Bayesian Bi-clustering Methods with Applications in Computational Biology

TL;DR

This paper introduces Bayesian bi-clustering models tailored for complex biological data, effectively identifying clusters and feature patterns in noisy, high-dimensional datasets, with demonstrated superiority over existing methods.

Contribution

The paper proposes three novel Bayesian bi-clustering models for categorical data, enhancing clustering accuracy and pattern recovery in high-dimensional biological datasets.

Findings

Models outperform existing clustering methods in simulations

Effective in noisy, high-dimensional, and hierarchical data scenarios

Successfully applied to genetic datasets

Abstract

Bi-clustering is a useful approach in analyzing biological data when observations come from heterogeneous groups and have a large number of features. We outline a general Bayesian approach in tackling bi-clustering problems in moderate to high dimensions, and propose three Bayesian bi-clustering models on categorical data, which increase in complexities in their modeling of the distributions of features across bi-clusters. Our proposed methods apply to a wide range of scenarios: from situations where data are cluster-distinguishable only among a small subset of features but masked by a large amount of noise, to situations where different groups of data are identified by different sets of features or data exhibit hierarchical structures. Through simulation studies, we show that our methods outperform existing (bi-)clustering methods in both identifying clusters and recovering feature distributional patterns across bi-clusters. We apply our methods to two genetic datasets, though the area of application of our methods is even broader.