JOINT IDENTIFICATION OF SPATIALLY VARIABLE GENES VIA A NETWORK-ASSISTED BAYESIAN REGULARIZATION APPROACH

• 成果类型: Article
• 署名作者: Wu, Mingcong; Li, Yang; Ma, Shuangge; Wu, Mengyun
• 署名单位: Renmin University of China; Renmin University of China; Yale University; Shanghai University of Finance & Economics
• 刊物名称: ANNALS OF APPLIED STATISTICS
• ISSN/ISSBN: 1932-6157; 1941-7330
• DOI: 10.1214/25-AOAS2097
• 发表日期: 2025-12
• 页码: 2705-2723
• 关键词: Spatial transcriptomic data; Network analysis; Bayesian regularization; architecture; selection

摘要

Identifying genes that display spatial patterns is critical to investigating expression interactions within a spatial context and further dissecting biological understanding of complex mechanistic functionality. Despite the increase in statistical methods designed to identify spatially variable genes, they are mostly based on marginal analysis and share the limitation that the dependence (network) structures among genes are not well accommodated, where a biological process usually involves changes in multiple genes that interact in a complex network. In addition, the latent cellular composition within the spots can introduce confounding variations, negatively affecting the accuracy of the identification. In this study we develop a novel Bayesian regularization approach for spatial transcriptomic data, with confounding variations induced by varying cellular distributions effectively corrected. Significantly advancing from existing studies, a thresholded graph Laplacian regularization is proposed to simultaneously identify spatially variable genes and accommodate the network structure among genes. The proposed method is based on a zero-inflated negative binomial distribution, effectively accommodating the count nature, zero inflation, and overdispersion of spatial transcriptomic data. Extensive simulations and applications to real data demonstrate the competitive performance of the proposed method.