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Updated: Jul 16, 2026

Mining Spatial Transcriptomics Datasets using DeepSpaceDB
Published on: September 5, 2025
Variational sparse Gaussian-process method for detecting spatially variable genes and cellular interactions in
Zhicong Wang1,2,3, Jing Li1, Liqing Xie3,4
1School of Computer Science, Northwestern Polytechnical University, 1 Dongxiang Rd., Chang'an Dist., Xi'an, Shaanxi, 710129, China.
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.
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