Context-dependent clustering for dynamic cellular state modeling of microarray gene expression

Motivation: High-throughput expression profiling allows researchers to study gene activities globally. Genes with similar expression profiles are likely to encode proteins that may participate in a common structural complex, metabolic pathway or biological process. Many clustering, classification and dimension reduction approaches, powerful in elucidating the expression data, are based on this rationale. However, the converse of this common perception can be misleading. In fact, many biologically related genes turn out uncorrelated in expression. Results: In this article, we present a novel method for investigating gene co-expression patterns. We assume the correlation between functionally related genes can be strengthened or weakened according to changes in some relevant, yet unknown, cellular states. We develop a context-dependent clustering (CDC) method to model the cellular state variable. We apply it to the transcription regulatory study for Saccharomyces cerevisiae, using the St...

• Type: Journal
• Year: 2007
• Where: BIOINFORMATICS
• Authors: Shinsheng Yuan, Ker-Chau Li
• Keywords: BIOINFORMATICS 2007, Expression Data, Expression Profiles, Related Genes