| Autor: |
Martin B, Colin Megill, Angela Oliveira Pisco, Kinsella M, Smith T, Cool J, Haliburton G, Lombardo M, Mani A, Badajoz S, Prins L, Griffin F, Justin T. Kiggins, Dunitz M, McCandless B, Weiden M, Weaver C, Jeremy Freeman, Huang T, Antony M. Carr, Sidney M Bell, Chambers S |
| Rok vydání: |
2021 |
| Předmět: |
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| DOI: |
10.1101/2021.04.05.438318 |
| Popis: |
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@chanzuckerberg.com. |
| Databáze: |
OpenAIRE |
| Externí odkaz: |
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