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Transformer and graph variational autoencoder to identify microenvironments: A deep learning protocol for spatial transcriptomics. 2025
Karla Paniagua, and
Yufei Huang, and
Shou-Jiang Gao, and
Yidong Chen, and
Yu-Fang Jin, and
Mario Flores
Department of Electrical and Computer Engineering, KLESSE School of Engineering and Integrated Design, University of Texas at San Antonio, San Antonio, TX 78249, USA. Electronic address: karla.paniaguarivera@my.utsa.edu.
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.
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