Inferring biological functions and associated transcriptional regulators using gene set expression coherence analysis

In this study, we propose an algorithm for discovery of molecular functions and elucidation of transcriptional logics using two kinds of gene information, functional and regulatory motif gene sets. The algorithm, termed gene set expression coherence analysis first selects functional gene sets with significantly high expression coherences. Those candidate gene sets are further processed into a number of functionally related themes or functional clusters according to the expression similarities. Each functional cluster is then, investigated for the enrichment of transcriptional regulatory motifs using modified gene set enrichment analysis and regulatory motif gene sets. The method was tested for two publicly available expression profiles representing murine myogenesis and erythropoiesis. For respective profiles, our algorithm identified myocyte- and erythrocyte-related molecular functions, along with the putative transcriptional regulators for the corresponding molecular functions.As an integrative and comprehensive method for the analysis of large-scaled gene expression profiles, our method is able to generate a set of testable hypotheses: the transcriptional regulator X regulates function Y under cellular condition Z. GSECA algorithm is implemented into freely available software package.Advanced high-throughput microarray technologies have facilitated the investigation of gene expression in a genome-wide manner [1,2]. Because of the complex nature and large volume of data, whole-genome expression profiles often require appropriate and comprehensive analytic methods. Gene clustering according to the expression similarity has been popularly used in this perspective, often as the first step of analysis [3]. In addition, functional enrichment analysis or pathway analysis was proposed to explain the global gene expression changes in the context of available knowledge, such as functional annotation of genes [4]. A classical enrichment analysis uses functionally annotated