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

Lukas M. Weber, Arkajyoti Saha, Abhirup Datta, Kasper D. Hansen and Stephanie C. Hicks ()
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Lukas M. Weber: Johns Hopkins Bloomberg School of Public Health
Arkajyoti Saha: University of Washington
Abhirup Datta: Johns Hopkins Bloomberg School of Public Health
Kasper D. Hansen: Johns Hopkins Bloomberg School of Public Health
Stephanie C. Hicks: Johns Hopkins Bloomberg School of Public Health

Nature Communications, 2023, vol. 14, issue 1, 1-12

Abstract: Abstract Feature selection to identify spatially variable genes or other biologically informative 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 .

Date: 2023
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DOI: 10.1038/s41467-023-39748-z

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