EvolSOM: An R Package for evolutionary conservation analysis with SOMs

Abstract

Motivation: Unraveling the connection between genes and traits is crucial for solving many biological puzzles. In the intricate structure of DNA, genes provide instructions for building cellular machinery, directing the processes that sustain life. RNA 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 presents a significant challenge. Results: We introduce evolSOM, a novel R package that utilizes Self-Organizing Maps (SOMs) to explore and visualize the conservation of biological variables, easing the integration of phenotypic and genotypic attributes. The package enables the projection of multi-dimensional expression profiles onto easily interpretable two-dimensional grids, aiding in the identification of conserved gene modules/phenotypes across multiple species or conditions. By constructing species-specific or condition-specific SOMs that capture non-redundant patterns, evolSOM allows the analysis of displacement of biological variables between species or conditions. Variables displaced together suggest membership in the same regulatory network, and the nature of the displacement (early/delayed/flip) may hold biological significance. These displacements are automatically calculated and graphically presented by the package, enabling efficient comparison and revealing both conserved and displaced variables. The package facilitates the integration of diverse phenotypic data types, such as morphological data or metabolomics, enabling the exploration of potential gene drivers underlying observed phenotypic changes. Its user-friendly interface and visualization capabilities enhance the accessibility of complex network analyses. As an illustrative example, we employed evolSOM to study the displacement of genes and phenotypic traits, successfully identifying potential drivers of phenotypic differentiation in grass leaves.