Covariance Nonstationarity is Evident in Spatial Transcriptomics and Provides a New Categorization of Spatially Varying Genes
Velidi, P.; Wei, Z.; Nathoo, F.
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BackgroundGaussian process models underlie many spatial transcriptomics tools but typically assume stationary covariance. While typically ignored, non-stationarity of spatial covariance in gene expression may correspond to tissue heterogeneity or cell aggregates. ResultsAcross 13 Visium datasets, we use approximate Bayes factors from R-INLA to compare stationary and non-stationary Matern covariance functions. Evidence for covariance non-stationarity appears in 3% to 50% of genes across tissue samples. We further characterize the power and false discovery rate of the Bayesian analysis of non-stationarity. We find that gene sets associated with immune, cytokine, and other effector functions are enriched among genes favoring non-stationary spatial covariance. ConclusionsCovariance stationarity is not a benign technical simplification in spatial transcriptomics; it is frequently violated, the violation is biologically structured, and it changes the definition and classification of spatially varying genes.
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