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Updated: Jun 16, 2025

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

Kinnary Shah1, Boyi Guo1, Stephanie C Hicks1,2,3,4

  • 1Department of Biostatistics, Johns Hopkins Bloomberg School of Public Health, 615 N Wolfe Street, Baltimore, MD 21205, United States.

Biostatistics (Oxford, England)
|June 14, 2025
PubMed
Summary
This summary is machine-generated.

Spatially resolved transcriptomics (SRT) analysis can be biased by log-transformation. The new "spoon" framework uses empirical Bayes to remove this bias, improving the identification of spatially variable genes (SVGs).

Keywords:
Gaussian process regressionempirical Bayesmean–variance biasspatial transcriptomicsspatially variable gene

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Area of Science:

  • Genomics
  • Bioinformatics
  • Computational Biology

Background:

  • Identifying spatially variable genes (SVGs) is crucial for analyzing spatially resolved transcriptomics (SRT) data.
  • Existing methods for ranking SVGs often rely on P-values or effect sizes, potentially introducing technical biases.
  • Log-transformation in RNA sequencing data analysis is known to violate the mean-variance relationship, affecting gene count analysis.

Purpose of the Study:

  • To demonstrate the mean-variance relationship in spatially resolved transcriptomics data.
  • To introduce 'spoon', a novel statistical framework to address and remove bias in SVG identification.
  • To improve the accuracy of prioritizing spatially variable genes in SRT datasets.

Main Methods:

  • Demonstration of the mean-variance relationship in SRT data.
  • Development of 'spoon', a statistical framework employing empirical Bayes techniques.
  • Validation of the method using both simulated and real SRT datasets.

Main Results:

  • The mean-variance relationship was confirmed in SRT data.
  • 'spoon' effectively removes the technical bias associated with log-transformation.
  • The framework leads to more accurate prioritization of spatially variable genes.

Conclusions:

  • The proposed 'spoon' framework offers a statistically robust approach for SVG identification in SRT.
  • By correcting for the mean-variance bias, 'spoon' enhances the reliability of spatial gene expression analysis.
  • A software implementation is available, facilitating the adoption of this improved methodology.