Gene signature extraction and cell identity recognition at the single-cell level with Cell-ID
Gene signature extraction and cell identity recognition at the single-cell level with Cell-ID
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DOI：
10.1038/s41587-021-00896-6
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发表时间：
2021-04-29
影响因子：
46.9
通讯作者：
Rausell, Antonio
中科院分区：
文献类型：
--
作者：
Cortal, Akira;Martignetti, Loredana;Rausell, Antonio
由于与高通量单细胞测序相关的随机性，目前用于探索细胞类型多样性的方法依赖于基于聚类的计算方法，其中异质性的特征在于细胞亚群而不是完整的单细胞分辨率。在这里，我们提出了Cell-ID，这是一种无聚类的多变量统计方法，用于从单细胞测序数据中稳健地提取每个细胞的基因签名。我们将Cell-ID应用于来自多个人类和小鼠样本的数据，包括血细胞，胰岛和气道，肠道和嗅觉上皮，以及全面的小鼠细胞图谱数据集。我们证明了Cell-ID签名在不同供体、来源组织、物种和单细胞组学技术中是可重复的，并且可以用于跨数据集的自动细胞类型注释和细胞匹配。Cell-ID改善了单个细胞水平的生物学解释，从而能够发现以前未表征的罕见细胞类型或细胞状态。Cell-ID是一个开源的R软件包，可以在单细胞水平上分析多个样本的细胞类型异质性和细胞同一性。
Because of the stochasticity associated with high-throughput single-cell sequencing, current methods for exploring cell-type diversity rely on clustering-based computational approaches in which heterogeneity is characterized at cell subpopulation rather than at full single-cell resolution. Here we present Cell-ID, a clustering-free multivariate statistical method for the robust extraction of per-cell gene signatures from single-cell sequencing data. We applied Cell-ID to data from multiple human and mouse samples, including blood cells, pancreatic islets and airway, intestinal and olfactory epithelium, as well as to comprehensive mouse cell atlas datasets. We demonstrate that Cell-ID signatures are reproducible across different donors, tissues of origin, species and single-cell omics technologies, and can be used for automatic cell-type annotation and cell matching across datasets. Cell-ID improves biological interpretation at individual cell level, enabling discovery of previously uncharacterized rare cell types or cell states. Cell-ID is distributed as an open-source R software package.Cell-ID facilitates the analysis of cell-type heterogeneity and cell identity across multiple samples at the single-cell level.