Institute of Computational Biology, Helmholtz Center Munich, Munich, Germany.
Wellcome Sanger Institute, Cambridge, UK.
Nat Cell Biol. 2023 Feb;25(2):337-350. doi: 10.1038/s41556-022-01072-x. Epub 2023 Feb 2.
The increasing availability of large-scale single-cell atlases has enabled the detailed description of cell states. In parallel, advances in deep learning allow rapid analysis of newly generated query datasets by mapping them into reference atlases. However, existing data transformations learned to map query data are not easily explainable using biologically known concepts such as genes or pathways. Here we propose expiMap, a biologically informed deep-learning architecture that enables single-cell reference mapping. ExpiMap learns to map cells into biologically understandable components representing known 'gene programs'. The activity of each cell for a gene program is learned while simultaneously refining them and learning de novo programs. We show that expiMap compares favourably to existing methods while bringing an additional layer of interpretability to integrative single-cell analysis. Furthermore, we demonstrate its applicability to analyse single-cell perturbation responses in different tissues and species and resolve responses of patients who have coronavirus disease 2019 to different treatments across cell types.
单细胞图谱的大规模可用性不断增加，使得对细胞状态的详细描述成为可能。与此同时，深度学习的进步使得通过将新生成的查询数据集映射到参考图谱中，能够快速分析它们。然而，现有的数据转换学习到的映射查询数据，很难用基因或途径等生物学已知概念来解释。在这里，我们提出了 expiMap，这是一种基于生物学的深度学习架构，能够实现单细胞参考映射。expiMap 学习将细胞映射到可理解的生物学组件中，这些组件代表已知的“基因程序”。在同时细化它们并学习新程序的过程中，学习每个基因程序的每个细胞的活性。我们表明，expiMap 与现有方法相比具有优势，同时为整合单细胞分析带来了额外的可解释性。此外，我们证明了它适用于分析不同组织和物种的单细胞扰动反应，并解析了患有 2019 年冠状病毒病的患者对不同细胞类型治疗的反应。
