HiMaLAYAS: enrichment-based annotation of hierarchically clustered matrices

Abstract

Summary: Hierarchical clustering reveals structure in high-dimensional biological matrices, but the resulting dendrogram-defined clusters are commonly visualized rather than tested for annotation enrichment. Existing workflows often require separate tools to cluster data, test enrichment, and display heatmaps. Here, we introduce Hierarchical Matrix Layout and Annotation Software (HiMaLAYAS), a Python framework for post hoc enrichment-based annotation and visualization of hierarchically clustered matrices. HiMaLAYAS treats dendrogram-defined clusters as statistical units, tests categorical annotations for enrichment, controls multiple testing, and renders significant annotations alongside clusters in a single workflow. Applied to a Saccharomyces cerevisiae genetic interaction profile similarity matrix, HiMaLAYAS annotated biological-process enrichment at parent-cluster and zoomed-cluster levels. Parent-level annotations persisted across clustering-parameter sweeps, exceeded null expectations from randomized cluster membership and annotation labels, and remained stable under matrix perturbation. HiMaLAYAS also annotated clusters in a non-biological country-sector input-output matrix, extending its application beyond biology. Availability and Implementation: HiMaLAYAS is a Python package available via pip, distributed under the BSD 3-Clause License at https://github.com/himalayas-base/himalayas, and archived on Zenodo at https://doi.org/10.5281/zenodo.18610373.