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added mosaic plot tutorial
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‎README.md

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* [Beta diversity analysis](./docs/beta_diversity_analysis.md)
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* [Co-presence analysis](./docs/copresence_analysis.md)
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* [ComplexHeatmap plotting](./docs/make_ComplexHeatmap.md)
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* [Make mosaic plot](./docs/make_mosaic_plot.md)

‎docs/make_mosaic_plot.md

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# Make mosaic plot
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This tutorial is to use a python script to draw a mosaic plot for visualizing frequency distribution of two variables.
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#### Python packages required
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* [Pandas](https://pandas.pydata.org/)
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* [SciPy](https://scipy.org/)
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* [Matplotlib](https://matplotlib.org/)
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* [statsmodels](https://www.statsmodels.org/stable/index.html)
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##### Drawing a mosaic plot using `mosaic_plot.py`
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You will use a python script [mosaic_plot.py](../scripts/mosaic_plot.py) in the path `path_to_the_package/KunDH-2023-CRM-MSM_metagenomics/scripts/`, and a table containing species associated with *MSM* and *Non-MSM* individuals which were identified as Gram-negative or not in [two_variable_mosaic.tsv](../example_data/two_variable_mosaic.tsv).
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```{python}
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usage: mosaic_plot.py [-h] [--input [INPUT]] [--facecolor_map [FACECOLOR_MAP]] [--font_style [FONT_STYLE]] [--output [OUTPUT]]
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This program is to draw a mosaic plot.
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optional arguments:
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-h, --help show this help message and exit
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--input [INPUT] Input a file containing two variable information regarding each individual subject.
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--facecolor_map [FACECOLOR_MAP]
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Specify the the pathway to SCFA metabolisms database. default: /vol/projects/khuang/databases/SCFA/SCFA_pathways.tsv
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--font_style [FONT_STYLE]
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Specify the font style, font family and font type is delimited by a comma. default: [sans-serif,Arial]
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--output [OUTPUT] Specify the output figure name.
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examples: mosaic_plot.py --input input_file.tsv --facecolor_map facecolor_mapfile.tsv --output mosaic_plot.png
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```
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Example command:
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```{bash}
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mosaic_plot.py --input two_variable_mosaic.tsv --facecolor_map facecolor_map.tsv --output mosaic_plot.png
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```
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![Mosaic plot](../images/)
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*Note*
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The face color of mosaic plot should be specified as in the example [mapping file](../example_data/facecolor_map.tsv).
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‎example_data/facecolor_map.tsv

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MSM grey
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Non-MSM red

‎example_data/two_variable_mosaic.tsv

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SGB Sexuality Gram_negative
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SGB1667 MSM No
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SGB15090 MSM Yes
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SGB1662 MSM No
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SGB1676 MSM No
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SGB5066 MSM Yes
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SGB1653 MSM No
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SGB1666 MSM No
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SGB13999 MSM Yes
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SGB15107 MSM Yes
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SGB9333 MSM No
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SGB1675 MSM No
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SGB1668 MSM No
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SGB1883_group MSM No
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SGB5904 MSM No
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SGB1928 MSM No
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SGB1437 MSM No
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SGB6817 MSM Yes
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SGB9274 MSM No
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SGB1657 MSM No
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SGB2230 MSM No
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SGB2240 MSM No
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SGB4367 MSM Yes
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SGB5967 MSM No
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SGB6973 MSM No
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SGB15260 MSM Yes
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SGB5858 MSM No
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SGB1699 MSM No
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SGB2238 MSM No
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SGB1626 MSM No
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SGB15225 MSM Yes
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SGB5239 MSM Yes
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SGB15244 MSM Yes
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SGB15106 MSM Yes
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SGB3677 MSM No
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SGB4886 MSM Yes
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SGB1680 MSM No
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SGB15322 MSM Yes
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SGB1474 MSM No
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SGB1475 MSM No
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SGB1472 MSM No
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SGB9243 MSM No
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SGB1672_group MSM No
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SGB1701 MSM No
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SGB2241 MSM No
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SGB15093 MSM Yes
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SGB1663 MSM No
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SGB1614 MSM No
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SGB9209 MSM No
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SGB4893 MSM Yes
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SGB1636_group MSM No
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SGB1644 MSM No
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SGB4910 MSM Yes
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SGB1673 MSM No
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SGB4909 MSM Yes
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SGB15370 MSM Yes
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SGB2239 MSM No
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SGB1677 MSM No
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SGB1617_group MSM No
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SGB1861 Non-MSM No
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SGB4820 Non-MSM Yes
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SGB2286 Non-MSM No
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SGB1790 Non-MSM No
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SGB1965 Non-MSM No
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SGB9340 Non-MSM No
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SGB17256 Non-MSM Yes
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SGB1798 Non-MSM No
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SGB15342 Non-MSM Yes
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SGB1613 Non-MSM No
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SGB5190 Non-MSM Yes
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SGB9262 Non-MSM No
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SGB15120 Non-MSM Yes
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SGB1877 Non-MSM No
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SGB9347 Non-MSM No
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SGB1815 Non-MSM No
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SGB17248 Non-MSM Yes
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SGB4584 Non-MSM Yes
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SGB15132 Non-MSM Yes
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SGB1862 Non-MSM No
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SGB4557 Non-MSM Yes
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SGB1836_group Non-MSM No
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SGB2290 Non-MSM No
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SGB8601 Non-MSM No
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SGB4269 Non-MSM Yes
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SGB17237 Non-MSM Yes
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SGB4608 Non-MSM Yes
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SGB14993_group Non-MSM Yes
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SGB2071 Non-MSM No
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SGB1934 Non-MSM No
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SGB4422 Non-MSM Yes
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SGB5792 Non-MSM No
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SGB4714 Non-MSM Yes
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SGB1814 Non-MSM No
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SGB2303 Non-MSM No
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SGB2318 Non-MSM No
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SGB2301 Non-MSM No
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SGB4705 Non-MSM Yes
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SGB4874 Non-MSM Yes

