Effective visualization of biomedical data using plot-misc.

Authors

Amand Floriaan Schmidt, Nikita Hukerikar, Chris Finan, Marion van Vugt

Published in

Bioinformatics advances. Volume 6. Issue 1. Pages vbag184. Epub Jun 27, 2026.

Abstract

Computational visualizations are prevalent in all disciplines of biomedical science, from wet-lab research, through clinical science to population health and epidemiology. While matplotlib and seaborn are established Python tools for generating illustrations, both omit visualization archetypes commonly used in biomedical research. Producing such visualization using matplotlib's low-level interface may result in verbose, brittle code that demands considerable programming experience, and limits reuse between projects and users.
plot-misc is a Python package that is designed specifically for publication quality biomedical research visualization. It combines fine-grained control with archetype-based plotting, prioritizing customizable figure generation over integrated statistical routines. Available archetypes include forest plots, survival plots, volcano plots, heatmaps, and incidence matrices. Thanks to its matplotlib-first design, most Python users will be able to readily integrate plot-misc into existing routines. Online tutorials are available to onboard new users and provide robust code examples. Plot-misc provides a unified and flexible framework for creating high-quality, publication-ready illustrations that caters to the diverse visualization needs of modern biomedical researchers.
plot-misc is available on Conda, PyPi, as well as through GitLab: https://schmidtaf.gitlab.io/plot-misc.

PMID:
42524702
Bibliographic data and abstract were imported from PubMed on 29 Jul 2026.

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