Vitessce is an open-source interactive visualization framework for exploration of multi-modal and spatially-resolved single-cell data, with a modular architecture compatible with transcriptomic, proteomic, genome-mapped, and imaging data types. Its modular, coordinated multiple view implementation facilitates a wide range of visualization tasks to support all common single-cell assays. Vitessce is a client-side web application designed to be integrated with computational analysis tools and data resources and does not require specialized server infrastructure. The software is available at http://vitessce.io.

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A growing community is constructing a next-generation file format (NGFF) for bioimaging to overcome problems of scalability and heterogeneity. Organized by the Open Microscopy Environment (OME), individuals and institutes across diverse modalities facing these problems have designed a format specification process (OME-NGFF) to address these needs. This paper brings together a wide range of those community members to describe the format itself -- OME-Zarr -- along with tools and data resources available today to increase FAIR access and remove barriers in the scientific process. The current momentum offers an opportunity to unify a key component of the bioimaging domain -- the file format that underlies so many personal, institutional, and global data management and analysis tasks.

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Recent advances in highly multiplexed imaging have enabled the comprehensive profiling of complex tissues in healthy and diseased states, facilitating the study of fundamental biology and human disease in spatially-resolved contexts at subcellular resolution. However, current computational infrastructure to distribute and visualize these data on the web remains complex to set up and maintain. To address these limitations, we have developed Viv—an open-source image visualization library for high-resolution multiplexed image data that is implemented in JavaScript and builds on modern web technologies. Viv directly renders Bio-Formats-compatible Zarr and OME-TIFF data formats. Three use cases, including integration into Jupyter Notebooks (https://github.com/hms-dbmi/vizarr) and a data portal, as well as an image viewer (http://avivator.gehlenborglab.org) demonstrate the capabilities of our proposed approach.

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