Chuck McCallum

We present HiGlass, an open source visualization tool built on web technologies that provides a rich interface for rapid, multiplex, and multiscale navigation of 2D genomic maps alongside 1D genomic tracks, allowing users to combine various data types, synchronize multiple visualization modalities, and share fully customizable views with others. We demonstrate its utility in exploring different experimental conditions, comparing the results of analyses, and creating interactive snapshots to share with collaborators and the broader public. HiGlass is accessible online at http://higlass.io and is also available as a containerized application that can be run on any platform.

Multiomics technologies with single-cell and spatial resolution make it possible to measure thousands of features across millions of cells. However, visual analysis of high-dimensional transcriptomic, proteomic, genome-mapped and imaging data types simultaneously remains a challenge. Here we describe Vitessce, an interactive web-based visualization framework for exploration of multimodal and spatially resolved single-cell data. We demonstrate integrative visualization of millions of data points, including cell-type annotations, gene expression quantities, spatially resolved transcripts and cell segmentations, across multiple coordinated views. The open-source software is available at http://vitessce.io .

Multi-omics technologies with single-cell and spatial resolution make it possible to measure thousands of features across millions of cells. However, visual analysis of high-dimensional transcriptomic, proteomic, genome-mapped, and imaging data types simultaneously remains a challenge. Here, we describe Vitessce, an interactive web-based visualization framework for exploration of multimodal and spatially-resolved single-cell data. We demonstrate integrative visualization of millions of data points including cell type annotations, gene expression quantities, spatially-resolved transcripts, and cell segmentations across multiple coordinated views. The open source software is available at http://vitessce.io.

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 OME-TIFF and OME-NGFF data formats. Three use cases, including integration into Jupyter Notebooks (https://github.com/hms-dbmi/vizarr), a visual exploration tool, and an image viewer (http://avivator.gehlenborglab.org) demonstrate the capabilities of our proposed approach.