LaGrACE: Estimating gene program dysregulation using latent gene regulatory network for biomedical discovery

Abstract

AbstractGene expression programs that establish and maintain specific cellular states are orchestrated through a regulatory network composed of transcription factors, cofactors, and chromatin regulators. Dysregulation of this network can lead to a broad range of diseases by altering gene programs. This article presents LaGrACE, a novel method designed to estimate dysregulation of gene programs utilizing omics data with clinical information. This approach facilitates grouping of samples exhibiting similar patterns of gene program dysregulation, thereby enhancing the discovery of underlying molecular mechanisms. We rigorously evaluated LaGrACE’s performance using synthetic data, bulk RNA-seq clinical datasets (breast cancer, chronic obstructive pulmonary disease (COPD)), and single-cell RNA-seq drug perturbation datasets. Our findings demonstrate that LaGrACE is exceptionally robust in identifying biologically meaningful and prognostic molecular subtypes. Additionally, it effectively discerns drug-response signals at a single-cell resolution. Moreover, the COPD analysis revealed a new association between LEF1 and COPD molecular mechanisms and mortality. Collectively, these results underscore the utility of LaGrACE as a valuable tool for elucidating underlying mechanisms of diseases.