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An important task in the analysis of spatially resolved transcriptomics (SRT) data is to identify spatially variable genes (SVGs), or genes that vary in a 2D space. Current approaches rank SVGs based on either   $ P $  -values or an effect size, such as the proportion of spatial variance. However, previous work in the analysis of RNA-sequencing data identified a technical bias with log-transformation, violating the “mean–variance relationship” of gene counts, where highly expressed genes are more likely to have a higher variance in counts but lower variance after log-transformation. Here, we demonstrate the mean–variance relationship in SRT data. Furthermore, we propose  spoon , a statistical framework using empirical Bayes techniques to remove this bias, leading to more accurate prioritization of SVGs. We demonstrate the performance of  spoon  in both simulated and real SRT data. A software implementation of our method is available at  https://bioconductor.org/packages/spoon .

Addressing the mean–variance relationship in spatially resolved transcriptomics data with spoon