TiC2D: Trajectory Inference From Single-Cell RNA-Seq Data Using Consensus Clustering
TiC2D: Trajectory Inference From Single-Cell RNA-Seq Data Using Consensus Clustering
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TiC2D：使用共识聚类从单细胞 RNA-seq 数据进行轨迹推断
DOI：
10.1109/tcbb.2021.3061720
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发表时间：
2021-02
期刊：
影响因子：
--
通讯作者：
Shuigeng Zhou
中科院分区：
文献类型：
--
作者：
Yanglan Gan;Ning Li;Cheng Guo;Guobing Zou;Jihong Guan;Shuigeng Zhou
细胞程序在程序执行的时间上经常表现出很强的异质性和复杂性。单细胞RNA-seq技术通过以单细胞分辨率同时定量许多参数，为表征这些细胞过程提供了前所未有的机会。鲁棒轨迹推断是分析基因表达动态时序的关键步骤，可以揭示正常发育和疾病的机制。在这里，我们提出了TiC 2D，这是一种用于从单细胞RNA-seq数据推断细胞轨迹的新算法，它采用一致聚类策略来精确聚类细胞。为了评估TiC 2D的功效，我们在四个独立的单细胞RNA-seq数据集上将其与三种最先进的方法进行了比较。结果表明，TiC 2D可以准确地从单细胞转录组推断发育轨迹。此外，重建的轨迹使我们能够确定参与细胞命运决定的关键基因，并获得有关它们在不同发育阶段作用的新见解。
Cellular programs often exhibit strong heterogeneity and asynchrony in the timing of program execution. Single-cell RNA-seq technology has provided an unprecedented opportunity for characterizing these cellular processes by simultaneously quantifying many parameters at single-cell resolution. Robust trajectory inference is a critical step in the analysis of dynamic temporal gene expression, which can shed light on the mechanisms of normal development and diseases. Here, we present TiC2D, a novel algorithm for cell trajectory inference from single-cell RNA-seq data, which adopts a consensus clustering strategy to precisely cluster cells. To evaluate the power of TiC2D, we compare it with three state-of-the-art methods on four independent single-cell RNA-seq datasets. The results show that TiC2D can accurately infer developmental trajectories from single-cell transcriptome. Furthermore, the reconstructed trajectories enable us to identify key genes involved in cell fate determination and to obtain new insights about their roles at different developmental stages.
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