Multivariate measurement of gene expression relationships

The operational activities of cells are based on an awareness of their curr ent state, coupled to a programmed response to internal and external cues i n a context-dependent manner. One key goal of functional genomics is to dev elop analytical methods for delineating the ways in which the individual ac tions of genes are integrated into our understanding of the increasingly co mplex systems of organelle, cell, organ, and organism. This paper describes a novel approach to assess the codetermination of gene transcriptional sta tes based upon statistical evaluation of reliably informative subsets of da ta derived from large-scale simultaneous gene expression measurements with cDNA microarrays. The method finds associations between the expression patt erns of individual genes by determining whether knowledge of the transcript ional levels of a small gene set can be used to predict the associated tran scriptional state of another gene. To test this approach for identification of the relevant contextual elements of cellular response, we have modeled our approach using data from known gene response pathways including ionizin g radiation and downstream targets of inactivating gene mutations. This app roach strongly suggests that evaluation of the transcriptional status of a given gene(s) can be combined with data from global expression analyses to predict the expression level of another gene. With data sets of the size cu rrently available, this approach should be useful in finding sets of genes that participate in particular biological processes. As larger data sets an d more computing power become available, the method can be extended to vali dating and ultimately identifying biologic (transcriptional) pathways based upon large-scale gene expression analysis. (C) 2000 Academic Press.