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- WX公zhong号：生信学习者
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- 知hu：生信学习者
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介绍
用于复现Huang et al.研究分析的计算工作流程，所有复现数据和代码：生信学习者。
目录
- 累积分布函数Read质量检查：Reads quality inspection with cumulative distribution function
- Alpha多样性分析：Alpha diversity analysis
- Beta多样性分析：Beta diversity analysis
- 共现分析：Co-presence analysis
- 复杂热图绘制：ComplexHeatmap plotting
- 分组马赛克图：Make mosaic plot
- 机器学习分析：Machine learning analysis
- 偏差图、UpSet图、棒棒糖图：Deviation plot, UpSet plot, Lollipop plot
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
- 提取码: WX公zhong号生信学习者后台发送 复现msm 获取提取码
用法
提供了两种类型的实用工具：1) Python脚本；2) 可导入的R函数。
- Python脚本是封装好的代码，用于执行特定的分析。
- 可导入的R函数是包装好的R代码，用于解决特定问题，只需导入它们的脚本即可重用
可视化R包
可视化用到的R包
- ggpubr
- ComplexHeatmap
文章MSM_metagenomics（二）：累积分布函数Read质量检查
介绍
本教程介绍如何使用累积分布函数来检查宏基因组测序Reads的质量，特别是检查不同组样本中是否存在测序偏差。
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
- 提取码: WX公zhong号生信学习者后台发送 复现msm 获取提取码
Python 包
- Python >= 3.9.0
- scipy >= 1.8.1
- pandas >= 1.3.5
- numpy >= 1.23.5
- seaborn >= 0.11.2
- matplotlib >= 3.5.0
绘制Reads计数的累积分布函数
您将使用一个Python脚本cumulative_distribution_function.py，以及一个包含每个宏基因组样本中读取数量的表格，例如./data/example_reads_stats.tsv。
cumulative_distribution_function.py代码
#!/usr/bin/env python
import pandas as pd
from scipy.stats import ranksums
import seaborn as sns
import matplotlib.pyplot as plt
import matplotlib
import sys
import argparse
import textwrap
import numpy as np
"""
NAME: cumulative_distribution_function.py
DESCRIPTION: cumulative_distribution_function.py is a python script to draw cumulative curves.
AUTHOR: Kun D. Huang
Date: 29.10.2023
"""
def read_args(args):
# This function is to parse arguments
parser = argparse.ArgumentParser(formatter_class=argparse.RawDescriptionHelpFormatter,
description = textwrap.dedent('''
This program is to run draw cumulative curves.
'''),
epilog = textwrap.dedent('''
examples: cumulative_distribution_function.py --input_table <input_table.tsv> --output_figure <output.svg> --value_header <value_header>
'''))
parser.add_argument('--input_table',
nargs = '?',
help = 'Input a table containing values you would like to draw cumulative function on.',
type = str,
default = None)
parser.add_argument('--output_figure',
nargs = '?',
help = 'Specificy the output figure name.',
type = str,
default = None)
parser.add_argument('--value_header',
nargs = '?',
help = 'Specify the header name where values of interest are.',
type = str,
default = None)
parser.add_argument('--variable_header',
nargs = '?',
help = 'Specify the header name if these values are from different groups of a variable.',
type = str,
default = None)
parser.add_argument('--palette_map',
nargs = '?',
help = 'Give a palette map file where the first column is group names and second column is values.',
type = str,
default = None)
parser.add_argument('--font_style',
nargs = '?',
help = 'Specify the font style. default: [Arial]',
type = str,
default = "Arial")
parser.add_argument('--font_size',
nargs = '?',
help = 'Specify the font size. default: [11]',
type = str,
default = 11)
return vars(parser.parse_args())
def EvalCdf(sample, x):
count = 0.0
for value in sample:
if value <= x:
count = 1
prob = count / len(sample)
return prob
def make_cdf_table(input_df, value_header, variable_header = None):
cutoffs = np.linspace(0, max(input_df[value_header].to_list()), 10)
if variable_header:
groups = set(input_df[variable_header].to_list())
cdf_dfs = []
for group in groups:
group_df = pd.DataFrame([[group, cutoff, EvalCdf(input_df[input_df[variable_header] == group][value_header].to_list(), cutoff)] for cutoff in cutoffs],
columns = [variable_header, "Cutoffs", "CDFs"])
cdf_dfs.append(group_df)
