LaGrACE: estimating gene program dysregulation with latent regulatory network.

Area of Science:

• Genomics and Bioinformatics
• Molecular Biology
• Computational Biology

Background:

• Cellular states are regulated by complex gene expression networks.
• Dysregulation of these networks contributes to various diseases.
• Identifying disease-specific molecular mechanisms requires advanced analytical tools.

Purpose of the Study:

• To introduce LaGrACE, a novel computational method for estimating gene program dysregulation.
• To enable the grouping of samples based on gene program dysregulation patterns.
• To facilitate the discovery of molecular mechanisms in disease subpopulations.

Main Methods:

• LaGrACE integrates omics data (e.g., RNA-seq) with clinical information.
• The method was validated using synthetic data, bulk RNA-seq datasets (breast cancer, COPD), and single-cell RNA-seq drug perturbation data.

Main Results:

• LaGrACE robustly identifies biologically meaningful and prognostic molecular subtypes.
• The method effectively detects drug response signals at single-cell resolution.
• Analysis of COPD data revealed a novel role for the LEF1 regulator in disease mechanisms linked to mortality.

Conclusions:

• LaGrACE is a valuable tool for elucidating disease mechanisms by analyzing gene program dysregulation.
• The method enhances the discovery of molecular subtypes and drug responses.
• Findings highlight the potential of LaGrACE in advancing precision medicine and disease research.