mitoviz 0.4.1

Plot variants on the human mitochondrial genome.

mitoviz

Plot variants on the human mitochondrial genome.

• Free software: MIT license

• Documentation: https://mitoviz.readthedocs.io

• GitHub repo: https://github.com/robertopreste/mitoviz

Features

mitoviz is a simple python package to plot human mitochondrial variants on a graphical representation of the human mitochondrial genome. It currently supports plotting variants stored in VCF and tabular files, as well as from general pandas dataframes when using mitoviz from inside Python.

Variants are shown according to their heteroplasmic fraction (HF), plotting variants with HF = 1.0 on the outer border of the mitochondrial circle, those with HF = 0.0 on the inner border and all the others according to their actual HF value.

If the HF information is not available, variants will all be shown in the middle of the mitochondrial circle.

Usage

mitoviz can be used both from the command line and as a python module.

Command Line

Given a VCF file with human mitochondrial variants (sample.vcf), plotting them is fairly simple:

$ mitoviz sample.vcf

An image named mitoviz.png will be created in the current directory.

If you want to provide a specific filename where the plot will be saved, just add the --output option with the desired path:

$ mitoviz sample.vcf --output my_mt_plot.png

If the provided VCF file contains more than one sample, a separate plot will be created for each of them; if you want to only plot a specific sample, use the --sample option:

$ mitoviz multisample.vcf --sample SRR1777294

It is also possible to plot variants stored in a tabular file, such as CSV or TSV formats; mitoviz will automatically recognise them, treating the file as comma-separated by default. If a different separator is used (as in the case of TSV files), just specify it with the --sep option:

$ mitoviz sample.tsv --sep "\t"

Python Module

Import mitoviz and use its plot_vcf function to use it in your own script:

from mitoviz import plot_vcf

my_plot = plot_vcf("sample.vcf")

In this case, no plot will be shown until a call to plt.show() is made. It is possible to save the resulting plot using the save option and to provide a specific file where the plot will be saved using the output option:

plot_vcf("sample.vcf", save=True, output="my_mt_plot.png")

If the provided VCF file contains more than one sample, a separate plot will be created for each of them; if you want to only plot a specific sample, use the sample option:

plot_vcf("multisample.vcf", save=True, sample="SRR1777294")

A similar function to plot variants contained in a pandas DataFrame is available as plot_df. Supposing you have a pandas DataFrame with human mitochondrial variants named variants_df, it is possible to plot them as follows:

from mitoviz import plot_df

plot_df(variants_df)

Variants stored in tabular files can be plotted using plot_table, which accepts the same options available for plot_vcf and plot_df, with the addition of sep, which is used to specify the column separator. By default, the comma is used as column delimiter:

from mitoviz import plot_table

# plotting a CSV file
plot_table("sample.csv")
# plotting a TSV (tab-separated) file
plot_table("sample.tsv", sep="\t")

plot_table also accept additional keyword options, which will be passed to pandas.read_table when processing the given input file:

plot_table("sample.tsv", sep="\t", comment="#", skiprows=0)

Please refer to the Usage section of the documentation for further information.

Installation

PLEASE NOTE: HmtNote only supports Python >= 3.6!

The preferred installation method for mitoviz is using pip:

$ pip install mitoviz

Please refer to the Installation section of the documentation for further information.

Credits

This package was created with Cookiecutter and the cc-pypackage project template.

History

0.1.0 (2019-12-27)

• First release.

0.2.0 (2019-12-29)

• Add functionality to plot multiple samples.

0.2.1 (2020-01-06)

• Add legend to plots and update colors.

0.2.2 (2020-01-08)

• Add option to plot variant labels.

0.2.3 (2020-01-11)

• Make legend plotting optional.

0.3.0 (2020-01-15)

• Add plot_df function to plot variants from a pandas DataFrame.

0.4.0 (2020-01-26)

• Add plot_table function to plot variants from tabular files;

• add CLI functionality to plot variants from tabular files;

• refactor code.

0.4.1 (2020-02-13

• Refactor to use abstract classes;

• Rename internal classes to _PolarLocus and _PolarVariant.