cdf_df = pd.concat(cdf_dfs)
else:
cdf_df = pd.DataFrame([[cutoff, EvalCdf(input_df[value_header].to_list(), cutoff)] for cutoff in cutoffs],
columns = ["Cutoffs", "CDFs"])
return cdf_df
class VisualTools:
def __init__(self, processed_df):
self.processed_df = processed_df
def step_curves(self, opt_name, variable_header = None, palette = None, font_type = "Arial", font_size = 11):
fig, ax = plt.subplots()
font_family = 'sans-serif'
matplotlib.rcParams['font.family'] = font_family
matplotlib.rcParams['font.{}'.format(font_family)] = font_type
if palette:
palette_dict = {i.rstrip().split('t')[0]: i.rstrip().split('t')[1] for i in open(palette).readlines()}
if variable_header:
categories = list(set(self.processed_df[variable_header].to_list()))
for c in categories:
sub_df = self.processed_df[self.processed_df[variable_header] == c]
plt.step(sub_df["Cutoffs"], sub_df["CDFs"], label = c, color = palette_dict[c])
else:
plt.step(self.processed_df["Cutoffs"], self.processed_df["CDFs"])
else:
if variable_header:
categories = list(set(self.processed_df[variable_header].to_list()))
for c in categories:
sub_df = self.processed_df[self.processed_df[variable_header] == c]
plt.step(sub_df["Cutoffs"], sub_df["CDFs"], label = c)
else:
plt.step(self.processed_df["Cutoffs"], self.processed_df["CDFs"])
plt.xlabel("Values", fontsize = font_size)
plt.ylabel("Cumulative distribution function", fontsize = font_size)
if variable_header:
plt.legend(title = variable_header)
plt.xticks(fontsize = font_size)
plt.yticks(fontsize = font_size)
fig.savefig(opt_name, bbox_inches = "tight")
if __name__ == "__main__":
pars = read_args(sys.argv)
input_df = pd.read_csv(pars["input_table"], sep = "t", index_col = False)
CDF_df = make_cdf_table(input_df,
pars["value_header"],
pars["variable_header"])
vis_obj = VisualTools(CDF_df)
vis_obj.step_curves(pars["output_figure"],
pars["variable_header"],
pars["palette_map"],
pars["font_style"],
pars["font_size"])- usage of
cumulative_distribution_function.py
cumulative_distribution_function.py -h
usage: cumulative_distribution_function.py [-h] [--input_table [INPUT_TABLE]] [--output_figure [OUTPUT_FIGURE]] [--value_header [VALUE_HEADER]]
[--variable_header [VARIABLE_HEADER]] [--palette_map [PALETTE_MAP]] [--font_style [FONT_STYLE]] [--font_size [FONT_SIZE]]
This program is to run draw cumulative curves.
optional arguments:
-h, --help show this help message and exit
--input_table [INPUT_TABLE]
Input a table containing values you would like to draw cumulative function on.
--output_figure [OUTPUT_FIGURE]
Specificy the output figure name.
--value_header [VALUE_HEADER]
Specify the header name where values of interest are.
--variable_header [VARIABLE_HEADER]
Specify the header name if these values are from different groups of a variable.
--palette_map [PALETTE_MAP]
Give a palette map file where the first column is group names and second column is values.
--font_style [FONT_STYLE]
Specify the font style. default: [Arial]
--font_size [FONT_SIZE]
Specify the font size. default: [11]
examples:
python cumulative_distribution_function.py --input_table <input_table.tsv> --output_figure <output.svg> --value_header <value_header>示例命令 1:
代码语言：javascript复制python cumulative_distribution_function.py
--input_table example_reads_stats.tsv
--output_figure nr_QC_reads_pairs.svg
--value_header nr_QC_reads_pairs
--palette_map reads_stats_color_map.tsv示例命令 2:
代码语言：javascript复制python cumulative_distribution_function.py
--input_table example_reads_stats.tsv
--output_figure nr_raw_reads_pairs.svg
--value_header nr_raw_reads_pairs
--palette_map reads_stats_color_map.tsv如果您希望为不同的变量类别指定特定的颜色，例如将灰色指定给MSM（男男性行为者），将红色指定给Non-MSM（非男男性行为者），您可以使用一个颜色调色板映射，如./data/reads_stats_color_map.tsv。
