Improved detection of differentially expressed genes through incorporation of gene locations.

In determining differential expression in cDNA microarray experiments, the expression level of an individual gene is usually assumed to be independent of the expression levels of other genes, but many recent studies have shown that a gene's expression level tends to be similar to that of its neighbors on a chromosome, and differentially expressed (DE) genes are likely to form clusters of similar transcriptional activity along the chromosome. When modeled as a one-dimensional spatial series, the expression level of genes on the same chromosome frequently exhibit significant spatial correlation, reflecting spatial patterns in transcription. By modeling these spatial correlations, we can obtain improved estimates of transcript levels. Here, we demonstrate the existence of spatial correlations in transcriptional activity in the Escherichia coli (E. coli) chromosome across more than 50 experimental conditions. Based on this finding, we propose a hierarchical Bayesian model that borrows information from neighboring genes to improve the estimation of the expression level of a given gene and hence the detection of DE genes. Furthermore, we extend the model to account for the circular structure of E. coli chromosome and the intergenetic distance between gene neighbors. The simulation studies and analysis of real data examples in E. coli and yeast Saccharomyces cerevisiae show that the proposed method outperforms the commonly used significant analysis of microarray (SAM) t-statistic in detecting DE genes.