磕磕绊绊基本学明白了R，现在用python下载和处理（文件名对应TCGA barcode），主要是通过任务带一下自己对python的学习；
准备说明
一共3个脚本，分别为var.py,fu.py和total.py;这里其实主要参考了，TCGA官网的python examplesPython Examples - GDC Docs (cancer.gov) 。即，total.py是真正运行的脚本，var.py和fu.py是一些定义，放在相同文件夹即可；从网上看到的所有教程，当涉及到路径的时候，要有一个基本的认知，是发布者测试的时候用到的路径，并非直接适用，要改为自己的路径运行；
- 从网上下载用到的搜索固定格式，var.py；主要用于TCGA样本的测序数据下载和临床信息下载；
filters是样本测序数据下载时用到的筛选用的框架，pre和pos是临床信息下载时用到的固定格式部分，middle在后面的脚本部分有展示，是测序数据的文件名信息，即基于测序数据中的文件名在TCGA中检索获得其对应的barcode和其他临床信息；
filters = {
"op": "and",
"content":[
# {
# "op": "in",
# "content":{
# "field": "cases.project.primary_site",
# "value": ["Lung"]
# }
# },
{
"op": "in",
"content":{
"field": "cases.project.project_id",
"value": ["TCGA-LUAD"]
}
},
# {
# "op": "in",
# "content":{
# "field": "cases.demographic.gender",
# "value": ["female"]
# }
# },
{
"op": "in",
"content":{
"field": "files.analysis.workflow_type",
"value": ["HTSeq - Counts"]
}
}
]
}
pre = '''
{
"filters":{
"op":"in",
"content":{
"field":"files.submitter_id",
"value":[
'''
pos = '''
]
}
},
"format":"TSV",
"fields":"file_id,file_name,cases.submitter_id,cases.case_id,data_category,data_type,cases.samples.tumor_descriptor,cases.samples.tissue_type,cases.samples.sample_type,cases.samples.submitter_id,cases.samples.sample_id,cases.samples.portions.analytes.aliquots.aliquot_id,cases.samples.portions.analytes.aliquots.submitter_id",
"size":"100"
}
'''
- fu.py是用到的函数的定义
import tarfile
import os
import pandas as pd
def tar(fname):
t = tarfile.open(fname + ".tar.gz", "w:gz")
for root, dir, files in os.walk(fname):
print(root, dir, files)
for file in files:
fullpath = os.path.join(root, file)
t.add(fullpath)
t.close()
def untar(fname, dirs):
t = tarfile.open(fname)
t.extractall(path = dirs)
def readFile(filepath):
list = os.listdir(filepath)
ens = pd.DataFrame()
submitter_id = ''
for root, dirs, files in os.walk(filepath):
# print(root)
for item in files:
if item.split('.')[-1] == 'gz':
tmp_path = os.path.join(root,item)
item_pre = item[:-16]
data = pd.read_csv(tmp_path, compression='gzip',
error_bad_lines=False,sep='\t',names=['ENSEMBLE',item_pre])
ens[item_pre] = data[item_pre]
ens['ENSEMBLE'] = data['ENSEMBLE']
return(ens)
- total.py是整体结构；Part1是数据下载部分，最终下载结果以压缩包的形式存在；Part2是数据处理部分，解压缩并将文件批量读取为一个矩阵；Part3是临床信息下载；Part4是将TCGA的barcode整合成矩阵的列名；
from va import *
from fu import *
import os
import gzip
import pandas as pd
import requests
import re
import json
##Part1 Here we download matrix of counts of TCGA-LUAD
files_endpt = "https://api.gdc.cancer.gov/files"
fields = [
"cases.submitter_id",
"file_name",
"cases.project.project_id",
"cases.project.primary_site"
]
fields = ','.join(fields)
params = {
"filters": json.dumps(filters),
"fields": fields,
"format": "JSON",
"size": "1000"
}
response = requests.get(files_endpt, params = params)
file_uuid_list = []
# This step populates the download list with the file_ids from the previous query
for file_entry in json.loads(response.content.decode("utf-8"))["data"]["hits"]:
file_uuid_list.append(file_entry["id"])
#file_uuid_list = file_uuid_list[51:100]
data_endpt = "https://api.gdc.cancer.gov/data"
params = {"ids": file_uuid_list}
response = requests.post(data_endpt, data = json.dumps(params), headers = {"Content-Type": "application/json"})
response_head_cd = response.headers["Content-Disposition"]
path = 'D:/tmp/'
file_name = path + 'TCGA-LUAD.tar.gz'
with open(file_name, "wb") as output_file:
output_file.write(response.content)
######Part2 Decompress and read files
f_gz = 'D:/tmp/TCGA-LUAD.tar.gz'
path1 = 'D:/tmp/tcga'
####untar是自定义的函数，把下载的tar.gz文件解压缩，readFile即把解压缩的文件夹里所有文件，保留每个文件的第二列（数据），index是文件的第一列，留一个即可，整合至一个文件；
untar(fname=f_gz,dirs=path1)
a = readFile('D:/tmp/tcga')
######Part3 download clinic info
path = 'D:/tmp/'
file_name = path + 'info.txt'
middle = pd.DataFrame(a.columns[:-1])
###middle是需要查询的临床信息对应的文件名，即files.submitter_id,是文件名加上_count
middle = '"'+ middle +'_count"'
middle = ',\n'.join(middle[0])
###whole是临床信息下载时用到的全部字段
whole = pre+str(middle)+pos
with open(file_name, "wb") as output_file:
output_file.write(bytes(whole.encode()))
url = 'https://api.gdc.cancer.gov/files'
payload = open("D:/tmp/info.txt")
headers = {'Content-type': 'application/json', 'Accept-Charset': 'UTF-8'}
r = requests.post(url, data=payload, headers=headers)
path = 'D:/tmp/'
file_name = path + 'clinic.txt'
with open(file_name, "wb") as output_file:
output_file.write(r.text.encode())
#####Part4 process the clinic and matrix
a.index = a.ENSEMBLE.tolist()
a1 = pd.DataFrame(a.values.T,index=a.columns, columns=a.index)
clinic = pd.read_table(file_name)
clinic.file_name = [re.sub('\.ht.+$','',x) for x in clinic.file_name]
df = pd.merge(clinic,a1,left_on='file_name',right_index = True)
df.index = df.loc[:,'cases.0.submitter_id'].tolist()
df_data = df.iloc[:,[('ENS' in x ) for x in df.columns]]
df_data.to_csv('D:/tmp/df.csv')