生物
空间语境意识
背景(考古学)
标杆管理
空间分析
空间生态学
空间学习
转录组
计算生物学
人工智能
计算机科学
神经科学
基因
基因表达
遗传学
生态学
古生物学
业务
地质学
遥感
营销
海马体
作者
Chang Xu,Xiyun Jin,Songren Wei,Pingping Wang,Meng Luo,Zhaochun Xu,Wenyi Yang,Yideng Cai,Lixing Xiao,Xiaoyu Lin,Hongxin Liu,Rui Cheng,Fenglan Pang,Rui Chen,Xi Su,Ying Hu,Guohua Wang,Qinghua Jiang
摘要
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.
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