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nnSVG for the scalable identification of spatially variable genes using nearest-neighbor Gaussian processes

Lukas M. Weber, Arkajyoti Saha, Abhirup Datta, Kasper D. Hansen, Stephanie C. Hicks
doi: https://doi.org/10.1101/2022.05.16.492124

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doi 
https://doi.org/10.1101/2022.05.16.492124
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  • June 15, 2023.

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The copyright holder for this preprint is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made available under a CC-BY 4.0 International license.

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  1. Lukas M. Weber1,
  2. Arkajyoti Saha2,
  3. Abhirup Datta1,
  4. Kasper D. Hansen1 and
  5. Stephanie C. Hicks1,*
  1. 1Department of Biostatistics, Johns Hopkins Bloomberg School of Public Health, Baltimore, MD, USA
  2. 2Department of Statistics, University of Washington, Seattle, WA, USA
  1. ↵*Corresponding author: shicks19{at}jhu.edu
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Posted June 15, 2023.
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nnSVG for the scalable identification of spatially variable genes using nearest-neighbor Gaussian processes
Lukas M. Weber, Arkajyoti Saha, Abhirup Datta, Kasper D. Hansen, Stephanie C. Hicks
bioRxiv 2022.05.16.492124; doi: https://doi.org/10.1101/2022.05.16.492124
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