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Effective visualization of biomedical data using plot-misc
Amand Floriaan Schmidt1,2,3, Nikita Hukerikar1,2, Chris Finan1,2
1Institute of Cardiovascular Science, Faculty of Population Health, University College London, London WC1E 6HX, United Kingdom.
Bioinformatics Advances
|July 29, 2026
Summary
A new Python package, plot-misc, simplifies biomedical data visualization. It offers specialized plot archetypes for researchers, enhancing publication quality and code reusability.
Area of Science:
- Biomedical research
- Data visualization
- Scientific computing
Background:
- Computational visualizations are essential across biomedical disciplines, including wet-lab research, clinical science, and epidemiology.
- Existing Python tools like matplotlib and seaborn lack common biomedical visualization archetypes.
- Generating specialized plots with low-level interfaces is complex and limits code reuse.
Purpose of the Study:
- Introduce plot-misc, a Python package for publication-quality biomedical research visualization.
- Provide a user-friendly solution for creating complex biomedical plots.
- Facilitate integration into existing Python workflows.
Main Methods:
- Developed plot-misc as a Python package leveraging matplotlib.
- Implemented archetype-based plotting for common biomedical figures (forest plots, survival plots, volcano plots, heatmaps, incidence matrices).
- Prioritized customizable figure generation over integrated statistical functions.
Main Results:
- plot-misc offers a unified framework for diverse biomedical visualization needs.
- The package enables fine-grained control combined with archetype-based plotting.
- It is designed for easy integration by Python users familiar with matplotlib.
Conclusions:
- plot-misc addresses the gap in specialized visualization tools for biomedical research.
- The package promotes high-quality, publication-ready figures with enhanced code reusability.
- plot-misc is accessible via Conda, PyPi, and GitLab, with online tutorials available.
