Abstract
Single-cell RNA-sequencing (scRNA-seq) has revolutionized molecular biology and medicine by enabling high-throughput studies of cellular heterogeneity in diverse tissues. Applying network biology approaches to scRNA-seq data can provide useful insights into genes driving heterogeneous cell-type compositions of tissues. Here, we present scNetViz- a Cytoscape app to aid biological interpretation of cell clusters in scRNA-seq data using network analysis. scNetViz calculates the differential expression of each gene across clusters and then creates a cluster-specific gene functional interaction network between the significantly differentially expressed genes for further analysis, such as pathway enrichment analysis. To automate a complete data analysis workflow, scNetViz integrates parts of the Scanpy software, which is a popular Python package for scRNA-seq data analysis, with Cytoscape apps such as stringApp, cyPlot, and enhancedGraphics. We describe our implementation of methods for accessing data from public single cell atlas projects, differential expression analysis, visualization, and automation. scNetViz enables users to analyze data from public atlases or their own experiments, which we illustrate with two use cases. Analysis can be performed via the Cytoscape GUI or CyREST programming interface using R (RCy3) or Python (py4cytoscape).
Highlights
Single-cell RNA sequencing has yielded significant insights into mechanisms regulating diverse biological systems.[1]
To study cell types in scRNA-seq data, it is useful to compare cell clusters to identify celltype-specific gene expression markers and genes associated with a phenotype.[2,5]
Overview scNetViz consists of two main components, a Java-based Cytoscape app and a web service implemented on a compute cluster at the Resource for Biocomputing, Visualization and Informatics (RBVI) at the University of California, San Francisco
Summary
Single-cell RNA sequencing (scRNA-seq) has yielded significant insights into mechanisms regulating diverse biological systems.[1] This technology captures transcriptome-wide expression profiles of single cells, which can be used to cluster cells and identify the biological features that distinguish the clusters.[2] With continued technological advances and cost efficiencies, scRNA-seq is becoming increasingly common and new data are being generated at a rapid pace Global efforts such as the Human Cell Atlas,[3] which aims to map a healthy human, and the EMBL-EBI Single Cell Expression Atlas,[4] which organizes published datasets across multiple species and provides access to results from standardized analyses, are growing rapidly. It provides a convenient interface for integrating gene network information with scRNA-seq datasets, which can save time for researchers
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