Journal Article

DZNE-2026-00753

http://join2-wiki.gsi.de/foswiki/pub/Main/Artwork/join2_logo100x88.png Variational sparse Gaussian-process method for detecting spatially variable genes and cellular interactions in spatial transcriptomics.

Wang, Z. (First author)DZNE* ; Li, J. ; Xie, L.DZNE* ; Wang, Y. ; Wang, Y. ; Chen, J. ; Shang, X. ; Li, X. ; Liu, Z. ; Hu, J. (Last author)DZNE*

2026
Oxford University Press Oxford [u.a.]

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Please use a persistent id in citations: doi:10.1093/bib/bbag371

Abstract: Advanced spatially resolved transcriptomic (SRT) technologies preserve the spatial context of gene expression within tissues, enabling the study of context-dependent transcriptional regulation. Here, we propose a variational inference-assisted sparse Gaussian-process (VISGP) framework for identifying spatially variable genes (SVGs) and inferring spatially dependent cellular interactions from SRT data. VISGP combines sparse Gaussian-process approximations with variational inference via inducing variables to reduce computational and memory costs while enabling gene-specific adaptation of spatial covariance structures. Across simulated data and four real SRT datasets, VISGP detected more SVGs than existing methods and identified 85 spatially constrained ligand-receptor pairs that were missed by alternative approaches. Together, VISGP provides a scalable and statistically grounded strategy for decoding spatial gene regulation and cell-cell communication, yielding biological insights into cellular heterogeneity and cancer pathology.

Keyword(s): cellular interactions ; ligand–receptor interactions ; sparse Gaussian process ; spatial transcriptomics ; spatially variable genes ; variational inference

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Contributing Institute(s):

1. Population & Clinical Neuroepidemiology (AG Aziz)
2. Translational Biogerontology (AG Ehninger)

Research Program(s):

1. 354 - Disease Prevention and Healthy Aging (POF4-354) (POF4-354)
2. 352 - Disease Mechanisms (POF4-352) (POF4-352)

Appears in the scientific report 2026

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