Institute of Epigenetics and Stem Cells, Helmholtz Munich, D-81377 Munich, Germany.
Institute of Functional Epigenetics, Helmholtz Munich, D-85764 Neuherberg, Germany.
Development. 2023 Jun 1;150(11). doi: 10.1242/dev.201264. Epub 2023 Jun 8.
A powerful feature of single-cell genomics is the possibility of identifying cell types from their molecular profiles. In particular, identifying novel rare cell types and their marker genes is a key potential of single-cell RNA sequencing. Standard clustering approaches perform well in identifying relatively abundant cell types, but tend to miss rarer cell types. Here, we have developed CIARA (Cluster Independent Algorithm for the identification of markers of RAre cell types), a cluster-independent computational tool designed to select genes that are likely to be markers of rare cell types. Genes selected by CIARA are subsequently integrated with common clustering algorithms to single out groups of rare cell types. CIARA outperforms existing methods for rare cell type detection, and we use it to find previously uncharacterized rare populations of cells in a human gastrula and among mouse embryonic stem cells treated with retinoic acid. Moreover, CIARA can be applied more generally to any type of single-cell omic data, thus allowing the identification of rare cells across multiple data modalities. We provide implementations of CIARA in user-friendly packages available in R and Python.
单细胞基因组学的一个强大功能是有可能根据其分子特征来识别细胞类型。特别是，识别新的罕见细胞类型及其标记基因是单细胞 RNA 测序的一个关键潜力。标准的聚类方法在识别相对丰富的细胞类型方面表现良好，但往往会错过更罕见的细胞类型。在这里，我们开发了 CIARA（用于鉴定稀有细胞类型标记物的独立聚类算法），这是一种独立于聚类的计算工具，旨在选择可能是稀有细胞类型标记物的基因。由 CIARA 选择的基因随后与常见的聚类算法集成，以分离出稀有细胞类型的群体。CIARA 在稀有细胞类型检测方面优于现有的方法，我们使用它来发现人类原肠胚和用视黄酸处理的小鼠胚胎干细胞中以前未表征的稀有细胞群。此外，CIARA 可以更普遍地应用于任何类型的单细胞组学数据，从而可以在多个数据模态中识别罕见细胞。我们在 R 和 Python 中提供了用户友好的包的 CIARA 实现。
