Context Dependent Clustering and Its Application on Microarray Expression Data

Abstract

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 assumption. However, the converse of this assumption does not hold and many biologically related genes often have no strong correlations. Many reasons are possible, including noisy array measurements, post-translational regulation, and multiple gene functions. Liquid association is a computational method to help the identification of important cellular players that may contribute to the weakening of the correlation. In this talk, we present a different analysis strategy. We assume correlation between functionally related genes can be strengthened or weakened according to changes in the relevant, yet unknown, cellular states. We develop a context-dependent clustering (CDC) method to model the cellular state variable directly. We apply CDC to the transcription regulatory study in Saccharomyces cerevisiae, using the Stanford cell-cycle gene expression data. We investigate the co-expression patterns between transcription factors (TFs) and their target genes (TGs) predicted by the genome-wide location analysis of Harbison et. al (2004). Since TF regulates the expression of its TGs, correlation between TF’s and TG’s expression profiles can be expected. But as many authors have observed, the expression of transcription factors do not correlate well with the expression of their target genes. Instead of attributing the main reason to the lack of correlation between the transcript abundance and TF activity, we search for cellular conditions that would facilitate the TF-TG correlation. The results for sulfur amino acid pathway regulation by MET4, respiratory genes regulation by HAP4, and the branched chain amino acid biosynthesis regulation by LEU3 are discussed in detail. Our method suggests a new way to understand the complex biological system from microarray data.