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cellxgene: a performant, scalable exploration platform for high dimensional sparse matrices

Colin Megill, Bruce Martin, Charlotte Weaver, View ORCID ProfileSidney Bell, Lia Prins, Seve Badajoz, Brian McCandless, View ORCID ProfileAngela Oliveira Pisco, Marcus Kinsella, View ORCID ProfileFiona Griffin, View ORCID ProfileJustin Kiggins, Genevieve Haliburton, Arathi Mani, Matthew Weiden, Madison Dunitz, Maximilian Lombardo, Timmy Huang, Trent Smith, Signe Chambers, View ORCID ProfileJeremy Freeman, View ORCID ProfileJonah Cool, View ORCID ProfileAmbrose Carr
doi: https://doi.org/10.1101/2021.04.05.438318
Colin Megill
1Chan Zuckerberg Initiative
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Bruce Martin
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Charlotte Weaver
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Sidney Bell
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Lia Prins
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Seve Badajoz
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Brian McCandless
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Angela Oliveira Pisco
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Marcus Kinsella
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Fiona Griffin
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Justin Kiggins
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Genevieve Haliburton
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Arathi Mani
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Matthew Weiden
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Madison Dunitz
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Maximilian Lombardo
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Timmy Huang
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Trent Smith
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Signe Chambers
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Jeremy Freeman
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Jonah Cool
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  • For correspondence: jcool@chanzuckerberg.com acarr@chanzuckerberg.com
Ambrose Carr
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  • For correspondence: jcool@chanzuckerberg.com acarr@chanzuckerberg.com
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Abstract

Quickly and flexibly exploring high-dimensional datasets, such as scRNAseq data, is underserved but critical for hypothesis generation, dataset annotation, publication, sharing, and community reuse. cellxgene is a highly generalizable, web-based interface for exploring high dimensional datasets along categorical, continuous and spatial dimensions, as well as feature annotation. cellxgene is differentiated by its ability to performantly handle millions of observations, and bridges a critical gap by enabling computational and experimental biologists to iteratively ask questions of private and public datasets. In doing so, cellxgene increases the utility and reusability of datasets across the single-cell ecosystem.

The codebase can be accessed at https://github.com/chanzuckerberg/cellxgene. For questions and inquiries, please contact cellxgene{at}chanzuckerberg.com.

Competing Interest Statement

The authors have declared no competing interest.

Footnotes

  • https://cellxgene.cziscience.com/

Copyright 
The copyright holder has placed this preprint in the Public Domain. It is no longer restricted by copyright. Anyone can legally share, reuse, remix, or adapt this material for any purpose without crediting the original authors.
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Posted April 06, 2021.
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cellxgene: a performant, scalable exploration platform for high dimensional sparse matrices
Colin Megill, Bruce Martin, Charlotte Weaver, Sidney Bell, Lia Prins, Seve Badajoz, Brian McCandless, Angela Oliveira Pisco, Marcus Kinsella, Fiona Griffin, Justin Kiggins, Genevieve Haliburton, Arathi Mani, Matthew Weiden, Madison Dunitz, Maximilian Lombardo, Timmy Huang, Trent Smith, Signe Chambers, Jeremy Freeman, Jonah Cool, Ambrose Carr
bioRxiv 2021.04.05.438318; doi: https://doi.org/10.1101/2021.04.05.438318
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cellxgene: a performant, scalable exploration platform for high dimensional sparse matrices
Colin Megill, Bruce Martin, Charlotte Weaver, Sidney Bell, Lia Prins, Seve Badajoz, Brian McCandless, Angela Oliveira Pisco, Marcus Kinsella, Fiona Griffin, Justin Kiggins, Genevieve Haliburton, Arathi Mani, Matthew Weiden, Madison Dunitz, Maximilian Lombardo, Timmy Huang, Trent Smith, Signe Chambers, Jeremy Freeman, Jonah Cool, Ambrose Carr
bioRxiv 2021.04.05.438318; doi: https://doi.org/10.1101/2021.04.05.438318

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