Multivariate Measurement of Gene Expression Relationships

The operational activities of cells are based on an awareness of their current state, coupled to a programmed response to internal and external cues in a context-dependent manner. One key goal of functional genomics is to develop analytical methods for delineating the ways in which the individual actions of genes are integrated into our understanding of the increasingly complex systems of organelle, cell, organ, and organism. This paper describes a novel approach to assess the codetermination of gene transcriptional states based upon statistical evaluation of reliably informative subsets of data derived from large-scale simultaneous gene expression measurements with cDNA microarrays. The method finds associations between the expression patterns of individual genes by determining whether knowledge of the transcriptional levels of a small gene set can be used to predict the associated transcriptional 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 ionizing radiation and downstream targets of inactivating gene mutations. This approach 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 currently available, this approach should be useful in finding sets of genes that participate in particular biological processes. As larger data sets and more computing power become available, the method can be extended to validating and ultimately identifying biologic (transcriptional) pathways based upon large-scale gene expression analysis.