Robust Integrative Biclustering for Multi-view Data

TL;DR

This paper introduces iSSVD, a new multi-view biclustering method that improves stability, interpretability, and performance in identifying meaningful sample-variable groups in biomedical data.

Contribution

The paper extends sparse SVD biclustering to multi-view data, incorporating stability selection for better error control and interpretability.

Findings

iSSVD outperforms existing biclustering methods in simulations.

It effectively detects meaningful biclusters in real biomedical data.

The method is computationally efficient and user-friendly.

Abstract

In many biomedical research, multiple views of data (e.g., genomics, proteomics) are available, and a particular interest might be the detection of sample subgroups characterized by specific groups of variables. Biclustering methods are well-suited for this problem as they assume that specific groups of variables might be relevant only to specific groups of samples. Many biclustering methods exist for identifying row-column clusters in a view but few methods exist for data from multiple views. The few existing algorithms are heavily dependent on regularization parameters for getting row-column clusters, and they impose unnecessary burden on users thus limiting their use in practice. We extend an existing biclustering method based on sparse singular value decomposition for single-view data to data from multiple views. Our method, integrative sparse singular value decomposition (iSSVD), incorporates stability selection to control Type I error rates, estimates the probability of samples and variables to belong to a bicluster, finds stable biclusters, and results in interpretable row-column associations. Simulations and real data analyses show that iSSVD outperforms several other single- and multi-view biclustering methods and is able to detect meaningful biclusters. iSSVD is a user-friendly, computationally efficient algorithm that will be useful in many disease subtyping applications.