Single cell omics technologies present unique opportunities for biomedical and life sciences from lab to clinic, but the high dimensional nature of such data poses challenges for computational analysis and interpretation. Furthermore, FAIR data management as well as data privacy and security become crucial when working with clinical data, especially in cross-institutional and translational settings. Existing solutions are either bound to the desktop of one researcher or come with dependencies

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... vendor-specific technology for cloud storage or user authentication. Results: To facilitate analysis and interpretation of single-cell data by users without bioinformatics expertise, we present SCelVis, a flexible, interactive and user-friendly app for web-based visualization of pre-processed single-cell data. Users can survey multiple interactive visualizations of their single cell expression data and cell annotation, define cell groups by filtering or manual selection and perform differential gene expression, and download raw or processed data for further offline analysis. SCelVis can be run both on the desktop and cloud systems, accepts input from local and various remote sources using standard and open protocols, and allows for hosting data in the cloud and locally. We test and validate our visualization using publicly available scRNA-seq data. Methods: SCelVis is implemented in Python using Dash by Plotly. It is available as a standalone application as a Python package, via Conda/Bioconda and as a Docker image. All components are available as open source under the permissive MIT license and are based on open standards and interfaces, enabling further development and integration with third party pipelines and analysis components. The GitHub repository is https://github.com/bihealth/scelvis. PeerJ reviewing PDF | Abstract 17 Background: Single cell omics technologies present unique opportunities for biomedical and life 18 sciences from lab to clinic, but the high dimensional nature of such data poses challenges for 19 computational analysis and interpretation. Furthermore, FAIR data management as well as data privacy 20 and security become crucial when working with clinical data, especially in cross-institutional and 21 translational settings. Existing solutions are either bound to the desktop of one researcher or come with 22 dependencies on vendor-specific technology for cloud storage or user authentication. 23 Results: To facilitate analysis and interpretation of single-cell data by users without bioinformatics 24 expertise, we present SCelVis, a flexible, interactive and user-friendly app for web-based visualization of 25 pre-processed single-cell data. Users can survey multiple interactive visualizations of their single cell 26 expression data and cell annotation, define cell groups by filtering or manual selection and perform 27 differential gene expression, and download raw or processed data for further offline analysis. SCelVis can 28 be run both on the desktop and cloud systems, accepts input from local and various remote sources using 29 standard and open protocols, and allows for hosting data in the cloud and locally. We test and validate our 30 visualization using publicly available scRNA-seq data. 31 Methods: SCelVis is implemented in Python using Dash by Plotly. It is available as a standalone 32 application as a Python package, via Conda/Bioconda and as a Docker image. All components are 33 available as open source under the permissive MIT license and are based on open standards and 34 interfaces, enabling further development and integration with third party pipelines and analysis 35 components. The GitHub repository is https://github.com/bihealth/scelvis. PeerJ reviewing PDF |