Covariance Nonstationarity is Evident in Spatial Transcriptomics and Provides a New Categorization of Spatially Varying Genes
Background: Gaussian 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. Results: Across 13 Visium datasets, we use approximate Bayes factors from R-INLA to compare stationary and non-stationary Matern…
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