Genome-wide matching of genes to cellular roles using guilt-by-association models derived from single sample analysis.

BACKGROUND

High-throughput methods that ascribe a cellular or physiological function for each gene product are useful to understand the roles of genes that have not been extensively characterized by molecular or genetic approaches. One method to infer gene function is "guilt-by-association", in which the expression pattern of a poorly characterized gene is shown to co-vary with the expression of better-characterized genes. The function of the poorly characterized gene is inferred from the known function(s) of the well-described genes. For example, genes co-expressed with transcripts that vary during the cell cycle, development, environmental stresses, and with oncogenesis have been implicated in those processes.

FINDINGS

While examining the expression characteristics of several poorly characterized genes, we noted that we could associate each of the genes with a cellular phenotype by correlating individual gene expression changes with gene set enrichment scores from individual samples. We evaluated the effectiveness of this approach using a modest sized gene expression data set (expO) and a compendium of gene expression phenotypes (MSigDBv3.0). We found the transcripts that correlated best with enrichment in mitochondrial and lysosomal gene sets were mostly related to those processes (89/100 and 44/50, respectively). The reciprocal evaluation, ranking gene sets according to correlation of enrichment with an individual gene's expression, also reflected known associations for prominent genes in the biomedical literature (16/19). In evaluating the model, we also found that 4% of the genome encodes proteins that are associated with small molecule and small peptide signal transduction gene sets, implicating a large number of genes in both internal and external environmental sensing.

CONCLUSIONS

Our results show that this approach is useful to infer functions of disparate sets of genes. This method mirrors the biological experimental approaches used by others to associate individual genes with defined gene expression changes. Moreover, the approach can be used beyond discovering genes related to a cellular process to discover meaningful expression phenotypes from a compendium that are associated with a given gene. The effectiveness, versatility, and breadth of this approach make possible its application in a variety of contexts and with a variety of downstream analyses.