A set-theoretic definition of cell types with an algebraic structure on gene regulatory networks and application in annotation of RNA-seq data.
A set-theoretic definition of cell types with an algebraic structure on gene regulatory networks and application in annotation of RNA-seq data.
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DOI：
10.1016/j.stemcr.2022.10.015
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发表时间：
2023-01-10
影响因子：
5.9
通讯作者：
Okano, Hideyuki
中科院分区：
文献类型：
--
作者：
Okano, Yuji;Kase, Yoshitaka;Okano, Hideyuki
单细胞 RNA 测序 (RNA-seq) 的出现从根本上改变了对细胞多样性的观察。尽管 RNA-seq 数据的注释需要保留同一身份细胞之间的属性，但使用传统方法的注释尚无法捕获细胞类型的通用特征。对细胞表达水平的分析无法准确注释，因为转录的差异不一定能解释细胞功能方面的生物学特征，而且数据本身并不能告知细胞类型和基因之间的正确映射。因此，在这项研究中，我们开发了一种新的细胞身份表示形式，可以在不同的数据集上进行比较，同时保留重要的生物语义。为了概括细胞类型的概念，我们开发了一个新的框架来根据集合论管理细胞身份。我们通过安装细胞生物学的数学描述来提供对细胞的进一步见解。我们还进行了与 RNA-seq 数据注释中的实际应用相对应的实验。基于统计依赖性的 GRN 被设计来表示细胞功能通过数学描述概括了细胞身份的定义应用了比较细胞的新框架来注释 scRNA-seq 数据中枢神经系统样本中的注释性能与基于 DEG 的方法进行了比较 Okano 等人。表明细胞类型的普遍特征可以通过基因的统计依赖性来表征，可以在不同的数据集之间进行比较。作为理论考虑的结果，他们还展示了一种可解释的实践，通过基因调控网络的结构来注释 scRNA-seq 数据中的细胞簇。
The emergence of single-cell RNA sequencing (RNA-seq) has radically changed the observation of cellular diversity. Although annotations of RNA-seq data require preserved properties among cells of an identity, annotations using conventional methods have not been able to capture universal characters of a cell type. Analysis of expression levels cannot be accurately annotated for cells because differences in transcription do not necessarily explain biological characteristics in terms of cellular functions and because the data themselves do not inform about the correct mapping between cell types and genes. Hence, in this study, we developed a new representation of cellular identities that can be compared over different datasets while preserving nontrivial biological semantics. To generalize the notion of cell types, we developed a new framework to manage cellular identities in terms of set theory. We provided further insights into cells by installing mathematical descriptions of cell biology. We also performed experiments that could correspond to practical applications in annotations of RNA-seq data. GRNs based on statistical dependency were designed to represent cell functions The definition of cellular identity was generalized by mathematical descriptions A new framework to compare cells was applied to annotate scRNA-seq data Annotation performance in CNS samples was compared with the DEG-based method Okano et al. show that universal features of cell types can be characterized by statistical dependencies of genes, which can be compared among different datasets. As a result of theoretical considerations, they also show an interpretable practice to annotate clusters of cells in scRNA-seq data by the structures of the gene regulatory networks.
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