文章MSM_metagenomics（五）：共现分析

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介绍

本教程是使用一个Python脚本来分析多种微生物（即strains, species, genus等）的共现模式。

数据

大家通过以下链接下载数据：

• 百度网盘链接：https://pan.baidu.com/s/1f1SyyvRfpNVO3sLYEblz1A
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Python packages required

• pandas >= 1.3.5
• matplotlib >= 3.5.0
• seaborn >= 0.11.2

Co-presence pattern analysis

使用step_curve_drawer.py 做共线性分析

• 代码

#!/usr/bin/env python"""
NAME: step_curve_drawer.py
DESCRIPTION: This script is to analyze the co-prsense of multiple species in different categories,by drawing step curves.
"""import pandas as pd
import seaborn as sns
import matplotlib.pyplot as plt
import sys
import argparse
import textwrapdef read_args(args):# This function is to parse argumentsparser = argparse.ArgumentParser(formatter_class=argparse.RawDescriptionHelpFormatter,description = textwrap.dedent('''\This program is to do draw step curves to analyze co-presense of multiple species in different groups.'''),epilog = textwrap.dedent('''\examples:step_curve_drawer.py --abundance_table <abundance_table_w_md.tsv> --variable <variable_name> --species_number <nr_sps> --output <output.svg>'''))parser.add_argument('--abundance_table',nargs = '?',help = 'Input the MetaPhlAn4 abundance table which contains only a group of species one wants to analyze their co-presense state, with metadata being wedged.',type = str,default = None)parser.add_argument('--variable',nargs = '?',help = 'Specify the header of the variable in the metadata table you want to assess. For example, \[Diet] variable columns has three categries - [vegan]/[Flexitarian]/[Omnivore].',type = str,default = None)parser.add_argument('--minimum_abundance',nargs = '?',help = 'Specify the minimum abundance used for determining presense. note: [0, 100] and [0.0] by default',type = float,default = 0.0)parser.add_argument('--species_number',nargs = '?',help = 'Specify the total number of multiple species in the analysis.',type = int)parser.add_argument('--output',nargs = '?',help = 'Specify the output figure name.',type = str,default = None)parser.add_argument('--palette',nargs = '?',help = 'Input a tab-delimited mapping file where values are group names and keys are color codes.',type = str,default = None)return vars(parser.parse_args())class PandasDealer:"""This is an object for dealing pandas dataframe."""def __init__(self, df_):self.df_ = df_def read_csv(self):# Ths fucntion will read tab-delimitted file into a pandas dataframe.return pd.read_csv(self.df_, sep = '\t', index_col = False, low_memory=False)def rotate_df(self):# this function is to rotate the metaphlan-style table into tidy dataframe to ease searching work,df_ = self.read_csv()df_rows_lists = df_.values.tolist()rotated_df_dict = {df_.columns[0]: df_.columns[1:]}for i in df_rows_lists:rotated_df_dict[i[0]] = i[1:]rotated_df = pd.DataFrame.from_dict(rotated_df_dict)return rotated_dfclass CopEstimator:def __init__(self, sub_df_md):self.sub_df_md = sub_df_md # sub_df_md: a subset of dataframe which contains only a group of species one wants to do co-presence analysis.def make_copresense_df(self, factor, total_species_nr, threshold = 0.0):# factor: the factor you want to assess the category percentage.# total_species_nr: specify the total number of species you want to do co-presense analysis.rotated_df = PandasDealer(self.sub_df_md)rotated_df = rotated_df.rotate_df()cols = rotated_df.columns[-total_species_nr: ].to_list() categories = list(set(rotated_df[factor].to_list()))copresense = []cate_name = []ratios = []for c in categories:sub_df = rotated_df[rotated_df[factor] == c]species_group_df = sub_df[cols]species_group_df = species_group_df.apply(pd.to_numeric)species_group_df['total'] = species_group_df[cols].gt(threshold).sum(axis=1)for i in range(1, total_species_nr + 1):ratio = count_non_zero_rows(species_group_df, i)copresense.append(i)cate_name.append(c)ratios.append(ratio)return pd.DataFrame.from_dict({"copresense": copresense,factor: cate_name,"percentage": ratios})def count_non_zero_rows(df_, nr):total_rows = len(df_.index)sub_df = df_[df_['total'] >= nr]ratio = len(sub_df.index)/total_rowsreturn ratioclass VisualTools:def __init__(self, processed_df, factor):self.processed_df = processed_dfself.factor = factordef step_curves(self, opt_name, palette = None):categories = list(set(self.processed_df[self.factor].to_list()))if palette:palette_dict = {i.rstrip().split('\t')[0]: i.rstrip().split('\t')[1] for i in open(palette).readlines()}for c in categories:sub_df = self.processed_df[self.processed_df[self.factor] == c]plt.step(sub_df["percentage"]*100, sub_df["copresense"], label = c, color = palette_dict[c])else:for c in categories:sub_df = self.processed_df[self.processed_df[self.factor] == c]plt.step(sub_df["percentage"]*100, sub_df["copresense"], label = c)plt.title("Number of species in an individual if present")plt.xlabel("Percentage")plt.ylabel("Co-presense")plt.legend(title = self.factor)plt.savefig(opt_name, bbox_inches = "tight")if __name__ == "__main__":pars = read_args(sys.argv)cop_obj = CopEstimator(pars['abundance_table'])p_df = cop_obj.make_copresense_df(pars['variable'], pars['species_number'], pars['minimum_abundance'])vis_obj = VisualTools(p_df, pars['variable'])vis_obj.step_curves(pars['output'], palette = pars['palette'])

• 用法

usage: step_curve_drawer.py [-h] [--abundance_table [ABUNDANCE_TABLE]] [--variable [VARIABLE]] [--minimum_abundance [MINIMUM_ABUNDANCE]] [--species_number [SPECIES_NUMBER]] [--output [OUTPUT]][--palette [PALETTE]]This program is to do draw step curves to analyze co-presense of multiple species in different groups.optional arguments:-h, --help            show this help message and exit--abundance_table [ABUNDANCE_TABLE]Input the MetaPhlAn4 abundance table which contains only a group of species one wants to analyze their co-presense state, with metadata being wedged.--variable [VARIABLE]Specify the header of the variable in the metadata table you want to assess. For example, [Diet] variable columns has three categries - [vegan]/[Flexitarian]/[Omnivore].--minimum_abundance [MINIMUM_ABUNDANCE]Specify the minimum abundance used for determining presense. note: [0, 100] and [0.0] by default--species_number [SPECIES_NUMBER]Specify the total number of multiple species in the analysis.--output [OUTPUT]     Specify the output figure name.--palette [PALETTE]   Input a tab-delimited mapping file where values are group names and keys are color codes.examples:python step_curve_drawer.py --abundance_table <abundance_table_w_md.tsv> --variable <variable_name> --species_number <nr_sps> --output <output.svg>

为了演示step_curve_drawer.py的使用，我们将绘制基于metaphlan相对丰度表特定于Segatalla copri（之前称为Prevotella copri）的八个谱系：./data/mpa4_pcopri_abundances_md.tsv的共现模式，这些数据来自MSM和Non-MSM人群。MSM和Non-MSM样本将使用自定义颜色进行标记，颜色分配来自一个颜色映射文件color map file: ./data/copresence_color_map.tsv。

python step_curve_drawer.py \--abundance_table mpa_pcopri_abundances_md.tsv \--variable sexual_orientation \--species_number 8 \--palette copresence_color_map.tsv \--output copresence_plot.png

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