Although tremendous effort has been put into cell-type annotation, identification of previously uncharacterized cell types in heterogeneous single-cell RNA-seq data remains a challenge. Here we present MARS, a meta-learning approach for identifying and annotating known as well as new cell types. MARS overcomes the heterogeneity of cell types by transferring latent cell representations across multiple datasets. MARS uses deep learning to learn a cell embedding function as well as a set of landmarks in the cell embedding space. The method has a unique ability to discover cell types that have never been seen before and annotate experiments that are as yet unannotated. We apply MARS to a large mouse cell atlas and show its ability to accurately identify cell types, even when it has never seen them before. Further, MARS automatically generates interpretable names for new cell types by probabilistically defining a cell type in the embedding space.

尽管已经为细胞类型注释付出了巨大的努力，但是在异质单细胞RNA-seq数据中鉴定以前未表征的细胞类型仍然是一个挑战。在这里，我们介绍MARS，这是一种用于识别和注释已知以及新细胞类型的元学习方法。MARS通过跨多个数据集传输潜在细胞表示，克服了细胞类型的异质性。MARS使用深度学习来学习单元嵌入功能以及单元嵌入空间中的一组地标。该方法具有发现以前从未见过的细胞类型并注释尚未注释的实验的独特能力。我们将MARS应用于大型小鼠细胞图集，并展示了其准确识别细胞类型的能力，即使以前从未见过。进一步，