DeepST: identifying spatial domains in spatial transcriptomics by deep learning

By Chang Xu, Xiyun Jin, Songren Wei, Pingping Wang, Meng Luo, Zhaochun Xu, Wenyi Yang, Yideng Cai, Lixing Xiao, Xiaoyu Lin and 8 more. Nucleic Acids Research, 2022

298 citations recorded by the source corpus.

Abstract 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.

Subfields: Immunology, Molecular Biology

View Paper: https://www.semanticscholar.org/paper/4d6838fdbdb44a3ba802104c2da3e8d83188aa51

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In Molecular Biology

1. Huan Fu
2. Kok Siong Ang
3. Jinmiao Chen

Citations in the corpus

• Unsupervised spatially embedded deep representation of spatial transcriptomics (2021, methodology): “Next, current methods that integrate gene expression with associated spatial information were used to benchmark SEDR, namely SpatialLDA [33], Giotto [26], stLearn [28], SpaGene[34], SpaGCN [29], BayesSpace [27], UTAG [32], DeepST [30], and STAGATE [31] (Additional file 1: Supplementary methods).”