Effective visualisation of biomedical data using plot-misc

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ID: 319142
2026
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Abstract
Abstract Motivation Computational visualisations 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 visualisation archetypes commonly used in biomedical research. Producing such visualisation using matplotlib’s low-level interface may result in verbose, brittle code that demands considerable programming experience, and limits reuse between projects and users. Results plot-misc is a Python package that is designed specifically for publication quality biomedical research visualisation. It combines fine-grained control with archetype-based plotting, prioritising customisable 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 visualisation needs of modern biomedical researchers. Availability and implementation plot-misc is available on Conda, PyPi, as well as through GitLab: https://schmidtaf.gitlab.io/plot-misc.
Reference Key
openalex_W7166535727 Use this key to autocite in the manuscript while using SciMatic Manuscript Manager or Thesis Manager
Authors A Floriaan Schmidt, Nikita Hukerikar, Chris Finan, Marion van Vugt
Journal Bioinformatics advances
Year 2026
DOI
10.1093/bioadv/vbag184
URL
https://doi.org/10.1093/bioadv/vbag184
Keywords Keywords not found

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