RT Journal Article
SR Electronic
T1 nnSVG for the scalable identification of spatially variable genes using nearest-neighbor Gaussian processes
JF bioRxiv
FD Cold Spring Harbor Laboratory
SP 2022.05.16.492124
DO 10.1101/2022.05.16.492124
A1 Weber, Lukas M.
A1 Saha, Arkajyoti
A1 Datta, Abhirup
A1 Hansen, Kasper D.
A1 Hicks, Stephanie C.
YR 2023
UL http://biorxiv.org/content/early/2023/06/15/2022.05.16.492124.abstract
AB 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.Competing Interest StatementThe authors have declared no competing interest.