)
|
||
if not heredi_check:
|
||
df = pd.read_csv(heredi_file, sep='\t')
|
||
df = df.fillna('.')
|
||
heredires = df.to_dict('records')
|
||
self.sheet['hereditary'] = heredires
|
||
|
||
def heredity_res(self):
|
||
"""
|
||
遗传结果文件
|
||
"""
|
||
hereditary_file = os.path.join(self.path, 'hereditary', f'{self.sample_name}.hereditary.txt')
|
||
heredi_risk_file = os.path.join(self.path, 'hereditary', f'{self.sample_name}.risk.txt')
|
||
for name, file in zip(['hereditary', 'hereditary_risk', ], [hereditary_file, heredi_risk_file]):
|
||
file_check = check_file_exist_and_empty(file)
|
||
if not file_check:
|
||
df = pd.read_csv(file, sep='\t')
|
||
df = df.fillna('.')
|
||
self.sheet[name] = df.to_dict('records')
|
||
else:
|
||
# raise UserWarning('%s 目录下缺%s 文件' % (self.path, name))
|
||
print(file_check)
|
||
self.sheet[name] = []
|
||
|
||
def indication(self):
|
||
|
||
indication_file = os.path.join(self.path, 'mutation', 'indication.txt')
|
||
file_check = check_file_exist_and_empty(indication_file)
|
||
if not file_check:
|
||
df = pd.read_csv(indication_file, sep='\t')
|
||
df = df.fillna('.')
|
||
self.sheet['indication'] = df.to_dict('records')
|
||
else:
|
||
# raise UserWarning('%s 目录下缺%s 文件' % (self.path, name))
|
||
print(file_check)
|
||
|
||
def longindel(self):
|
||
|
||
filter_sum_pos = os.path.join(self.path, 'fusion',
|
||
f'{self.sample_name}.longindel.hg19_multianno.filter.pos.txt')
|
||
filter_sum_pos_check = check_file_exist_and_empty(filter_sum_pos)
|
||
|
||
filter_sum_pos_res = list()
|
||
pos_dict = dict()
|
||
|
||
if not filter_sum_pos_check:
|
||
filter_sum_pos_df = pd.read_csv(filter_sum_pos, sep='\t')
|
||
# 添加常规列
|
||
filter_sum_pos_df = self._add_columns(filter_sum_pos_df)
|
||
# 添加基因功能
|
||
filter_sum_pos_df = self._add_gene_function(filter_sum_pos_df, colname='ref_gene')
|
||
# 药物处理
|
||
self.drug_parse(filter_sum_pos_df['DrugCn'].to_list())
|
||
filter_sum_pos_df['Validated'] = 1
|
||
filter_sum_pos_df = filter_sum_pos_df.fillna('.')
|
||
|
||
grouped_df = filter_sum_pos_df.groupby(['#CHROM', 'POS', 'REF', 'ALT'])
|
||
# 对每个分组进行操作
|
||
for group_name, group_data in grouped_df:
|
||
chr, pos, ref, alt = group_name
|
||
if any(group_data['AMP_mut_level'] == 'I'):
|
||
filter_condition = (filter_sum_pos_df['#CHROM'] == chr) & \
|
||
(filter_sum_pos_df['POS'] == pos) & \
|
||
(filter_sum_pos_df['REF'] == ref) & \
|
||
(filter_sum_pos_df['ALT'] == alt)
|
||
filter_sum_pos_df.loc[filter_condition, 'AMP_mut_level'] = 'I'
|
||
|
||
pos_dict = filter_sum_pos_df.set_index(['#CHROM', 'POS', 'REF', 'ALT'])['AMP_mut_level'].to_dict()
|
||
filter_sum_pos_res = filter_sum_pos_df.to_dict('records')
|
||
|
||
filter_sum = os.path.join(self.path, 'fusion', f'{self.sample_name}.longindel.hg19_multianno.filter.txt')
|
||
filter_sum_check = check_file_exist_and_empty(filter_sum)
|
||
filter_sum_res = list()
|
||
if not filter_sum_check:
|
||
filter_sum_df = pd.read_csv(filter_sum, sep='\t')
|
||
|
||
filter_sum_df['Validated'] = 1
|
||
level_dict = dict()
|
||
level_dict.update(pos_dict)
|
||
filter_sum_df['AMP_mut_level'] = filter_sum_df.set_index(['#CHROM', 'POS', 'REF', 'ALT']).index.map(
|
||
level_dict)
|
||
filter_sum_df = filter_sum_df.fillna('.')
