Biclustering Methods via Sparse Penalty

TL;DR

This paper introduces a novel sparse penalty called Prenet for biclustering in gene expression data, demonstrating its effectiveness through simulations and real data analysis.

Contribution

It proposes the Prenet penalty for biclustering, extending sparse SVD methods, and evaluates its performance on simulated and real gene expression datasets.

Findings

Prenet penalty improves biclustering accuracy for non-overlapped data

Effective in both 1-layer and multi-layer approximations

Demonstrated success on real gene expression data

Abstract

In this paper, we first reviewed several biclustering methods that are used to identify the most significant clusters in gene expression data. Here we mainly focused on the SSVD(sparse SVD) method and tried a new sparse penalty named "Prenet penalty" which has been used only in factor analysis to gain sparsity. Then in the simulation study, we tried different types of generated datasets (with different sparsity and dimension) and tried 1-layer approximation then for k-layers which shows the mixed Prenet penalty is very effective for non-overlapped data. Finally, we used some real gene expression data to show the behavior of our methods.