SpaSEG: unsupervised deep learning for multi-task analysis of spatially resolved transcriptomics

Abstract Spatially resolved transcriptomics (SRT) for characterizing spatial cellular heterogeneities in tissue environments requires systematic analytical approaches to elucidate gene expression variations within their physiological context. Here, we introduce SpaSEG, an unsupervised deep learning...

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Главные авторы:	Yong Bai, Xiangyu Guo, Keyin Liu, Bingjie Zheng, Yilin Wei, Yingyue Wang, Wenxi Zhang, Qiuhong Luo, Jianhua Yin, Liang Wu, Yuxiang Li, Yong Zhang, Ao Chen, Xiangdong Wang, Xun Xu, Chuanyu Liu, Xin Jin
Формат:	Artigo
Язык:	Inglês
Опубликовано:	BMC 2025-07-01
Серии:	Genome Biology
Предметы:	Spatially resolved transcriptomics Deep learning Spatial domain identification Multi-section integration Spatially variable gene Cell–cell interaction
Online-ссылка:	https://doi.org/10.1186/s13059-025-03697-1
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