Matrix Factorization Methods Applied in Microarray Data Analysis

Abstract

Numerous methods have been applied to microarray data to group genes into clusters that show similar expression patterns. These methods assign each gene to a single group, which does not reflect the widely held view among biologists that most, if not all, genes in eukaryotes are involved in multiple biological processes and therefore will be multiply regulated. Here, we review several methods that have been developed that are capable of identifying patterns of behavior in transcriptional response and assigning genes to multiple patterns. Broadly speaking, these methods define a series of mathematical approaches to matrix factorization with different approaches to the fitting of the model to the data. We focus on these methods in contrast to traditional clustering methods applied to microarray data, which assign one gene to one cluster.