Cui Lingyu, Wang Bo, Ren Changjing, Wang Ailan, An Hong, Liang Wei
School of Science, Dalian Maritime University, Dalian, China.
Geneis (Beijing) Co., Ltd., Beijing, China.
Front Genet. 2021 Mar 15;12:648898. doi: 10.3389/fgene.2021.648898. eCollection 2021.
Single-cell sequencing technology can not only view the heterogeneity of cells from a molecular perspective, but also discover new cell types. Although there are many effective methods on dropout imputation, cell clustering, and lineage reconstruction based on single cell RNA sequencing (RNA-seq) data, there is no systemic pipeline on how to compare two single cell clusters at the molecular level. In the study, we present a novel pipeline on comparing two single cell clusters, including calling differential gene expression, coexpression network modules, and so on. The pipeline could reveal mechanisms behind the biological difference between cell clusters and cell types, and identify cell type specific molecular mechanisms. We applied the pipeline to two famous single-cell databases, Usoskin from mouse brain and Xin from human pancreas, which contained 622 and 1,600 cells, respectively, both of which were composed of four types of cells. As a result, we identified many significant differential genes, differential gene coexpression and network modules among the cell clusters, which confirmed that different cell clusters might perform different functions.
单细胞测序技术不仅可以从分子角度观察细胞的异质性，还能发现新的细胞类型。尽管基于单细胞RNA测序（RNA-seq）数据在缺失值插补、细胞聚类和谱系重建方面有许多有效的方法，但在如何在分子水平上比较两个单细胞簇方面却没有系统的流程。在本研究中，我们提出了一种用于比较两个单细胞簇的新流程，包括调用差异基因表达、共表达网络模块等。该流程可以揭示细胞簇和细胞类型之间生物学差异背后的机制，并识别细胞类型特异性分子机制。我们将该流程应用于两个著名的单细胞数据库，来自小鼠大脑的Usoskin和来自人类胰腺的Xin，它们分别包含622个和1600个细胞，两者均由四种细胞类型组成。结果，我们在细胞簇之间鉴定出许多显著的差异基因、差异基因共表达和网络模块，这证实了不同的细胞簇可能执行不同的功能。
