DeepST: identifying spatial domains in spatial transcriptomics by deep learning.
IF: 19.160
Cited by: 11


Recent advances in spatial transcriptomics (ST) have brought unprecedented opportunities to understand tissue organization and function in spatial context. However, it is still challenging to precisely dissect spatial domains with similar gene expression and histology in situ. Here, we present DeepST, an accurate and universal deep learning framework to identify spatial domains, which performs better than the existing state-of-the-art methods on benchmarking datasets of the human dorsolateral prefrontal cortex. Further testing on a breast cancer ST dataset, we showed that DeepST can dissect spatial domains in cancer tissue at a finer scale. Moreover, DeepST can achieve not only effective batch integration of ST data generated from multiple batches or different technologies, but also expandable capabilities for processing other spatial omics data. Together, our results demonstrate that DeepST has the exceptional capacity for identifying spatial domains, making it a desirable tool to gain novel insights from ST studies.


Spatial Transcriptomics

MeSH terms

Deep Learning
Gene Expression Profiling


Xu, Chang
Jin, Xiyun
Wei, Songren
Wang, Pingping
Luo, Meng
Xu, Zhaochun
Yang, Wenyi
Cai, Yideng
Xiao, Lixing
Lin, Xiaoyu
Liu, Hongxin
Cheng, Rui
Pang, Fenglan
Chen, Rui
Su, Xi
Hu, Ying
Wang, Guohua
Jiang, Qinghua