Covariance Nonstationarity is Evident in Spatial Transcriptomics Providing a New Categorization of Spatially Varying Genes
Year: 2026
Type: preprint
Abstract: Gaussian process models underlie many spatial transcriptomics tools but typically assume stationary covariance. To test this assumption, we use approximate Bayes factors from R-INLA to compare stationary and non-stationary Matérn covariance functions across 12 Visium datasets; covariance non-stationarity is substantially evident as it appears in 3% to 50% of genes across datasets. We find that gene sets associated with effector functions are enriched among genes favoring non-stationary spatial covariance.
Authors: Puneet Velidi, Zhengxiao Wei, Farouk S. Nathoo
Institution: University of Victoria
Language: English
Subfield: Molecular Biology
Domain: Life Sciences
Open Access status: green
OpenAlex ID: W7203548411