Bayesian Subgroup Learning of Spatially Resolved Transcriptomics Data

Authors

• Hou-Cheng Yang
• Huimin Li, The University of Texas Rio Grande Valley
• Guanyu Hu
• Qiwei Li, University of Texas at Dallas

Document Type

Article

Publication Date

6-22-2026

Abstract

Recent advancements in spatially resolved transcriptomics (SRT) technologies have enabled the comprehensive molecular and spatial characterization of single cells, providing valuable insights into the cellular organization of tissues. SRT techniques, such as single-molecule fluorescence in situ hybridization (FISH)-based methods (e.g., seqFISH, STARmap) and next-generation sequencing (NGS)-based methods (e.g., spatial transcriptomics, 10x Visium), allow for the measurement of gene expression across large populations of cells or tissue spots. These approaches generate high-dimensional data that integrate both molecular profiles and spatial context, which is crucial for understanding tissue structure and function in areas like development, neuroscience, and cancer biology. Identifying spatially variable (SV) genes, whose expression patterns differ across spatial locations, is a key step in analyzing these complex spatial transcriptomic maps. To enhance our understanding of the spatial profiles of SV genes, we propose a Bayesian nonparametric zero-inflated Poisson (ZIP) regression model for clustering these genes. Our model explicitly accounts for zero-inflation in the data, uses non-negative matrix factorization to uncover gene expression patterns, and incorporates Moran’s I (MI) basis functions to address potential confounding. Additionally, the model infers the number of clusters directly from the data, obviating the need for pre-specifying the number of clusters. We demonstrate the utility of this approach on two SRT datasets, showing that it provides more robust and interpretable clustering of SV genes, opening new avenues for understanding complex biological processes.

Comments

This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. 

Recommended Citation

Yang, Hou-Cheng, Huimin Li, Guanyu Hu, and Qiwei Li. "Bayesian Subgroup Learning of Spatially Resolved Transcriptomics Data." Statistics in Biosciences (2026): 1-29. https://doi.org/10.1007/s12561-026-09526-8

Creative Commons License

This work is licensed under a Creative Commons Attribution 4.0 International License.

Publication Title

Statistics in Biosciences

DOI

10.1007/s12561-026-09526-8