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Hierarchical Matrix Layout and Annotation Software

Project description

HiMaLAYAS

Python PyPI License

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 their matrix regions. HiMaLAYAS supports both biological and non-biological domains.

For a full description of HiMaLAYAS and its applications, see:
Horecka, I. and Rost, H. (unpublished). HiMaLAYAS: enrichment-based annotation of hierarchically clustered matrices. Manuscript in preparation.

Documentation and Tutorial

Full documentation is available at:

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.

Installation

HiMaLAYAS is compatible with Python 3.8 or later and runs on major operating systems. To install the latest version, run:

pip install himalayas --upgrade

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 overview 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Horecka, I. and Rost, H. (unpublished) HiMaLAYAS: enrichment-based annotation of hierarchically clustered matrices. Manuscript in preparation.

Software archive

No Zenodo archive is available yet.

Contributing

We welcome contributions from the community:

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.

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