Cellenium—a scalable and interactive visual analytics app for exploring multimodal single-cell data

Abstract

Multimodal single-cell sequencing data provide detailed views into the molecular biology of cells. To allow for interactive analyses of such rich data and to readily derive insights from it, new analysis solutions are required. In this work we present Cellenium, our new scalable visual analytics web application which enables users to semantically integrate and organize all their single-cell RNA-, ATAC-, and CITE-sequencing studies. Users can then find relevant studies and analyze single-cell data within and across studies. An interactive cell annotation feature allows for adding user-defined cell types. Source code and documentation are freely available under an MIT license and are available on GitHub (https://github.com/Bayer-Group/cellenium). The server backend is implemented in PostgreSQL, Python 3 and GraphQL, the frontend is written in ReactJS, TypeScript and Mantine css, plots are generated using plotlyjs, seaborn, vega-lite and nivo.rocks. The application is dockerized and can be deployed and orchestrated on a standard workstation via docker-compose. Supplementary data are available at Bioinformatics online.