Authors
Zhicong Wang, Jing Li, Liqing Xie, Yiran Wang, Yongtian Wang, Jing Chen, Xuequn Shang, Xingyi Li, Zhaowen Liu, Jialu Hu
Published in
Briefings in bioinformatics. Volume 27. Issue 4. Jul 03, 2026.
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.
PMID:
42447338
Bibliographic data and abstract were imported from PubMed on 15 Jul 2026.
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