SimiC

SimiC infers phenotype-specific gene regulatory networks (GRNs) at single-cell resolution using similarity constraints to enable comparative analysis of regulatory interactions.

Key Features:

• Phenotype-Specific GRN Inference: Generates a separate high-resolution GRN for each phenotype in the input dataset, preserving regulatory specificity of distinct cellular states or conditions.
• Similarity Constraints: Enforces similarity constraints between GRNs derived from different phenotypes to ensure smooth transitions and facilitate direct comparisons across cellular states.
• Revealing Regulatory Dynamics: Jointly infers GRNs with similarity constraints to uncover variations and commonalities in regulatory relationships applicable to model and non-model systems.

Scientific Applications:

• Systems Biology: Quantifies regulatory architectures across distinct cellular phenotypes to inform systems-level models of gene regulation.
• Comparative GRN Analysis: Enables direct comparison of GRNs across treatments, conditions, or cellular states to identify shared and state-specific regulatory interactions.
• Cellular Function and Disease Mechanisms: Provides insights into how gene regulation varies across biological contexts, supporting studies of cellular function and disease-related regulatory changes.

Methodology:

Uses single-cell RNA-sequencing data to jointly infer phenotype-specific GRNs while enforcing similarity constraints that link networks across phenotypes.