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PreprintCC BY-NDbioRxiv

SCG: Spatially Co-Expressed Gene Identification through Spatially Varying Networks

  • I. E. Buker
  • Y. Ni
  • S. C. Hicks
  • J. Kang
  • S. Acharyya1
  1. 1University of Alabama at Birmingham
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Abstract

Spatial transcriptomics has enabled the advancement of gene expression analysis, yet spatial co-expression remains understudied. We introduce spatial covariance regression (SCR), a scalable Bayesian factor-model-based framework for estimation of spatially-resolved gene co-expression networks across tissue domains. These networks provide the spatial map of gene-gene correlations and enable the identification of spatially co-expressed genes (SCGs), which serve as potential prognostic biomarkers and therapeutic targets.

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Cite

BibTeXRIS
BibTeX
@misc{buker2026spatially,
  title = {{SCG: Spatially Co-Expressed Gene Identification through Spatially Varying Networks}},
  author = {I. E. Buker and Y. Ni and S. C. Hicks and J. Kang and S. Acharyya},
  howpublished = {bioRxiv},
  year = {2026},
  month = sep,
  doi = {10.64898/2026.09.01.748618},
  url = {https://www.biorxiv.org/content/10.64898/2026.09.01.748618v1},
  note = {CC BY-ND},
}
RIS
TY  - UNPB
TI  - SCG: Spatially Co-Expressed Gene Identification through Spatially Varying Networks
AU  - I. E. Buker
AU  - Y. Ni
AU  - S. C. Hicks
AU  - J. Kang
AU  - S. Acharyya
PY  - 2026
DA  - 2026/09/01
PB  - bioRxiv
DO  - 10.64898/2026.09.01.748618
UR  - https://www.biorxiv.org/content/10.64898/2026.09.01.748618v1
AB  - Spatial transcriptomics has enabled the advancement of gene expression analysis, yet spatial co-expression remains understudied. We introduce spatial covariance regression (SCR), a scalable Bayesian factor-model-based framework for estimation of spatially-resolved gene co-expression networks across tissue domains. These networks provide the spatial map of gene-gene correlations and enable the identification of spatially co-expressed genes (SCGs), which serve as potential prognostic biomarkers and therapeutic targets.
ER  -