文献类型：期刊文献

英文题名：Spatial domain recognition for multi-slice spatial transcriptomics based on self encoder adversarial training

作者：Zhang, Xueqin[1];Peng, Xuemei[1];Zhu, Huitong[1];Ding, Weihong[2];Zhou, Yunlan[3];Wu, Zhichao[1]

机构：[1]East China Univ Sci & Technol, Sch Informat Sci & Engn, Shanghai 200237, Peoples R China;[2]Fudan Univ, Huashan Hosp, Shanghai 200040, Peoples R China;[3]Shanghai Jiao Tong Univ, Xinhua Hosp, Sch Med, Dept Clin Lab, Shanghai 200092, Peoples R China

年份：2026

卷号：115

外文期刊名：BIOMEDICAL SIGNAL PROCESSING AND CONTROL

收录：;EI(收录号：20260119845763);WOS:【SCI-EXPANDED(收录号:WOS:001656459900001)】；

基金：This work was supported by the Major Program of National Fund of Philosophy and Social Science of China (Grant No. 23 & ZD142) .

语种：英文

外文关键词：Spatial transcriptomics; Multi-slice integration; Spatial clustering; Adversarial autoencoder

摘要：Recent advances in spatial transcriptomics technology have facilitated the generation of increasingly diverse datasets, offering enhanced opportunities to explore organizational structure and function in a spatial context. However, the effective integration and analysis of such data remain challenging. To effectively integrate multi-slice information, we propose STBCGAE, an adversarial autoencoder-based framework for spatial domain identification in multi-slice spatial transcriptomics data. STBCGAE employs mutual nearest neighbor and nearest spot iterative algorithms to align the spatial positions across multiple slices, simultaneously establishing more precise cross-slice spatial relationships through the construction of a 3D neighbor map. To generate more effective feature embeddings, STBCGAE integrates batch information, gene expression and spatial information using a graph neural network-based autoencoder so that the model can effectively differentiate between technical variants and biological signals. Moreover, to eliminate batch effects, we introduce a batch classifier to train against the encoder. Finally, spatial clustering is performed using the Mclust method to identify spatial domains with expression profiles. By performing extensive experiments on multiple datasets, we demonstrate the capability of STBCGAE to effectively integrate multiple batches of samples in a variety of scenarios, significantly improving the accuracy of multi-slice spatial domain recognition.