PySCNet

PySCNet: Single-cell gene regulatory network reconstruction and analysis toolkit

PySCNet reconstructs cell-specific and trajectory-specific gene regulatory networks (GRNs) from scRNA-seq data and analyzes gene co-expression and regulatory importance within heterogeneous cell populations.

Key Features:

• GRN Reconstruction: Constructs gene regulatory networks specific to individual cells or inferred cell trajectories.
• Method Integration: Integrates competitive methodologies for GRN construction, enabling selection of analytical approaches suited to specific datasets.
• Gene Co-expression Module Detection: Identifies gene co-expression modules based on expression patterns across single cells.
• Gene Importance Evaluation: Quantifies the importance of genes within regulatory networks to identify key regulators.

Scientific Applications:

• Cell Heterogeneity Analysis: Characterizes regulatory differences across cell types within heterogeneous populations using cell-specific GRNs.
• Developmental and Differentiation Studies: Analyzes trajectory-specific GRNs to investigate developmental pathways and cell differentiation processes.
• Cancer Genomics: Compares gene regulatory networks between malignant and normal cells to identify altered regulatory mechanisms and candidate therapeutic targets.

Methodology:

PySCNet processes scRNA-seq data using established pipelines for cell population identification, marker detection, and trajectory reconstruction prior to GRN inference. It applies integrated GRN construction methodologies to generate cell-specific networks and downstream analyses of co-expression modules and gene importance.