458 lines
18 KiB
Python
458 lines
18 KiB
Python
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import json
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import re
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import sys
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import time
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from collections import defaultdict
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import pandas as pd
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from tools.readxlsx import read
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def tree():
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return defaultdict(tree)
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class BaseAssignment:
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def __init__(self, *args, **kwargs):
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super().__init__(*args, **kwargs)
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self.result = tree() # 报告结果
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self.signtb = set() # 具有明确或潜在临床意义的基因变异
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self.signdurg = set() # 潜在临床获益的治疗药物
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self.drugs_type = dict()
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class Parse(BaseAssignment):
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def __init__(self, sampledata, *args, **kwargs):
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super().__init__(*args, **kwargs)
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self.sampledata = sampledata
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def cms(self):
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"""
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样本信息处理
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"""
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data = pd.DataFrame(self.sampledata['sample_info'])
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if data.empty:
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raise UserWarning('sample_info表为空，生成报告失败！')
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data = data.applymap(
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lambda x: str(x).replace('.', '/').replace('-', '/').replace('——', '/') if str(x) in ['.', '-', '——'] else x)
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data_dict = data.to_dict('index')[0]
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data_dict['receiveTime'] = re.split(' ', data_dict['receiveTime'])[0]
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data_dict['reportTime'] = time.strftime("%Y-%m-%d", time.localtime())
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self.result['c'] = data_dict
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def target(self):
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data = pd.DataFrame(self.sampledata['snvindel'])
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res = []
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if data.empty:
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self.result['snvindel'] = res
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return
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data = data[data['Validated'] == 1].reset_index()
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data['muttype'] = '/'
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data.loc[data['ExonicFunc.refGene'].str.match('nonsynonymous SNV'), 'muttype'] = '错义突变'
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data.loc[data['ExonicFunc.refGene'].str.match('^frameshift'), 'muttype'] = '移码突变'
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data.loc[data['ExonicFunc.refGene'].str.match('^nonframeshift'), 'muttype'] = '非移码突变'
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data.loc[data['ExonicFunc.refGene'].str.match('stopgain'), 'muttype'] = '提前终止'
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# 拆分hgvs
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data[['gene', 'transcript', 'exon', 'nacid', 'aacid']] = data['AAChange.refGene'].str.split(':', expand=True)
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# 没有氨基酸改变用核苷酸改变代替
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data['aacid'] = data['aacid'].fillna(data['nacid'])
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for alter, alter_data in data.groupby('AAChange.refGene'):
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alter_data_need = alter_data[['gene', 'transcript', 'exon', 'nacid', 'aacid', 'mutant_frequency',
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'AMP_mut_level', 'muttype', 'Gene_function']]
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alter_res = alter_data_need.iloc[0].to_dict()
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alter_res['drug_category'] = self._drug_category(alter_data)
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drug_content = alter_data[
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['DrugCn', 'Response_Type', 'Indication', 'Evidence_Source', 'Efficacy_Evidence']]
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drug_content = drug_content[drug_content['DrugCn'] != '.']
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alter_res['drug_content'] = drug_content.reset_index().to_dict('records')
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alter_res['alter'] = alter
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res.append(alter_res)
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# 汇总
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if alter_res['AMP_mut_level'] in ['I', 'II']:
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self.signtb.add(alter)
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self.result['snvindel'] = res
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def fusion(self):
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data = pd.DataFrame(self.sampledata['fusion'])
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res = []
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if data.empty:
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self.result['fusion'] = res
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return
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data = data[data['Validated'] == 1].reset_index()
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for alter, alter_data in data.groupby('FUSION'):
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alter_data_need = alter_data[['FUSION', 'FREQ1', 'AMP_mut_level', 'Gene_function']]
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alter_res = alter_data_need.iloc[0].to_dict()
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alter_res['drug_category'] = self._drug_category(alter_data)
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drug_content = alter_data[
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['DrugCn', 'Response_Type', 'Indication', 'Evidence_Source', 'Efficacy_Evidence']]
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drug_content = drug_content[drug_content['DrugCn'] != '.']
