Annotation of spatially resolved single-cell data with STELLAR
Annotation of spatially resolved single-cell data with STELLAR
复制标题
DOI：
10.1038/s41592-022-01651-8
复制
发表时间：
2022-10-24
期刊：
影响因子：
48
通讯作者：
Leskovec, Jure
中科院分区：
文献类型：
--
作者：
Brbic, Maria;Cao, Kaidi;Leskovec, Jure
从空间分辨的单细胞中准确地进行细胞类型注释对于理解作为组织组织结构基础的功能空间生物学至关重要。然而，当前用于注释空间分辨单细胞数据的计算方法通常基于为分离的单细胞技术建立的技术，因此不考虑空间组织。在这里，我们介绍了STELLAR，这是一种几何深度学习方法，用于在空间分辨的单细胞数据集中发现和识别细胞类型。STELLAR自动将细胞分配给注释参考数据集中存在的细胞类型，并发现新的细胞类型和细胞状态。STELLAR跨不同解剖区域、不同组织和不同供体传输注释，并学习捕获高阶组织结构的细胞表示。我们成功地将STELLAR应用于CODEX多重荧光显微镜数据和多重RNA成像数据集。在人类生物分子图谱计划中，STELLAR已经注释了260万个空间分辨的单细胞，大大节省了时间。STELLAR（空间细胞学习）是一种几何深度学习模型，它与空间分辨的单细胞数据集一起使用，可以基于参考数据集在未注释的数据集中分配细胞类型，并发现新的细胞类型。
Accurate cell-type annotation from spatially resolved single cells is crucial to understand functional spatial biology that is the basis of tissue organization. However, current computational methods for annotating spatially resolved single-cell data are typically based on techniques established for dissociated single-cell technologies and thus do not take spatial organization into account. Here we present STELLAR, a geometric deep learning method for cell-type discovery and identification in spatially resolved single-cell datasets. STELLAR automatically assigns cells to cell types present in the annotated reference dataset and discovers novel cell types and cell states. STELLAR transfers annotations across different dissection regions, different tissues and different donors, and learns cell representations that capture higher-order tissue structures. We successfully applied STELLAR to CODEX multiplexed fluorescent microscopy data and multiplexed RNA imaging datasets. Within the Human BioMolecular Atlas Program, STELLAR has annotated 2.6 million spatially resolved single cells with dramatic time savings.STELLAR (spatial cell learning) is a geometric deep learning model that works with spatially resolved single-cell datasets to both assign cell types in unannotated datasets based on a reference dataset and discover new cell types.