Unraveling the connection between genes and traits is crucial for solving many biological puzzles. Ribonucleic acid molecules and proteins, derived from these genetic instructions, play crucial roles in shaping cell structures, influencing reactions, and guiding behavior. This fundamental biological principle links genetic makeup to observable traits, but integrating and extracting meaningful relationships from this complex, multimodal data present a significant challenge.
We introduce evolSOM, a novel R package that allows exploring and visualizing the conservation or displacement of biological variables, easing the integration of phenotypic and genotypic attributes. It enables the projection of multi-dimensional expression profiles onto interpretable two-dimensional grids, aiding in the identification of conserved or displaced genes/phenotypes across multiple conditions. Variables displaced together suggest membership to the same regulatory network, where the nature of the displacement may hold biological significance. The conservation or displacement of variables is automatically calculated and graphically presented by evolSOM. Its user-friendly interface and visualization capabilities enhance the accessibility of complex network analyses.
The package is available at https://github.com/sanprochetto/evolSOM, along with a step-by-step vignette and a full example dataset that can be accessed at https://github.com/sanprochetto/evolSOM/tree/main/inst/extdata.
