Incorporating gene functions into regression analysis of DNA-protein binding data and gene expression data to construct transcriptional networks

Useful information on transcriptional networks has been extracted by regression analyses of gene expression data and DNA-protein binding data. However, a potential limitation of these approaches is their assumption on the common and constant activity level of a transcription factor (TF) on all of the genes in any given experimental condition, for example, any TF is assumed to be either an activator or a repressor, but not both, whereas it is known that some TFs can be dual regulators. Rather than assuming a common linear regression model for all of the genes, we propose using separate regression models for various gene groups; the genes can be grouped based on their functions or some clustering results. Furthermore, to take advantage of the hierarchical structure of many existing gene function annotation systems such as Gene Ontology (GO), we propose a shrinkage method that borrows information from relevant gene groups. Applications to a yeast data set and simulations lend support to our proposed methods. In particular, we find that the shrinkage method consistently works well under various scenarios. We recommend the use of the shrinkage method as a useful alternative to the existing methods.