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nnSVG: 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
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Arkajyoti Saha
2Department of Statistics, University of Washington, Seattle, WA, USA
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Abhirup Datta
1Department of Biostatistics, Johns Hopkins Bloomberg School of Public Health, Baltimore, MD, USA
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Kasper D. Hansen
1Department of Biostatistics, Johns Hopkins Bloomberg School of Public Health, Baltimore, MD, USA
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Stephanie C. Hicks
1Department of Biostatistics, Johns Hopkins Bloomberg School of Public Health, Baltimore, MD, USA
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  • ORCID record for Stephanie C. Hicks
  • For correspondence: shicks19@jhu.edu
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Abstract

Feature selection to identify spatially variable genes is a key step during analyses of spatially-resolved transcriptomics data. Here, we propose nnSVG, a scalable approach to identify spatially variable genes based on nearest-neighbor Gaussian processes. Our method (i) identifies genes that vary in expression continuously across the entire tissue or within a priori defined spatial domains, (ii) uses gene-specific estimates of length scale parameters within the Gaussian process models, and (iii) scales linearly with the number of spatial locations. We demonstrate the performance of our method using experimental data from several technological platforms and simulations. A software implementation is available at https://bioconductor.org/packages/nnSVG.

Competing Interest Statement

The authors have declared no competing interest.

Copyright 
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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Posted May 16, 2022.
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nnSVG: 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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nnSVG: 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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