|
||
filter_sum_res = filter_sum_df.to_dict('records')
|
||
|
||
self.sheet['longindel_mut'] = filter_sum_res
|
||
self.sheet['longindel_drug'] = filter_sum_pos_res
|
||
|
||
def neoantigen(self):
|
||
neoantigen = os.path.join(self.path, 'neoantigen', f'MHC_Class_I', 'neoantigen.txt')
|
||
hla = os.path.join(self.path, 'neoantigen', f'hla', f'{self.normal_name}_result.tsv')
|
||
|
||
self.sheet['neoantigen'] = self.txt_2_excel(neoantigen)
|
||
self.sheet['hla'] = self.txt_2_excel(hla)
|
||
|
||
def qc(self):
|
||
qc_files = glob.glob(os.path.join(self.path, 'qc', '*_qc.txt'))
|
||
qc_res = []
|
||
if qc_files:
|
||
if os.path.getsize(qc_files[0]) > 0:
|
||
qc = pd.read_csv(qc_files[0], sep='\t', header=None)
|
||
qc_res = [qc.set_index(0)[1].to_dict()]
|
||
self.sheet['qc'] = qc_res
|
||
|
||
def drugs(self):
|
||
drug_record = pd.DataFrame(self.drug_record)
|
||
drug_record = drug_record.drop_duplicates()
|
||
self.sheet['drugs'] = drug_record.to_dict('records')
|
||
|
||
def collect(self):
|
||
writer = pd.ExcelWriter(self.outpath)
|
||
self.cms()
|
||
self.qc()
|
||
self.snv()
|
||
self.fusion()
|
||
self.longindel()
|
||
self.cnv()
|
||
self.msi()
|
||
self.germline()
|
||
self.heredity()
|
||
self.heredity_res()
|
||
self.chemo()
|
||
self.indication()
|
||
self.neoantigen()
|
||
self.drugs()
|
||
|
||
# 遍历CSV文件列表
|
||
for sheet_name in self.sheet:
|
||
# 读取CSV文件为DataFrame
|
||
df = pd.DataFrame(self.sheet[sheet_name])
|
||
df.to_excel(writer, sheet_name=sheet_name, index=False)
|
||
# 保存并关闭Excel写入器
|
||
writer.close()
|
||
|
||
# 加入cnv.png ???
|
||
wb = openpyxl.load_workbook(filename=self.outpath)
|
||
ws = wb['cnv_mut']
|
||
mr = ws.max_row
|
||
cell = 'C' + str(mr + 4)
|
||
cnv_pic_paths = glob.glob(os.path.join(self.path, 'cnv', '*cnv.png'))
|
||
if cnv_pic_paths:
|
||
image = Image(cnv_pic_paths[0])
|
||
ws.add_image(image, cell)
|
||
wb.save(self.outpath)
|
||
wb.close()
|
||
|
||
|
||
if __name__ == '__main__':
|
||
# 未加日志，未添加路径
|
||
parser = argparse.ArgumentParser(description="post Process Script")
|
||
parser.add_argument('-n', '--barcode', help="sample's barcode", required=True)
|
||
parser.add_argument('-s', '--normal', help="sample's normal", default='', required=False, nargs='?')
|
||
parser.add_argument('-c', '--path', help="workflow run path", required=True)
|
||
parser.add_argument('-o', '--output', help="Output", required=True)
|
||
args = parser.parse_args()
|
||
|
||
postprocess = PostProcess(args.barcode, args.normal, args.path, args.output)
|
||
postprocess.collect()
|