1. 病情说明：

第一次做宏基因组相关的分析的时候，我是基于kraken2这个方法的，然后又是组装，定量，转biom等等。是一个多步骤的流程呢。怎么说呢，各有各的好处吧，具体有哪些不同我们还是需要百度一下kraken 和metaphlan这两个方法。

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2. 治疗方案

import time
import threading
import argparse
import os
from pickle import FALSE
import threading
import shutil
import datetime
import json
import time
import subprocess
import pandas as pd
method_path = 'plot_scripts'
refer_path = 'DB'
# 1: check function
def metaphlan_check(data_path: str = None,                ## String: path to knead 
                    input_type: str = None,               ## default: fastq,{fastq,fasta,bowtie2out,sam}
                    min_mapq_val = 5,                     ## default [5],choose 6,7,8 Minimum mapping quality value
                    tax_lev: str = None,                  ## default : 'a', choose 'g','s',The taxonomic level for the relative abundance output
                    stat: str = None,                     ## [default tavg_g], Statistical approach for converting marker abundances into clade abundances,
                                                              #or choose 'avg_g','avg_l','tavg_g','tavg_l','wavg_g','wavg_l','med'                   
                    out_path: str = None                  ## String: output path for knead procedure
):
    ## check knead file
    check_file = f'{data_path}/knead_call_file.txt'
    if not os.path.exists(check_file):
        raise SystemExit('No knead file! Please run knead module!')
    else:
        knead_param = pd.read_csv(check_file, header=None)
        cleandata_path = knead_param.at[0,0]
        sampleinfo = knead_param.at[1,0]
    inputtype = f'{input_type}'
    min_mapq_val = f'{min_mapq_val}'
    tax_lev = f"'{tax_lev}' "
    stat = f"'{stat}' "
    
    if not os.path.exists(out_path):
        os.makedirs(out_path, exist_ok=True)
    else:
        pass
    ## output
    out_path = os.path.abspath(out_path)

    ## mataphlan v3 database 
    mataphlan_db = refer_path + '/metaphlan_databasesV3'
    metaphlan_path = os.path.abspath(mataphlan_db)
    
    check_file_path = f'{out_path}/metaphlan_check_file.txt'
    check_file = pd.DataFrame([
        cleandata_path,
        sampleinfo,
        inputtype,
        min_mapq_val,
        tax_lev,
        stat,
        out_path,
        metaphlan_path
    ])
    check_file.to_csv(check_file_path,
                      header=None, index=None, sep='\t', mode='w')
    return 0
    data_path = 'kneaddout2'           
input_type= 'fastq'          ## default: fastq,{fastq,fasta,bowtie2out,sam}
min_mapq_val = 5                   ## default [5],choose 6,7,8 Minimum mapping quality value
tax_lev= 'a'                 ## default : 'a', choose 'g','s',The taxonomic level for the relative abundance output
stat = 'tavg_g'                  ## [default tavg_g], Statistical approach for converting marker abundances into clade abundances,                              #or choose 'avg_g','avg_l','tavg_g','tavg_l','wavg_g','wavg_l','med'                   
out_path = 'metaphlanout'

metaphlan_check(data_path,input_type,min_mapq_val,tax_lev,stat,out_path)
def execCmd(cmd):
    try:
        print("命令{}开始运行{}".format(cmd, datetime.datetime.now()))
        time.sleep(5)
        os.system(cmd)
        print("命令{}结束运行{}".format(cmd, datetime.datetime.now()))
    except:
        print("{}\t运行失败".format(cmd)) 
def metaphlan_call(
    out_path: str = None                  ## output of metaphlan
):
    ## check metaphlan_check file
    metaphlan_check_path = f'{out_path}/metaphlan_check_file.txt'
    if not os.path.exists(metaphlan_check_path):
        raise SystemExit(' No metaphlan_check_file  ! Please run metaphlan_check !')
    else:
        metaphlan_check = pd.read_csv(f'{out_path}/metaphlan_check_file.txt', 
                                      header=None,delimiter='\t')
        cleandata_path = metaphlan_check.at[0,0]
        sampleinfo = metaphlan_check.at[1,0]
        inputtype =metaphlan_check.at[2,0]
        min_mapq_val= metaphlan_check.at[3,0]
        tax_lev = metaphlan_check.at[4,0]
        stat = metaphlan_check.at[5,0]
        out_path = metaphlan_check.at[6,0]
        metaphlan_path = metaphlan_check.at[7,0]
        
    sample_info_dic = {}
    cmds = []
    for line in open(sampleinfo):
        if not line.startswith("sample"):
            line=line.strip().split()
            sample_info_dic[line[0]]=line[1]
            cmd = f'''/root/miniconda3/envs/Rdoc/bin/metaphlan --input_type {inputtype} \
            {cleandata_path}/{line[0]}/{line[0]}.fastq \
            --bowtie2db {metaphlan_path} \
            --output_file {out_path}/{line[0]}.tsv \
            --tmp_dir {out_path} --min_mapq_val {min_mapq_val} \
            --tax_lev {tax_lev} --stat {stat} --nproc 8 \
            --read_min_len 70 --no_map '''
        cmds.append(cmd)
    threads = []
    for cmd in cmds:
        th = threading.Thread(target=execCmd, args=(cmd,))
        th.start()
        th.join()
        time.sleep(2)
        threads.append(th)

    time.sleep(10)
    
    ## merge all sample 
    merge_cmd = f'/root/miniconda3/envs/Rdoc/bin/merge_metaphlan_tables.py {out_path}/*.tsv \
     > {out_path}/merged_abundance_table.txt'
    execCmd(merge_cmd)

    ## define output file
    metaphlan_outfile = out_path+ '/merged_abundance_table.txt'
    metaphlan_outfile_path = os.path.abspath(metaphlan_outfile)

    call_file_path = f'{out_path}/metaphlan_call_file.txt'
    call_file = pd.DataFrame([metaphlan_outfile_path])

    call_file.to_csv(call_file_path,
                      header=None, index=None, sep='\t', mode='w')
            
    return 0           

3. 补充说明

最后文件夹会有这么几个文件噢,测试数据已经打包好啦，docker一键部署。持续关注噢，因为我会持续更新的。

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