himalayas 0.0.14

Hierarchical Matrix Layout and Annotation Software

HiMaLAYAS

Hierarchical Matrix Layout and Annotation Software (HiMaLAYAS) is a framework for post hoc enrichment-based annotation of hierarchically clustered matrices. HiMaLAYAS treats dendrogram-defined clusters as statistical units, evaluates annotation enrichment, and renders significant annotations alongside the matrix. 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 of hierarchically clustered matrices
bioRxiv. https://doi.org/10.64898/2026.02.11.705303
Submitted to Bioinformatics Advances.

Documentation and Tutorial

• Full Documentation: himalayas-base.github.io/himalayas-docs
• Try in Browser (Binder):
  • Quickstart:
  • Advanced Quickstart:
• Documentation Repository: github.com/himalayas-base/himalayas-docs

Installation

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.

Key Features of HiMaLAYAS

• Real-Valued Matrix Input: Operates on real-valued matrices encoding relationships among observations.
• Depth-Aware Cluster Definition: Cuts the dendrogram at a user-defined depth to define dendrogram-defined clusters for downstream analysis.
• Overrepresentation Testing: Uses a one-sided hypergeometric test to evaluate term enrichment in each cluster against the matrix background.
• Multiple-Testing Control: Supports Benjamini-Hochberg false discovery rate (FDR) correction for cluster-term tests.
• Annotation Mapping and Rendering: Maps significant annotations onto the clustered matrix and supports publication-ready matrix visualizations.

Example Usage

We applied HiMaLAYAS to a hierarchically clustered Saccharomyces cerevisiae genetic interaction profile similarity matrix (Costanzo et al., 2016), focusing on genes with high profile variance. Dendrogram-defined clusters were tested for Gene Ontology Biological Process (GO BP; Ashburner et al., 2000) enrichment, revealing hierarchical organization of biological processes.

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. The matrix is cut at a user-defined depth, and each dendrogram-defined cluster is evaluated for GO BP enrichment.

Citation

Primary citation

Horecka, I., and Röst, H. (2026)
HiMaLAYAS: enrichment-based annotation of hierarchically clustered matrices
bioRxiv. https://doi.org/10.64898/2026.02.11.705303
Submitted to Bioinformatics Advances.

Software archive

HiMaLAYAS software for the bioRxiv preprint.
Zenodo. https://doi.org/10.5281/zenodo.18627073

Contributing

We welcome contributions from the community:

• Issues Tracker
• Source Code

Support

If you encounter issues or have suggestions for new features, please use the Issues Tracker on GitHub.

License

This project is distributed under the BSD 3-Clause License.