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PreprintCC BYbioRxiv

Covariance Nonstationarity is Evident in Spatial Transcriptomics and Provides a New Categorization of Spatially Varying Genes

  • P. Velidi1
  • Z. Wei
  • F. Nathoo
  1. 1University of Victoria
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Abstract

Gaussian process models underlie many spatial transcriptomics tools but typically assume stationary covariance. To test this assumption, we use approximate Bayes factors from R-INLA to compare stationary and non-stationary Matern covariance functions across 12 Visium datasets; covariance non-stationarity is substantially evident as it appears in 3% to 50% of genes across datasets. We find that gene sets associated with effector functions are enriched among genes favoring non-stationary spatial covariance.

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Cite

BibTeXRIS
BibTeX
@misc{velidi2026covariance,
  title = {{Covariance Nonstationarity is Evident in Spatial Transcriptomics and Provides a New Categorization of Spatially Varying Genes}},
  author = {P. Velidi and Z. Wei and F. Nathoo},
  howpublished = {bioRxiv},
  year = {2026},
  month = aug,
  doi = {10.64898/2026.08.10.743911},
  url = {https://www.biorxiv.org/content/10.64898/2026.08.10.743911v2},
  note = {CC BY},
}
RIS
TY  - UNPB
TI  - Covariance Nonstationarity is Evident in Spatial Transcriptomics and Provides a New Categorization of Spatially Varying Genes
AU  - P. Velidi
AU  - Z. Wei
AU  - F. Nathoo
PY  - 2026
DA  - 2026/08/10
PB  - bioRxiv
DO  - 10.64898/2026.08.10.743911
UR  - https://www.biorxiv.org/content/10.64898/2026.08.10.743911v2
AB  - Gaussian process models underlie many spatial transcriptomics tools but typically assume stationary covariance. To test this assumption, we use approximate Bayes factors from R-INLA to compare stationary and non-stationary Matern covariance functions across 12 Visium datasets; covariance non-stationarity is substantially evident as it appears in 3% to 50% of genes across datasets. We find that gene sets associated with effector functions are enriched among genes favoring non-stationary spatial covariance.
ER  -