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      PlaqView 2.0: A comprehensive web portal for cardiovascular single-cell genomics

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          Abstract

          Single-cell RNA-seq (scRNA-seq) is a powerful genomics technology to interrogate the cellular composition and behaviors of complex systems. While the number of scRNA-seq datasets and available computational analysis tools have grown exponentially, there are limited systematic data sharing strategies to allow rapid exploration and re-analysis of single-cell datasets, particularly in the cardiovascular field. We previously introduced PlaqView, an open-source web portal for the exploration and analysis of published atherosclerosis single-cell datasets. Now, we introduce PlaqView 2.0 (www.plaqview.com), which provides expanded features and functionalities as well as additional cardiovascular single-cell datasets. We showcase improved PlaqView functionality, backend data processing, user-interface, and capacity. PlaqView brings new or improved tools to explore scRNA-seq data, including gene query, metadata browser, cell identity prediction, ad hoc RNA-trajectory analysis, and drug-gene interaction prediction. PlaqView serves as one of the largest central repositories for cardiovascular single-cell datasets, which now includes data from human aortic aneurysm, gene-specific mouse knockouts, and healthy references. PlaqView 2.0 brings advanced tools and high-performance computing directly to users without the need for any programming knowledge. Lastly, we outline steps to generalize and repurpose PlaqView's framework for single-cell datasets from other fields.

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          Most cited references40

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          Integrative analysis of complex cancer genomics and clinical profiles using the cBioPortal.

          The cBioPortal for Cancer Genomics (http://cbioportal.org) provides a Web resource for exploring, visualizing, and analyzing multidimensional cancer genomics data. The portal reduces molecular profiling data from cancer tissues and cell lines into readily understandable genetic, epigenetic, gene expression, and proteomic events. The query interface combined with customized data storage enables researchers to interactively explore genetic alterations across samples, genes, and pathways and, when available in the underlying data, to link these to clinical outcomes. The portal provides graphical summaries of gene-level data from multiple platforms, network visualization and analysis, survival analysis, patient-centric queries, and software programmatic access. The intuitive Web interface of the portal makes complex cancer genomics profiles accessible to researchers and clinicians without requiring bioinformatics expertise, thus facilitating biological discoveries. Here, we provide a practical guide to the analysis and visualization features of the cBioPortal for Cancer Genomics.
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            Integrating single-cell transcriptomic data across different conditions, technologies, and species

            Computational single-cell RNA-seq (scRNA-seq) methods have been successfully applied to experiments representing a single condition, technology, or species to discover and define cellular phenotypes. However, identifying subpopulations of cells that are present across multiple data sets remains challenging. Here, we introduce an analytical strategy for integrating scRNA-seq data sets based on common sources of variation, enabling the identification of shared populations across data sets and downstream comparative analysis. We apply this approach, implemented in our R toolkit Seurat (http://satijalab.org/seurat/), to align scRNA-seq data sets of peripheral blood mononuclear cells under resting and stimulated conditions, hematopoietic progenitors sequenced using two profiling technologies, and pancreatic cell 'atlases' generated from human and mouse islets. In each case, we learn distinct or transitional cell states jointly across data sets, while boosting statistical power through integrated analysis. Our approach facilitates general comparisons of scRNA-seq data sets, potentially deepening our understanding of how distinct cell states respond to perturbation, disease, and evolution.
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              Enrichr: a comprehensive gene set enrichment analysis web server 2016 update

              Enrichment analysis is a popular method for analyzing gene sets generated by genome-wide experiments. Here we present a significant update to one of the tools in this domain called Enrichr. Enrichr currently contains a large collection of diverse gene set libraries available for analysis and download. In total, Enrichr currently contains 180 184 annotated gene sets from 102 gene set libraries. New features have been added to Enrichr including the ability to submit fuzzy sets, upload BED files, improved application programming interface and visualization of the results as clustergrams. Overall, Enrichr is a comprehensive resource for curated gene sets and a search engine that accumulates biological knowledge for further biological discoveries. Enrichr is freely available at: http://amp.pharm.mssm.edu/Enrichr.
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                Author and article information

                Contributors
                Journal
                Front Cardiovasc Med
                Front Cardiovasc Med
                Front. Cardiovasc. Med.
                Frontiers in Cardiovascular Medicine
                Frontiers Media S.A.
                2297-055X
                08 August 2022
                2022
                : 9
                : 969421
                Affiliations
                [1] 1Medical Scientist Training Program, University of Virginia , Charlottesville, VA, United States
                [2] 2Center for Public Health Genomics, University of Virginia , Charlottesville, VA, United States
                [3] 3Research Computing, University of Virginia , Charlottesville, VA, United States
                [4] 4Department of Biochemistry and Molecular Genetics, University of Virginia , Charlottesville, VA, United States
                [5] 5Department of Computer Engineering, University of Virginia , Charlottesville, VA, United States
                [6] 6Department of Molecular Biology and Genetics, Izmir Institute of Technology , Gülbahçe, Turkey
                [7] 7Central Diagnostics Laboratory, Division Laboratories, Pharmacy, and Biomedical Genetics, University Medical Center Utrecht, Utrecht University , Utrecht, Netherlands
                [8] 8Department of Public Health Sciences, University of Virginia , Charlottesville, VA, United States
                Author notes

                Edited by: Kathryn L. Howe, University of Toronto, Canada

                Reviewed by: Ayman Al Haj Zen, Hamad Bin Khalifa University, Qatar; Laura Zelarayán, University of Göttingen, Germany

                *Correspondence: Clint L. Miller clintm@ 123456virginia.edu

                This article was submitted to Atherosclerosis and Vascular Medicine, a section of the journal Frontiers in Cardiovascular Medicine

                Article
                10.3389/fcvm.2022.969421
                9393487
                6c87dd56-8b80-4799-9c1a-c8b868db73ba
                Copyright © 2022 Ma, Turner, Gancayco, Wong, Song, Mosquera, Auguste, Hodonsky, Prabhakar, Ekiz, van der Laan and Miller.

                This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.

                History
                : 15 June 2022
                : 21 July 2022
                Page count
                Figures: 8, Tables: 0, Equations: 0, References: 42, Pages: 14, Words: 7063
                Funding
                Funded by: National Institutes of Health, doi 10.13039/100000002;
                Award ID: R00HL125912
                Award ID: R01HL14823
                Funded by: Fondation Leducq, doi 10.13039/501100001674;
                Award ID: 18CVD02
                Funded by: ICIN Netherlands Heart Institute, doi 10.13039/501100006006;
                Award ID: CVON 2011/B019
                Award ID: CVON 2017-20
                Funded by: European Research Area Network on Cardiovascular Diseases, doi 10.13039/501100020407;
                Award ID: 01KL1802
                Categories
                Cardiovascular Medicine
                Original Research

                scrna-seq,single-cell,cardiovascular,genomics,web portal,database

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