Deciphering context-specific gene programs from single-cell and spatial transcriptomics data with DeCEP

Area of Science:

• Genomics
• Computational Biology
• Systems Biology

Background:

• Functional gene programs are crucial for cell identity and are implicated in various health and disease states.
• Deciphering complex gene programs at single-cell and spatial resolutions is essential but challenging.
• Single-cell RNA sequencing (scRNA-seq) and spatial transcriptomics (ST) offer powerful tools for comprehensive gene program characterization.

Purpose of the Study:

• To introduce DeCEP, a computational framework for characterizing context-specific gene programs.
• To leverage scRNA-seq and ST data for detailed analysis of gene networks and program activity.
• To identify context-dependent hub genes and assign gene program activity to individual cells or spatial locations.

Main Methods:

• DeCEP utilizes functional gene lists and directed graphs to build functional networks for specific cellular or spatial contexts.
• Network topology is employed to identify context-dependent hub genes associated with gene programs.
• Gene program activity is assigned to individual cells or spatial locations within the analyzed data.

Main Results:

• DeCEP enables a more fine-grained characterization of gene programs within specific biological contexts.
• The framework excels in analyzing contexts with significant transcriptional heterogeneity.
• Evaluations on simulated and real biological datasets confirm DeCEP's complementary strengths over existing methods.

Conclusions:

• DeCEP provides a robust computational approach for dissecting context-specific gene programs.
• The framework facilitates deeper biological insights into complex diseases like Alzheimer's and cancer.
• DeCEP advances the understanding of gene regulation in normal tissues and disease states.