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redPATH: Reconstructing the Pseudo Development Time of Cell Lineages in Single-cell RNA-seq Data and

本站小编 Free考研考试/2022-01-03

The recent advancement of single-cell RNA sequencing (scRNA-seq) technologies facilitates the study of cell lineages in developmental processes and cancer. In this study, we developed a computational method, called redPATH, to reconstruct the pseudo developmental time of cell lineages using a consensus asymmetric Hamiltonian path algorithm. Besides, we developed a novel approach to visualize the trajectory development and implemented visualization methods to provide biological insights. We validated the performance of redPATH by segmenting different stages of cell development on multiple neural stem cell and cancer datasets, as well as other single-cell transcriptome data. In particular, we identified a stem cell-like subpopulation in malignant glioma cells. These cells express known proliferative markers, such as GFAP, ATP1A2, IGFBPL1, and ALDOC, and remain silenced for quiescent markers such as ID3. Furthermore, we identified MCL1 as a significant gene that regulates cell apoptosis and CSF1R for reprogramming macrophages to control tumor growth. In conclusion, redPATH is a comprehensive tool for analyzing scRNA-seq datasets along the pseudo developmental time. redPATH is available at https://github.com/tinglabs/redPATH.
单细胞转录组测序技术(scRNA-seq)的最新进展促进了对发育过程和癌症中细胞谱系的研究。本文开发了一套名为 redPATH 的计算分析方法,首先设计了一个合并非对称哈密顿路径算法(consensus asymmetric Hamiltonian path algorithm)来重构细胞的伪发育时序。此后我们开发了一种新颖的方法来可视化细胞发育的轨迹和其它可视化方法,以提供生物学分析及解释。我们通过在多个神经干细胞和癌性数据集以及其他单细胞转录组数据上分割细胞发育的不同阶段来验证 redPATH 的性能。通过redPATH的分析,我们在恶性胶质瘤细胞中发现了一个干细胞样亚群 (stem-cell-like subpopulation)。这些细胞表达了已知的增殖标记基因,例如 GFAP、ATP1A2、IGFBPL1 和 ALDOC;并在静止标记基因中保持不表达的状态,例如ID3。此外,我们将 MCL1识别为调节细胞死亡的重要基因,也识别了 CSF1R为用于调控巨噬细胞来控制肿瘤生长的重要基因。总的来说,redPATH 是一个用于在单细胞转录组数据上进行伪发育时序分析的综合性工具。该软件可以在https://github.com/tinglabs/redPATH 获得。





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http://gpb.big.ac.cn/articles/download/855
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