Control analysis of DNA microarray expression data.

Abstract

DNA microarrays produce large amounts of data. Complex changes in gene expression are revealed; sometimes thousands of mRNAs change between experiments. Here we apply modular regulation analysis to microarray data to reveal and quantify the mRNA changes that are important for cellular responses. The mRNAs are sorted into clusters. How strongly a perturbation alters each cluster is multiplied by how strongly each cluster affects an output, to obtain coefficients that describe how much of the change in the output is transmitted through each mRNA cluster. An example published dataset is analysed to reveal that the response ('relative fitness') of yeast to 2-deoxy-D-glucose is not transmitted by a single mRNA cluster, but instead many clusters contribute to the overall response. The method is applicable to microarray, transcriptome, proteome and metabolome data.