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Library for multiscale visualization of high-resolution multiplexed bioimaging data on the web. Directly renders Zarr and OME-TIFF.

Viv npm version package documenation

A WebGL-powered toolkit for interactive visualization of high-resolution, multiplexed bioimaging datasets.

Interactive volumetric view in web browser; sliders control visible planes. Multi-channel rendering of high-resolution microscopy dataset

About

Viv is a JavaScript library for rendering OME-TIFF and OME-NGFF (Zarr) directly in the browser. The rendering components of Viv are packaged as deck.gl layers, making it easy to compose with existing layers to create rich interactive visualizations.

More details and related work can be found in our paper and original preprint. Please cite our paper in your research:

Trevor Manz, Ilan Gold, Nathan Heath Patterson, Chuck McCallum, Mark S Keller, Bruce W Herr II, Katy BΓΆrner, Jeffrey M Spraggins, Nils Gehlenborg, "Viv: multiscale visualization of high-resolution multiplexed bioimaging data on the web." Nature Methods (2022), doi:10.31219/10.1038/s41592-022-01482-7

πŸ’» Related Software

Screenshot Description
Avivator viewer running in Chrome Avivator
A lightweight viewer for local and remote datasets. The source code is include in this repository under avivator/. See our πŸŽ₯ video tutorial to learn more.
Vizarr viewer running in Jupyter Notebook Vizarr
A minimal, purely client-side program for viewing OME-NGFF and other Zarr-based images. Vizarr supports a Python backend using the imjoy-rpc, allowing it to not only function as a standalone application but also directly embed in Jupyter or Google Colab Notebooks.

πŸ’₯ In Action

πŸ’Ύ Supported Data Formats

Viv's data loaders support OME-NGFF (Zarr), OME-TIFF, and Indexed OME-TIFF*. We recommend converting proprietrary file formats to open standard formats via the bioformats2raw + raw2ometiff pipeline. Non-pyramidal datasets are also supported provided the individual texture can be uploaded to the GPU (< 4096 x 4096 in pixel size).

Please see the tutorial for more information.

*We describe Indexed OME-TIFF in our paper as an optional enhancement to provide efficient random chunk access for OME-TIFF. Our approach substantially improves chunk load times for OME-TIFF datasets with large Z, C, or T dimensions that otherwise may incur long latencies due to seeking. More information on generating an IFD index (JSON) can be found in our tutorial or documentation.

πŸ’½ Installation

$ npm install @hms-dbmi/viv

You will also need to install deck.gl and other peerDependencies manually. This step prevent users from installing multiple versions of deck.gl in their projects.

$ npm install deck.gl @luma.gl/core

Breaking changes may happen on the minor version update. Please see the changelog for information.

πŸ“– Documentation

Detailed API information and example sippets can be found in our documentation.

πŸ—οΈ Development

This repo is a monorepo using pnpm workspaces. The package manager used to install and link dependncies must be pnpm.

Each folder under packages/ are a published as a separate packages on npm under the @vivjs scope. The top-level package @hms-dbmi/viv exports from these dependencies.

To develop and test the @hms-dbmi/viv package:

  1. Run pnpm install in viv root folder
  2. Run pnpm dev to start a development server
  3. Run pnpm test to run all tests (or specific pnpm test --filter=@vivjs/layers

πŸ› οΈ Build

To build viv's documentation and the Avivator website (under sites/), run:

pnpm build # all packages, avivator, and documentation
pnpm -r build --filter=avivator # build a specific package or site

πŸ“„ Publish

Checkout latest master branch, run:

# checkout a new release branch
git checkout <branch name>
# commit and tag a new version
pnpm version [major | minor | patch]
# push commit and tag simultaneously
git push --atomic origin <branch name> <tag>

Open a PR for <branch name>. Our CI will run a release workflow for tagged commits starting with v* automatically. Inspect the GitHub Actions to ensure the workflow was successful.

🌎 Browser Support

Viv supports both WebGL1 and WebGL2 contexts, to provides coverage across Safari, Firefox, Chrome, and Edge. Please file an issue if you find a browser in which Viv does not work.