‎images/mosaic_plot.png

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‎scripts/mosaic_plot.py

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#!/usr/bin/env python
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"""
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NAME: mosaic_plot.py
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DESCRIPTION: mosaic_plot.py is a python script for visualizing proportions of data points along two variables.
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DATE: 29.11.2023
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AUTHOR: Kun D. Huang
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"""
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import pandas as pd
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from scipy.stats import fisher_exact
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import matplotlib.pyplot as plt
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from statsmodels.graphics.mosaicplot import mosaic
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import matplotlib
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import sys
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import argparse
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import textwrap
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def make_mosaic_plot(two_variable_file, facecolor_dict, output_fig, font_style = "sans-serif,Arial"):
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font_family, font_type = font_style.split(",")
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matplotlib.rcParams['font.family'] = font_family
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matplotlib.rcParams['font.sans-serif'] = font_type
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two_variable_df = pd.read_csv(two_variable_file, sep = "\t", index_col = False)
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features, variable1, variable2 = two_variable_df.columns
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cont_df = pd.crosstab(two_variable_df[variable1], two_variable_df[variable2])
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res = fisher_exact(cont_df, alternative = "two-sided")
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label_dict = {}
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for idx in cont_df.index.to_list():
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for col in cont_df.columns.to_list():
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label_dict[(idx, col)] = cont_df.loc[idx, col]
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labelizer = lambda k:label_dict[k]
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variable2_0, variable2_1 = sorted(set(two_variable_df[variable2].to_list()))
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props = {}
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for variable in facecolor_dict:
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props[(variable, variable2_0)] = {"facecolor": facecolor_dict[variable], "edgecolor": "white"}
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props[(variable, variable2_1)] = {"facecolor": facecolor_dict[variable], "edgecolor": "white"}
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mosaic(two_variable_df, [variable1, variable2], labelizer = labelizer, properties = props, title = " P-value: "+ str(res[1]) + " (Fisher's exact test)")
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plt.savefig(output_fig)
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if __name__ == "__main__":
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def read_args(args):
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# This function is to parse arguments
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parser = argparse.ArgumentParser(formatter_class=argparse.RawDescriptionHelpFormatter,
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description = textwrap.dedent('''\
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This program is to draw a mosaic plot.
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'''),
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epilog = textwrap.dedent('''\
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examples: mosaic_plot.py --input input_file.tsv --facecolor_map facecolor_mapfile.tsv --output mosaic_plot.png
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'''))
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parser.add_argument('--input',
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nargs = '?',
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help = 'Input a file containing two variable information regarding each individual subject.',
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type = str,
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default = None)
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parser.add_argument('--facecolor_map',
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nargs = '?',
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help = 'Specify the the pathway to SCFA metabolisms database. default: /vol/projects/khuang/databases/SCFA/SCFA_pathways.tsv',
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default = '/vol/projects/khuang/databases/SCFA/SCFA_pathways.tsv')
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parser.add_argument('--font_style',
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nargs = '?',
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help = 'Specify the font style, font family and font type is delimited by a comma. default: [sans-serif,Arial]',
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default = 'sans-serif,Arial')
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parser.add_argument('--output',
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nargs = '?',
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help = 'Specify the output figure name.',
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type = str,
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default = None)
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return vars(parser.parse_args())
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pars = read_args(sys.argv)
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facecolor_dict = {i.rstrip().split("\t")[0]: i.rstrip().split("\t")[1] for i in open(pars['facecolor_map']).readlines()}
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make_mosaic_plot(pars["input"], facecolor_dict , pars["output"], font_style = pars["font_style"])
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