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alter_res['drug_content'] = drug_content.reset_index().to_dict('records')
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alter_res['alter'] = '%s 融合' % (alter_res['FUSION'].replace('-', ':'))
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res.append(alter_res)
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# 汇总
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if alter_res['AMP_mut_level'] in ['I', 'II']:
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self.signtb.add(alter)
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self.result['fusion'] = res
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def cnv(self):
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data = pd.DataFrame(self.sampledata['cnv'])
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res = []
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if data.empty:
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self.result['cnv'] = res
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return
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data = data[data['Validated'] == 1].reset_index()
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for alter, alter_data in data.groupby('Gene_Symbol'):
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alter_data_need = alter_data[['Gene_Symbol', 'Copy_number', 'AMP_mut_level', 'Gene_function']].reset_index()
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alter_data_need['muttype'] = '缺失'
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alter_data_need.loc[alter_data_need['Copy_number'] > 2, 'muttype'] = '扩增'
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alter_res = alter_data_need.iloc[0].to_dict()
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alter_res['drug_category'] = self._drug_category(alter_data)
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drug_content = alter_data[
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['DrugCn', 'Response_Type', 'Indication', 'Evidence_Source', 'Efficacy_Evidence']]
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drug_content = drug_content[drug_content['DrugCn'] != '.']
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alter_res['drug_content'] = drug_content.reset_index().to_dict('records')
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alter_res['alter'] = '%s %s' % (alter, alter_res['muttype'])
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res.append(alter_res)
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# 汇总
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if alter_res['AMP_mut_level'] in ['I', 'II']:
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self.signtb.add(alter)
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self.result['cnv'] = res
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def hotspot(self):
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self._to_records('hotspot')
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def met(self):
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self._to_records('MET')
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def longindel(self):
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self._to_records('longindel')
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def mmr(self):
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data = pd.DataFrame(self.sampledata['MMR'])
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result_summary = '未检测到相关基因突变'
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predict = '对免疫检查点抑制剂可能不敏感'
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mmr_num = 0
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res = []
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if not data.empty:
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tmdf = data[['gene', 'p_change']].reset_index()
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tmdf['result_summary'] = tmdf.apply(lambda x: '%s %s' % (x['gene'], x['p_change']), axis=1)
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result_summary = ' | '.join(tmdf['result_summary'].to_list())
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predict = '对免疫检查点抑制剂可能敏感'
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mmr_num = len(data.index)
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res = data.to_dict('records')
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self.result['MMR'] = res
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self.result['sum']['mmr'] = dict(
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result_summary=result_summary,
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predict=predict,
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mmr_num=mmr_num
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)
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def msi(self):
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self._to_dicts('MSI')
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# def chemo(self):
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# data = pd.DataFrame(self.sampledata['chemo'])
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#
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# project = data['project'].to_list()[0]
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#
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# # 分类汇总 同位点，药物合并 drug.infos.txt
