Viv: Multiscale Visualization of High-Resolution Multiplexed Bioimaging Data on the Web

Manz, Trevor; Gold, Ilan; Patterson, Nathan Heath; McCallum, Chuck; Keller, Mark S; Herr, Bruce W.; Börner, Katy; Spraggins, Jeffrey M.; Gehlenborg, Nils

doi:10.31219/osf.io/wd2gu

Cited by 12 publications

(13 citation statements)

References 22 publications

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“…To address this problem, we will likely have to abandon our initial design goal of retaining the data on our collaborator's Google Drive so that Loon integrates nicely with their workflow. Imagepyramid-based approaches have been successfully used for microscopy visualization [26], and combined with other precomputations and efficient data structures, we expect to achieve significantly improved performance.…”

Section: Discussionmentioning

confidence: 99%

Loon: Using Exemplars to Visualize Large-Scale Microscopy Data

Lange

Polanco

Judson-Torres

et al. 2022

IEEE Trans. Visual. Comput. Graphics

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Fig. 1: Overview of the Loon Visualization Tool. (a) The Condition Selector visualizes cell growth rates for different drugs at different concentrations using small multiple line charts in a matrix. Analysts can pick conditions that show interesting behavior for detailed analysis. (b) The Image Selection View is used to navigate images and visualizes aggregate cell and track (cells tracked over time) properties. (c) The Image View shows a selected microscopy image and the segmentation of cells. (d) The Cell and Track Attributes View shows distributions of and correlations between attributes of cells and tracks and serves as the primary means to define selections and filters. (e) The Exemplar Cells View shows cells extracted from the images and samples from a user-specified distribution. It also shows the growth curves for the condition and the selected cells.

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Section: Discussionmentioning

confidence: 99%

Loon: Using Exemplars to Visualize Large-Scale Microscopy Data

Lange

Polanco

Judson-Torres

et al. 2022

IEEE Trans. Visual. Comput. Graphics

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show abstract

“…To address this problem, we will likely have to abandon our initial design goal of retaining the data on our collaborator's Google Drive so that Loon integrates nicely with their workflow. Imagepyramid based approaches have been successfully used for microscopy visualization [26] and combined with other pre-computations and efficient data structures, we expect to achieve significantly improved performance.…”

Section: Discussionmentioning

confidence: 99%

Loon: Using Exemplars to Visualize Large-Scale Microscopy Data

Lange¹,

Polanco²,

Judson-Torres³

et al. 2021

Preprint

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Which drug is most promising for a cancer patient? This is a question a new microscopy-based approach for measuring the mass of individual cancer cells treated with different drugs promises to answer in only a few hours. However, the analysis pipeline for extracting data from these images is still far from complete automation: human intervention is necessary for quality control for preprocessing steps such as segmentation, to adjust filters, and remove noise, and for the analysis of the result. To address this workflow, we developed Loon, a visualization tool for analyzing drug screening data based on quantitative phase microscopy imaging. Loon visualizes both, derived data such as growth rates, and imaging data. Since the images are collected automatically at a large scale, manual inspection of images and segmentations is infeasible. However, reviewing representative samples of cells is essential, both for quality control and for data analysis. We introduce a new approach of choosing and visualizing representative exemplar cells that retain a close connection to the low-level data. By tightly integrating the derived data visualization capabilities with the novel exemplar visualization and providing selection and filtering capabilities, Loon is well suited for making decisions about which drugs are suitable for a specific patient.

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“…We implemented client-side data loaders for accessing files stored on static web servers and cloud object stores, including for Zarr 12 , AnnData 4 , JSON and OME-TIFF [13][14][15] . Vitessce integrates the previously described Viv 16 and HiGlass 17 toolkits for visualization of multi-scale multiplexed imaging data and genome-mapped data, respectively.…”

Section: Mainmentioning

confidence: 99%

Vitessce: integrative visualization of multimodal and spatially-resolved single-cell data

Keller¹,

Gold²,

McCallum³

et al. 2021

Preprint

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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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Viv: Multiscale Visualization of High-Resolution Multiplexed Bioimaging Data on the Web

Cited by 12 publications

References 22 publications

Loon: Using Exemplars to Visualize Large-Scale Microscopy Data

Loon: Using Exemplars to Visualize Large-Scale Microscopy Data

Loon: Using Exemplars to Visualize Large-Scale Microscopy Data

Vitessce: integrative visualization of multimodal and spatially-resolved single-cell data

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