DENetwork: Unveiling Regulatory and Signaling Networks Behind Differentially-Expressed Genes

Abstract

ABSTRACTDifferential gene expression analysis from RNA-sequencing (RNA-seq) data offers crucial insights into biological differences between sample groups. However, the conventional focus on differentially-expressed (DE) genes often omits non-DE regulators, which are an integral part of such differences. Moreover, DE genes frequently serve as passive indicators of transcriptomic variations rather than active influencers, limiting their utility as intervention targets. To address these shortcomings, we have developedDENetwork. This innovative approach deciphers the intricate regulatory and signaling networks driving transcriptomic variations between conditions with distinct phenotypes. Unique in its integration of both DE and critical non-DE genes in a graphical model,DENetworkenhances the capabilities of traditional differential gene analysis tools, such asDESeq2. Our application ofDENetworkto an array of simulated and real datasets showcases its potential to encapsulate biological differences, as demonstrated by the relevance and statistical significance of enriched gene functional terms.DENetworkoffers a robust platform for systematically characterizing the biological mechanisms that underpin phenotypic differences, thereby augmenting our understanding of biological variations and facilitating the formulation of effective intervention strategies.