scNetViz: from single cells to networks using Cytoscape

Area of Science:

• Computational Biology
• Bioinformatics
• Genomics

Background:

• Single-cell RNA sequencing (scRNA-seq) enables high-throughput analysis of cellular heterogeneity.
• Network biology approaches can elucidate gene functions within complex cellular compositions.
• Interpreting scRNA-seq data requires robust tools for identifying key regulatory genes.

Purpose of the Study:

• To introduce scNetViz, a Cytoscape application for biological interpretation of scRNA-seq data through network analysis.
• To provide a user-friendly platform for visualizing and analyzing gene expression patterns across cell clusters.
• To integrate scRNA-seq data analysis with established network biology tools.

Main Methods:

• scNetViz calculates differential gene expression across cell clusters.
• It constructs cluster-specific gene functional interaction networks for differentially expressed genes.
• The app integrates Scanpy, stringApp, cyPlot, and enhancedGraphics for automated workflows.

Main Results:

• scNetViz facilitates the identification of key genes and pathways associated with cellular heterogeneity.
• The tool enables analysis of both public scRNA-seq datasets and user-generated data.
• Demonstrated utility through two distinct case studies.

Conclusions:

• scNetViz enhances the biological interpretation of scRNA-seq data by leveraging network analysis within Cytoscape.
• The application supports flexible data analysis via GUI or programming interfaces (R/Python).
• It offers a valuable resource for researchers studying cellular heterogeneity in diverse biological systems.