Introduction

Hierarchically clustered matrices commonly represent high-dimensional biological data and are widely used for visualization. Although methods exist to assess cluster stability, dendrogram-defined clusters are rarely used for statistical inference. HiMaLAYAS is a general framework for post hoc enrichment-based annotation of hierarchically clustered matrices. It treats dendrogram-defined clusters as statistical units, supports enrichment across dendrogram depths, and renders significant annotations alongside the matrix.

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.

Core Ideas

• Hierarchical clustering organizes rows and columns into contiguous regions that can be treated as clusters.
• Clusters are produced by cutting the dendrogram.
• Enrichment is tested across dendrogram-defined clusters and categorical annotations.
• Significant annotations are rendered alongside the matrix for interpretation.
• The workflow is domain-agnostic as long as you have a matrix and categorical annotations.
• Enrichment can be evaluated at different dendrogram depths by changing the cut threshold.

Typical Use Cases

• Saccharomyces cerevisiae genetic interaction matrices (Costanzo et al., 2016) with GO Biological Process enrichment.
• Expression similarity matrices with pathway enrichment.
• Non-biological matrices such as recipe-by-ingredient similarity with country-of-origin enrichment.

The Basic Object Graph

• Matrix: labeled numeric matrix used for clustering and plotting.
• Annotations: term-to-label mapping aligned to the matrix label background.
• Analysis: orchestrates clustering, enrichment, and layout.
• Results: stores enrichment output and context for plotting.
• Plotter: builds layered figures.