Hierarchical Matrix Layout and Annotation Software (HiMaLAYAS) is a 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. HiMaLAYAS supports both biological and non-biological domains.
For a full description of HiMaLAYAS and its applications, see:
Horecka, I., and Röst, H. (2026)
HiMaLAYAS: enrichment-based annotation and visualization of hierarchically clustered matrices
bioRxiv. https://www.biorxiv.org/content/10.64898/2026.02.11.705303v3
Submitted to Bioinformatics Advances.
- Full Documentation: himalayas-base.github.io/himalayas-docs
- Figure Gallery: himalayas-base.github.io/himalayas-docs/11_figure_gallery
- Try in Browser (Binder):
- Quickstart:
- Advanced Quickstart:
- Quickstart:
- Documentation Repository: github.com/himalayas-base/himalayas-docs
HiMaLAYAS is compatible with Python 3.8 or later and runs on major operating systems.
pip install himalayas
# Optional: faster clustering + richer compressed-label text processing
pip install "himalayas[speed,text]"Detailed installation options and fallback behavior are documented at himalayas-base.github.io/himalayas-docs/1_installation.
- Matrix-Based Input: Works with real-valued matrices representing relationships among observations.
- Configurable Clustering: Supports linkage method, distance metric, dendrogram distance threshold, and minimum cluster size settings.
- Cluster-Level Enrichment Testing: Treats dendrogram-defined clusters as statistical units and tests categorical annotations for enrichment.
- Multiple-Testing Control: Controls false discovery rate across cluster-term tests.
- Annotation-Aware Visualization: Renders significant annotations alongside clustered matrices.
- Zoomed and Condensed Views: Supports zoomed cluster reanalysis, condensed hierarchy views, and post hoc row data tracks.
- Publication-Ready Output: Exports configurable figures in raster or vector formats.
We applied HiMaLAYAS to a hierarchically clustered Saccharomyces cerevisiae genetic interaction profile similarity matrix (Costanzo et al., 2016), focusing on 1,053 genes with high profile variance. Dendrogram-defined clusters were tested for Gene Ontology Biological Process (GO BP; Ashburner et al., 2000) enrichment, with top-ranked significant annotations rendered alongside clusters.
HiMaLAYAS workflow and application to a hierarchically clustered yeast
genetic interaction profile similarity matrix (Costanzo et al., 2016).
A real-valued matrix and categorical annotations serve as inputs. HiMaLAYAS
hierarchically clusters the matrix, cuts the dendrogram at a user-defined
distance threshold, tests categorical annotations for enrichment, controls
multiple testing, and renders significant annotations alongside clusters.
Horecka, I., and Röst, H. (2026)
HiMaLAYAS: enrichment-based annotation and visualization of hierarchically clustered matrices
bioRxiv. https://www.biorxiv.org/content/10.64898/2026.02.11.705303v3
Submitted to Bioinformatics Advances.
HiMaLAYAS software archive.
Zenodo. https://doi.org/10.5281/zenodo.18610373
We welcome contributions from the community:
If you encounter issues or have suggestions for new features, please use the Issues Tracker on GitHub.
This project is distributed under the BSD 3-Clause License.