MuDCoD

MuDCoD performs multi-subject community detection in personalized dynamic gene co-expression networks derived from single-cell RNA sequencing (scRNA-seq) data to identify gene modules that vary across individuals and over time.

Key Features:

• Multi-subject Analysis: Integrates networks from multiple subjects to detect gene communities that are shared or subject-specific.
• Dynamic Network Handling: Analyzes dynamic gene co-expression networks across time points to capture temporal variation in gene expression.
• Spectral Clustering Framework: Uses a spectral clustering framework that promotes information sharing among subject-specific networks and across time points to identify communities.
• Robustness Across Conditions: Maintains effective community detection even when information sharing among networks is minimal or absent.

Scientific Applications:

• Subject-specific temporal module discovery: Enables exploration of subject-specific biological processes that vary over time by clustering genes within personalized dynamic networks.
• Comparative network phenotyping: Reveals variable and shared gene communities across subjects and temporal dimensions to support studies of phenotypic differences at the network level.
• Demonstrated datasets: Applied to population-scale scRNA-seq datasets including human-induced pluripotent stem cells during dopaminergic neuron differentiation and CD4+ T cell activation to infer time-varying personalized gene modules.

Methodology:

MuDCoD applies a spectral clustering approach to detect communities in multi-subject dynamic gene co-expression networks, promotes information sharing among subject-specific networks and across time points, and has been validated through benchmarking using simulation and real scRNA-seq datasets.