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nnSVG for the scalable identification of spatially variable genes using nearest-neighbor Gaussian processes
View ORCID ProfileLukas M. Weber, Arkajyoti Saha, View ORCID ProfileAbhirup Datta, View ORCID ProfileKasper D. Hansen, View ORCID ProfileStephanie C. Hicks
doi: https://doi.org/10.1101/2022.05.16.492124
Lukas M. Weber
1Department of Biostatistics, Johns Hopkins Bloomberg School of Public Health, Baltimore, MD, USA
Arkajyoti Saha
2Department of Statistics, University of Washington, Seattle, WA, USA
Abhirup Datta
1Department of Biostatistics, Johns Hopkins Bloomberg School of Public Health, Baltimore, MD, USA
Kasper D. Hansen
1Department of Biostatistics, Johns Hopkins Bloomberg School of Public Health, Baltimore, MD, USA
Stephanie C. Hicks
1Department of Biostatistics, Johns Hopkins Bloomberg School of Public Health, Baltimore, MD, USA
Posted June 15, 2023.
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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