Interrelated Two-way Clustering: An Unsupervised Approach for Gene Expression Data Analysis

DNA arrays can be used to measure the expression levels of thousands of genes simultaneously. Currently most research focuses on the interpretation of the meaning of the data. However, majority methods are supervised-based, less attention has been paid on unsupervised approaches which is important when domain knowledge is incomplete or hard to obtain. In this paper, we present a new framework for unsupervised analysis of gene expression data which applies an interrelated two-way clustering approach on the gene expression matrices. The goal of clustering is to find important gene patterns and perform cluster discovery on samples. The advantage of this approach is that we can dynamically use the relationships between the groups of the genes and samples while iteratively clustering through both gene-dimension and sample-dimension. We illustrate the method on gene expression data from a study of multiple sclerosis patients. The experiments demonstrate the effectiveness of this approach.