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 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. Since TF regulates the expression of its TGs, correlation between TFs and TGs 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 mitotic cell cycle regulation by ACE2/SWI5 are discussed in detail. Our method suggests a new way to understand the complex biological system from microarray data.