Genome-wide analysis of yeast expression data based on a priori generated co-regulation cliques

Abstract

DNA microarrays are highly sensitive tools to evaluate the gene expression status of organismic samples and standardized array formats exist for many different sample types. Differential expression studies usually utilize the strongest upor downregulated genes to generate networks visualizing the relationships among these genes. To include all yeast genes in one analysis and to get broader information on all cellular responses, we test a priori input of predefined genome-wide expression cliques and subsequent statistical analysis of the expression data. To this end, we generate a set of 72 co-regulation cliques using the information from 3196 microarray experiments. The obtained cliques performed highly significant in gene ontology and transcription factor enrichment analyses. We then tested the clique set on individual microarray experiments reporting on responses to pheromone, glycerol versus glucose based growth and the cellular response to heat. In all cases a highly significant determination of affected expression cliques was possible based on their average expression differences, the positions of their genes within hit rankings (UpRegScore) or the enrichment of the Top200 hits in certain cliques. The 72 cliques were finally used to compare experiments, which reported on the transcriptional response to polyglutamine proteins of different lengths. Using the predefined clique set it is possible to identify with high sensitivity and good significance sample and condition specific changes to gene expression. We thus conclude that an analysis, starting with these 72 preformed expression cliques, can complement traditional microarray analyses by visualizing the entire response on a static genome-wide gene set.