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<p>Gene 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 combining omics data with clinical information. This approach facilitates the grouping of samples exhibiting similar patterns of gene program dysregulation, thereby enhancing the discovery of underlying molecular mechanisms in disease subpopulations. 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. In addition, it effectively discerns drug response signals at a single-cell resolution. Moreover, the COPD analysis uncovered a new role of LEF1 regulator in COPD molecular mechanisms associated with mortality. Collectively, these results underscore the utility of LaGrACE as a valuable tool for elucidating the underlying mechanisms of diseases.</p>

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LaGrACE: estimating gene program dysregulation with latent regulatory network

  • 期刊论文

  • 出版模式：

    开放获取

  • 发表日期：

    2025年6月30日

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  • Minxue Jia,
  • Haiyi Mao,
  • Mengli Zhou,
  • Yu-Chih Chen,
  • Panayiotis V Benos

摘要

Gene 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 combining omics data with clinical information. This approach facilitates the grouping of samples exhibiting similar patterns of gene program dysregulation, thereby enhancing the discovery of underlying molecular mechanisms in disease subpopulations. 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. In addition, it effectively discerns drug response signals at a single-cell resolution. Moreover, the COPD analysis uncovered a new role of LEF1 regulator in COPD molecular mechanisms associated with mortality. Collectively, these results underscore the utility of LaGrACE as a valuable tool for elucidating the underlying mechanisms of diseases.