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Updated: Jun 6, 2025

Spatial Profiling of Protein and RNA Expression in Tissue: An Approach to Fine-Tune Virtual Microdissection
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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, Baltimore, MD, USA.

Biorxiv : the Preprint Server for Biology
|November 22, 2024
PubMed
Summary
This summary is machine-generated.

Spatially variable genes (SVGs) identification in spatial transcriptomics is biased by a mean-variance relationship. The spoon framework uses Empirical Bayes to remove this bias, improving SVG prioritization in gene expression data.

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 spatial transcriptomics data.
  • Current methods for SVG ranking may be affected by the mean-variance relationship, a technical bias observed in RNA-sequencing data.
  • This bias can lead to inaccurate prioritization of genes based on expression levels and variance.

Purpose of the Study:

  • To demonstrate the presence of the mean-variance relationship in spatial transcriptomics data.
  • To introduce spoon, a novel statistical framework designed to mitigate this bias.
  • To improve the accuracy of identifying and prioritizing spatially variable genes.

Main Methods:

  • Demonstration of the mean-variance relationship in spatial transcriptomics datasets.
  • Development of spoon, a statistical framework employing Empirical Bayes techniques.
  • Validation of spoon using simulated and real-world spatial transcriptomics data.

Main Results:

  • Confirmation of the mean-variance relationship in spatial transcriptomics.
  • spoon effectively removes the technical bias, leading to more accurate SVG identification.
  • Improved prioritization of SVGs compared to existing methods.

Conclusions:

  • The mean-variance relationship poses a challenge for accurate SVG identification in spatial transcriptomics.
  • spoon provides a robust solution for bias correction, enhancing the reliability of spatial gene expression analysis.
  • The spoon software implementation facilitates broader application of this improved methodology.