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PreprintCC BY-NCbioRxiv

Biological and technical spatial variation in spatial transcriptomics

  • K. C. Jackson
  • G. Gorin
  • L. Pachter1
  1. 1California Institute of Technology
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Abstract

Spatial transcriptomics technologies enable the identification of spatially variable genes that can shed light on tissue structure and function. However, spatial variability of gene expression can be due not only to biological function but also technical artifact. We develop a model for spatially variable capture that forms part of a bursty transcription model, allowing us to distinguish biological variability from spatially variable technical noise. Our approach is implemented in a program called stipple, which we demonstrate can be useful for identifying and interpreting spatial gene expression variation from spatial transcriptomics assays.

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Cite

BibTeXRIS
BibTeX
@misc{jackson2026biological,
  title = {{Biological and technical spatial variation in spatial transcriptomics}},
  author = {K. C. Jackson and G. Gorin and L. Pachter},
  howpublished = {bioRxiv},
  year = {2026},
  month = oct,
  doi = {10.64898/2026.10.13.758896},
  url = {https://www.biorxiv.org/content/10.64898/2026.10.13.758896v1},
  note = {CC BY-NC},
}
RIS
TY  - UNPB
TI  - Biological and technical spatial variation in spatial transcriptomics
AU  - K. C. Jackson
AU  - G. Gorin
AU  - L. Pachter
PY  - 2026
DA  - 2026/10/13
PB  - bioRxiv
DO  - 10.64898/2026.10.13.758896
UR  - https://www.biorxiv.org/content/10.64898/2026.10.13.758896v1
AB  - Spatial transcriptomics technologies enable the identification of spatially variable genes that can shed light on tissue structure and function. However, spatial variability of gene expression can be due not only to biological function but also technical artifact. We develop a model for spatially variable capture that forms part of a bursty transcription model, allowing us to distinguish biological variability from spatially variable technical noise. Our approach is implemented in a program called stipple, which we demonstrate can be useful for identifying and interpreting spatial gene expression variation from spatial transcriptomics assays.
ER  -