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# drugrsid = data[['drugname', 'genename', 'rsid', 'result', 'level', 'tips', 'drugsort']]
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# drugrsid = drugrsid.drop_duplicates()
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# resdrugrsid = drugrsid.groupby(['drugname', 'genename', 'rsid', 'result', 'level', 'drugsort'])['tips'].agg(
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# ','.join).reset_index()
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# resdrugrsid.rename(columns=
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# {'drugname': '药物', 'genename': '检测基因', 'rsid': '检测位点', 'result': '基因型',
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# 'level': '证据等级', 'tips': '用药提示'},
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# inplace=True)
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# resdrugrsid = resdrugrsid.sort_values(by=['drugsort', '药物', '检测基因'])
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# self.result['chemo']['druginfo'] = resdrugrsid.to_dict('records')
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#
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# # 药物 药物疗效 推荐程度合并 drug.res.txt
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# drugtypesum = data[['drugname', 'drugtype', 'rsid', 'weights']]
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# drugtypesum = drugtypesum.drop_duplicates()
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# drugtyperes = list()
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# drugsum = dict()
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# for drug, drugdata in drugtypesum.groupby('drugname'):
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# tipsnum = drugdata.groupby(['drugtype']).agg({'weights': 'sum'}).to_dict('index')
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# sumlist = list()
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# if 'LX' in tipsnum:
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# LX = tipsnum['LX']['weights']
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# if LX > 0:
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# lxdes = '疗效较好'
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# lxnum = 1
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# elif LX == 0:
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# lxdes = '疗效一般'
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# lxnum = 0
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# else:
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# lxdes = '疗效较差'
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# lxnum = -1
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# sumlist.append(lxdes)
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# else:
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# LX = 0
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# lxnum = 0
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# if 'DF' in tipsnum:
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# DF = tipsnum['DF']['weights']
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# if DF > 0:
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# dfdes = '毒副较低'
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# dfnum = 1
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# elif DF == 0:
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# dfdes = '毒副一般'
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# dfnum = 0
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# else:
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# dfdes = '毒副较高'
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# dfnum = -1
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# sumlist.append(dfdes)
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# else:
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# DF = 0
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# dfnum = 0
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#
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# # 评价方式 疗效 1 0 -1, 毒副 1 0 -1 ，可形成9宫格
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# sumnum = lxnum + dfnum
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# if sumnum > 0:
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# sumdes = '推荐'
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# elif sumnum == 0:
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# sumdes = '常规'
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# else:
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# sumdes = '谨慎'
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#
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# # 特别药物处理
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# if (drug == "氟尿嘧啶" or drug == "卡培他滨") and DF < 0:
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# sumdes = '谨慎'
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#
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# drugtyperes.append(dict(
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# 药物名称=drug,
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# 疗效=LX,
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# 毒副=DF,
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# 推荐程度=sumdes,
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# 疗效和毒副总结=','.join(sumlist)
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# ))
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# drugsum[drug] = sumdes
