scNetViz: from single cells to networks using Cytoscape [version 1; peer review: 2 approved]

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 comp...

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Autores principales:	Krishna Choudhary, Elaine C. Meng, J. Javier Diaz-Mejia, Gary D. Bader, Alexander R. Pico, John H. Morris
Formato:	article
Lenguaje:	EN
Publicado:	F1000 Research Ltd 2021
Materias:	Medicine R Science Q
Acceso en línea:	https://doaj.org/article/d1b920056d384c8aa9941a2eb0afd0f1
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id	oai:doaj.org-article:d1b920056d384c8aa9941a2eb0afd0f1
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spelling	oai:doaj.org-article:d1b920056d384c8aa9941a2eb0afd0f12021-11-22T12:41:06ZscNetViz: from single cells to networks using Cytoscape [version 1; peer review: 2 approved]2046-140210.12688/f1000research.52460.1https://doaj.org/article/d1b920056d384c8aa9941a2eb0afd0f12021-06-01T00:00:00Zhttps://f1000research.com/articles/10-448/v1https://doaj.org/toc/2046-1402Single-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).Krishna ChoudharyElaine C. MengJ. Javier Diaz-MejiaGary D. BaderAlexander R. PicoJohn H. MorrisF1000 Research LtdarticleMedicineRScienceQENF1000Research, Vol 10 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	Medicine R Science Q
spellingShingle	Medicine R Science Q Krishna Choudhary Elaine C. Meng J. Javier Diaz-Mejia Gary D. Bader Alexander R. Pico John H. Morris scNetViz: from single cells to networks using Cytoscape [version 1; peer review: 2 approved]
description	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).
format	article
author	Krishna Choudhary Elaine C. Meng J. Javier Diaz-Mejia Gary D. Bader Alexander R. Pico John H. Morris
author_facet	Krishna Choudhary Elaine C. Meng J. Javier Diaz-Mejia Gary D. Bader Alexander R. Pico John H. Morris
author_sort	Krishna Choudhary
title	scNetViz: from single cells to networks using Cytoscape [version 1; peer review: 2 approved]
title_short	scNetViz: from single cells to networks using Cytoscape [version 1; peer review: 2 approved]
title_full	scNetViz: from single cells to networks using Cytoscape [version 1; peer review: 2 approved]
title_fullStr	scNetViz: from single cells to networks using Cytoscape [version 1; peer review: 2 approved]
title_full_unstemmed	scNetViz: from single cells to networks using Cytoscape [version 1; peer review: 2 approved]
title_sort	scnetviz: from single cells to networks using cytoscape [version 1; peer review: 2 approved]
publisher	F1000 Research Ltd
publishDate	2021
url	https://doaj.org/article/d1b920056d384c8aa9941a2eb0afd0f1
work_keys_str_mv	AT krishnachoudhary scnetvizfromsinglecellstonetworksusingcytoscapeversion1peerreview2approved AT elainecmeng scnetvizfromsinglecellstonetworksusingcytoscapeversion1peerreview2approved AT jjavierdiazmejia scnetvizfromsinglecellstonetworksusingcytoscapeversion1peerreview2approved AT garydbader scnetvizfromsinglecellstonetworksusingcytoscapeversion1peerreview2approved AT alexanderrpico scnetvizfromsinglecellstonetworksusingcytoscapeversion1peerreview2approved AT johnhmorris scnetvizfromsinglecellstonetworksusingcytoscapeversion1peerreview2approved
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scNetViz: from single cells to networks using Cytoscape [version 1; peer review: 2 approved]

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