Institute of Biochemistry and Molecular Biology, University of Southern, Odense, Denmark.
Sino-Danish College (SDC), University of Chinese Academy of Sciences, Beijing, China.
Nat Commun. 2023 Dec 20;14(1):8473. doi: 10.1038/s41467-023-44279-8.
Single-cell and single-nucleus RNA-sequencing (sxRNA-seq) is increasingly being used to characterise the transcriptomic state of cell types at homeostasis, during development and in disease. However, this is a challenging task, as biological effects can be masked by technical variation. Here, we present JOINTLY, an algorithm enabling joint clustering of sxRNA-seq datasets across batches. JOINTLY performs on par or better than state-of-the-art batch integration methods in clustering tasks and outperforms other intrinsically interpretable methods. We demonstrate that JOINTLY is robust against over-correction while retaining subtle cell state differences between biological conditions and highlight how the interpretation of JOINTLY can be used to annotate cell types and identify active signalling programs across cell types and pseudo-time. Finally, we use JOINTLY to construct a reference atlas of white adipose tissue (WATLAS), an expandable and comprehensive community resource, in which we describe four adipocyte subpopulations and map compositional changes in obesity and between depots.
单细胞和单核 RNA 测序 (scRNA-seq) 越来越多地被用于描述细胞类型在稳态、发育和疾病过程中的转录组状态。然而，这是一项具有挑战性的任务，因为生物学效应可能会被技术变化所掩盖。在这里，我们提出了 JOINTLY，这是一种能够在批次之间对 scRNA-seq 数据集进行联合聚类的算法。JOINTLY 在聚类任务中的表现与最先进的批次整合方法相当或更好，并且优于其他内在可解释的方法。我们证明了 JOINTLY 在保留生物学条件之间微妙的细胞状态差异的同时，能够抵抗过度校正，并强调了如何使用 JOINTLY 的解释来注释细胞类型，并识别跨细胞类型和伪时间的活跃信号程序。最后，我们使用 JOINTLY 构建了一个白色脂肪组织 (WATLAS) 的参考图谱，这是一个可扩展和全面的社区资源，其中我们描述了四种脂肪细胞亚群，并绘制了肥胖和不同脂肪组织之间的组成变化。
