ArticleBiostatistics (Oxford, England)2025
Addressing the mean-variance relationship in spatially resolved transcriptomics data with spoon.
Article in Biostatistics (Oxford, England), 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 papers.
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3 citing papers in PubMed.
- A comparative study of statistical methods for identifying differentially expressed genes in spatial transcriptomics.PLoS computational biology · 2026Article
- Spatial transcriptomics on an expanded dataset at the brain-electrode interface: exploration of variability and identification of novel biomarkers.Frontiers in neuroscience · 2026Article
- A comparative study of statistical methods for identifying differentially expressed genes in spatial transcriptomics.bioRxiv : the preprint server for biology · 2025Article
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Abstract
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.
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