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scNetViz: from single cells to networks using Cytoscape.

Krishna Choudhary1, Elaine C Meng2, J Javier Diaz-Mejia2,3,4,5

  • 1Institute of Data Science and Biotechnology, Gladstone Institutes, San Francisco, California, 94158, USA.

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Summary

scNetViz is a Cytoscape app for interpreting single-cell RNA sequencing (scRNA-seq) data. It uses network analysis to reveal genes driving cell-type heterogeneity, aiding biological discovery.

Keywords:
AppCytoscapeExpression analysisNetwork biologySingle cellscRNA-seq

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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.