scNetViz

scNetViz performs network-based analysis and visualization of single-cell RNA sequencing (scRNA-seq) data to identify cluster-specific differentially expressed genes and construct their functional interaction networks for biological interpretation.

Key Features:

• Differential Expression Analysis: Calculates differential gene expression across cell clusters derived from scRNA-seq data.
• Functional Interaction Networks: Constructs cluster-specific gene functional interaction networks from identified differentially expressed genes.
• Pathway Enrichment Analysis: Performs pathway enrichment analysis on network genes to interpret their biological significance.
• Integration with Scanpy and Cytoscape apps: Integrates with Scanpy and Cytoscape apps including stringApp, cyPlot, and enhancedGraphics for network visualization and plotting.
• Data Access and Programmatic Automation: Supports access to public single-cell atlas projects and programmatic automation via CyREST with R (RCy3) or Python (py4cytoscape).

Scientific Applications:

• Cellular heterogeneity analysis: Identify genes and networks that drive cellular heterogeneity within tissues using scRNA-seq data.
• Developmental and disease mechanism investigation: Elucidate pathways and regulatory mechanisms relevant to developmental processes and disease mechanisms in molecular biology and medicine.
• Biomarker and therapeutic target discovery: Assist in identifying candidate biomarkers and potential therapeutic targets through network and enrichment analyses.

Methodology:

Access single-cell datasets from public atlases or user input; calculate differential expression of genes across identified cell clusters; construct functional interaction networks for significantly differentially expressed genes per cluster; visualize networks in Cytoscape and perform pathway enrichment analysis.