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#
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# # 报告中展示药物有顺序
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# drugsort = data[['drugname', 'drugsort']].drop_duplicates()
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# drugsort_dict = drugsort.set_index('drugname')['drugsort'].to_dict()
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# drugtyperes_sort = sorted(drugtyperes, key=lambda x: (
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# drugsort_dict[x['药物名称']] if x['药物名称'] in drugsort_dict else 100, x['药物名称']))
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#
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# drugtyperes_sort_df = pd.DataFrame(drugtyperes_sort)
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# self.result['chemo']['sum'] = drugtyperes_sort_df.groupby('推荐程度')['药物名称'].apply(','.join).to_dict()
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# self.result['chemo']['drugres'] = drugtyperes_sort_df.to_dict('records')
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#
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# # 联合用药
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# drug_combine_path = os.path.join(os.path.dirname(os.path.dirname(__file__)), 'database',
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# 'chemo_drug_combine.csv')
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# drug_combine = pd.read_csv(drug_combine_path, sep='\t')
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# drug_combine.fillna('.', inplace=True)
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# drug_combine_data = drug_combine[drug_combine['source'].str.contains(project)]
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# drug_combine_data = drug_combine_data.reset_index()
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# if not drug_combine_data.empty:
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# drug_combine_data['临床提示'] = drug_combine_data['用药方案'].apply(self._get_drug_plan, args=(drugsum,))
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# self.result['chemo']['combine'] = drug_combine_data.groupby('癌种').apply(
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# lambda group: group.set_index('癌种').to_dict('records')).to_dict()
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# else:
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# self.result['chemo']['combine'] = dict()
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# self.result['sum']['chemo_drug_num'] = len(drugsum.keys())
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def chemo(self):
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chemo_res = self._to_records('chemo_res', need=True)
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chemo_res_df = pd.DataFrame(chemo_res)
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chemo_res_df.index = chemo_res_df.index + 1
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chemo_res_df = chemo_res_df.reset_index()
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self.result['chemo']['chemo_res'] = chemo_res_df.to_dict('records')
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self.result['sum']['chemo']['drug_num'] = len(chemo_res)
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self.result['sum']['chemo']['drug_category'] = pd.DataFrame(chemo_res).groupby('推荐程度')['药物名称'].apply(
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','.join).to_dict()
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chemo_comb = self._to_records('chemo_comb', need=True)
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chemo_comb_res = dict()
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if chemo_comb:
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chemo_comb_res = pd.DataFrame(chemo_comb).groupby('癌种').apply(
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lambda group: group.set_index('癌种').to_dict('records')).to_dict()
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self.result['chemo']['chemo_comb'] = chemo_comb_res
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chemo_info = self._to_records('chemo_info', need=True)
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chemo_info_res = dict()
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if chemo_info:
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chemo_info_res = pd.DataFrame(chemo_info).groupby('药物').apply(
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lambda group: group.set_index('药物').to_dict('records')).to_dict()
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self.result['chemo']['chemo_info'] = chemo_info_res
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def hcs(self):
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self._to_records('HCS')
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self.result['sum']['hcs']['num'] = len(self.result['HCS'])
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def heredity(self):
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"""
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遗传的结果文件
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:return:
|
|||
|
|
"""
|
|||
|
|
hereditary = pd.DataFrame(self.sampledata['hereditary'])
|
|||
|
|
result = '/'
|
|||
|
|
disease = '/'
|
|||
|
|
risk = '/'
|
|||
|
|
if not hereditary.empty:
|
|||
|
|
result = '|'.join(hereditary.apply(lambda x: '%s %s' % (x['基因'], x['检测结果']), axis=1).to_list())
