Transformer and graph variational autoencoder to identify microenvironments: A deep learning protocol for spatial transcriptomics

作者：Karla Paniagua, Yufei Huang, Shou‐Jiang Gao, Yidong Chen, Yu-Fang Jin, Mario Flores · 发表于：STAR Protocols · 年份：2025 · DOI：10.1016/j.xpro.2025.104206 · 被引用次数：1 · 研究领域：Single-cell and spatial transcriptomics、Gene expression and cancer classification、Cell Image Analysis Techniques

We present transformer and graph variational autoencoder to identify microenvironments (TG-ME), a computational framework that integrates transformer and graph variational autoencoders to dissect spatial niches using spatial transcriptomics and morphological images. This protocol outlines data normalization, spatial transcriptomics integration, morphological feature extraction, and niche profiling. Using deep learning, TG-ME enables robust niche clustering applicable to healthy, tumor, and infected tissues. For complete details on the use and execution of this protocol, please refer to Paniagua et al. 1