Effective visualization of biomedical data using

Source: PubMed Central Open Access, NCBI / U.S. National Library of Medicine

Abstract Motivation Computational visualizations are prevalent in all disciplines of biomedical science, from wet-lab research, through clinical science to population health and epidemiology. Whileandare established Python tools for generating illustrations, both omit visualization archetypes commonly used in biomedical research. Producing such visualization usinglow-level interface may result in verbose, brittle code that demands considerable programming experience, and limits reuse between projects and users. s1 Results 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-first design, most Python users will be able to readily integrateinto existing routines. Online tutorials are available to onboard new users and provide robust code examples.provides a unified and flexible framework for creating high-quality, publication-ready illustrations that caters to the diverse visualization needs of modern biomedical researchers. s2 Availability and implementation is available on Conda, PyPi, as well as through GitLab:. s3