|
|||
|
|
disease = '|'.join(hereditary['遗传性肿瘤综合征'].to_list())
|
|||
|
|
|
|||
|
|
hereditary_risk = pd.DataFrame(self.sampledata['hereditary_risk'])
|
|||
|
|
if not hereditary_risk.empty:
|
|||
|
|
risk = ','.join(hereditary_risk[hereditary_risk['风险值'] == '偏高']['肿瘤类型'].to_list())
|
|||
|
|
|
|||
|
|
self.result['hereditary'] = hereditary.to_dict('records')
|
|||
|
|
self.result['sum']['hereditary']['result'] = result
|
|||
|
|
self.result['sum']['hereditary']['disease'] = disease
|
|||
|
|
self.result['sum']['hereditary']['risk'] = risk
|
|||
|
|
|
|||
|
|
def qc(self):
|
|||
|
|
# self._to_dicts('qc')
|
|||
|
|
data = pd.DataFrame(self.sampledata['qc'])
|
|||
|
|
res = {}
|
|||
|
|
if not data.empty:
|
|||
|
|
data.rename(columns={
|
|||
|
|
'Q30(%)': 'q30',
|
|||
|
|
'mean_depth(dedup)': 'depth',
|
|||
|
|
'coverage(>=0.2*meanx)': 'coverage'
|
|||
|
|
}, inplace=True)
|
|||
|
|
res = data.to_dict('index')[0]
|
|||
|
|
self.result['qc'] = res
|
|||
|
|
|
|||
|
|
def drugs(self):
|
|||
|
|
data = pd.DataFrame(self.sampledata['drugs'])
|
|||
|
|
res = {}
|
|||
|
|
if not data.empty:
|
|||
|
|
data = data.dropna()
|
|||
|
|
data = data[data['drug_detail'] != '.']
|
|||
|
|
res = data.set_index('drug_name')['drug_detail'].to_dict()
|
|||
|
|
self.result['drugs']['drugs_detail'] = res
|
|||
|
|
|
|||
|
|
def indication(self):
|
|||
|
|
self._to_records('indication')
|
|||
|
|
|
|||
|
|
def _to_records(self, sheetname, need=False):
|
|||
|
|
"""
|
|||
|
|
for many lines
|
|||
|
|
:param sheetname:
|
|||
|
|
:return:
|
|||
|
|
"""
|
|||
|
|
data = pd.DataFrame(self.sampledata[sheetname])
|
|||
|
|
res = []
|
|||
|
|
if data.empty:
|
|||
|
|
self.result[sheetname] = res
|
|||
|
|
return
|
|||
|
|
res = data.to_dict('records')
|
|||
|
|
if need:
|
|||
|
|
return res
|
|||
|
|
self.result[sheetname] = res
|
|||
|
|
|
|||
|
|
def _to_dicts(self, sheetname):
|
|||
|
|
"""
|
|||
|
|
for single line
|
|||
|
|
:param sheetname:
|
|||
|
|
:return:
|
|||
|
|
"""
|
|||
|
|
data = pd.DataFrame(self.sampledata[sheetname])
|
|||
|
|
res = {}
|
|||
|
|
if data.empty:
|
|||
|
|
self.result[sheetname] = res
|
|||
|
|
return
|
|||
|
|
res = data.to_dict('index')[0]
|
|||
|
|
self.result[sheetname] = res
|
|||
|
|
|
|||
|
|
def _drug_category(self, groupdata):
|
|||
|
|
drug_category_res = dict()
|
|||
|
|
for drug_category, drug_category_alter_data in groupdata.groupby('Drug_Category'):
|
|||
|
|
if drug_category == '.':
|
|||
|
|
continue
|
|||
|
|
# 敏感，可能敏感药物统计
|
|||
|
|
if drug_category in ['a', 'b', 'c']:
|
|||
|
|
self.signdurg.update(set(drug_category_alter_data['DrugCn'].str.split(',').explode().tolist()))
|
|||
|
|
drug_category_alter_data['drugdes'] = drug_category_alter_data.apply(
|
|||
|
|
lambda x: '%s 【%s 级】' % (x['DrugCn'], x['AMP_evidence_level']), axis=1)
|
|||
|
|
drug_category_res[drug_category] = '\n'.join(drug_category_alter_data['drugdes'].to_list())
|
|||
|
|
|
|||
|
|
# 所有药物信息
|
|||
|
|
groupdata['list_col'] = groupdata['DrugCn'].str.replace(' + ', '+').str.split(r'[+,]')
|
|||
|
|
exploded_df = groupdata.explode('list_col').reset_index()
|
|||
|
|
exploded_df = exploded_df[(exploded_df['list_col'] != '.') & (exploded_df['list_col'] != '')]
|
|||
|
|
exploded_dict = exploded_df.groupby('Response_Type')['list_col'].agg(lambda x: list(set(x))).to_dict()
|
|||
|
|
|
|||
|
|
for drug_type in exploded_dict:
|
|||
|
|
if drug_type in self.drugs_type:
|
|||
|
|
self.drugs_type[drug_type].extend(exploded_dict[drug_type])
|
|||
|
|
else:
|
|||
|
|
self.drugs_type[drug_type] = exploded_dict[drug_type]
|
|||
|
|
|
|||
|
|
# for drugall in exploded_df['Drug_Detail'].to_list():
|
|||
|
|
# for drug in drugall.split('|'):
|
|||
|
|
# match = re.search(r'\[\[(.*?)]](.*?)$', drug)
|
|||
|
|
# if match:
|
|||
|
|
# self.drugs_record['drugs'].update({match.group(1).strip(): match.group(2).strip()})
|
|||
|
|
return drug_category_res
|
|||
|
|
|
|||
|
|
@staticmethod
|
|||
|
|
def _get_drug_plan(x, drugsum):
|
|||
|
|
tlist = x.split('+')
|
|||
|
|
tdeslist = list()
|
|||
|
|
for tdes in tlist:
|
|||
|
|
if tdes.strip() in drugsum:
|
|||
|
|
t1_des = drugsum[tdes.strip()]
|
|||
|
|
tdeslist.append(t1_des)
|
|||
|
|
|
|||
|
|
if '慎用' in tdeslist or '谨慎' in tdeslist:
|
|||
|
|
return '慎用'
|
|||
|
|
elif '推荐' in tdeslist:
|
|||
|
|
return '推荐'
|
|||
|
|
elif '常规' in tdeslist:
|
|||
|
|
return '可选'
|
|||
|
|
else:
|
|||
|
|
return '可选'
|
|||
|
|
|
|||
|
|
def collect(self):
|
|||
|
|
self.cms()
|
|||
|
|
self.target()
|
|||
|
|
self.fusion()
|
|||
|
|
self.cnv()
|
|||
|
|
self.hotspot()
|
|||
|
|
self.met()
|
|||
|
|
self.longindel()
|
|||
|
|
self.mmr()
|
|||
|
|
self.msi()
|
|||
|
|
self.chemo()
|
|||
|
|
self.hcs()
|
|||
|
|
self.heredity()
|
|||
|
|
self.qc()
|
|||
|
|
self.indication()
|
|||
|
|
self.drugs()
|
|||
|
|
|
|||
|
|
# 汇总
|
|||
|
|
self.result['sum']['signtb_num'] = len(self.signtb)
|
|||
|
|
self.result['sum']['signdrug_num'] = len(self.signdurg)
|
|||
|
|
self.result['drugs']['drugs_type'] = {key: self.drugs_type[key] for key in sorted(self.drugs_type.keys())}
|
|||
|
|
return self.result
|
|||
|
|
|
|||
|
|
|
|||
|
|
def run(path):
|
|||
|
|
parse = Parse(read(path))
|
|||
|
|
res = parse.collect()
|
|||
|
|
resjson = json.dumps(res, indent=4, ensure_ascii=False)
|
|||
|
|
with open('t.json', 'w') as f:
|
|||
|
|
f.write(resjson)
|
|||
|
|
return resjson
|
|||
|
|
|
|||
|
|
|
|||
|
|
if __name__ == '__main__':
|
|||
|
|
run(sys.argv[1])
|