Ji Zhicheng, Ji Hongkai
Department of Biostatistics, Johns Hopkins Bloomberg School of Public Health, Baltimore, MD, USA.
Methods Mol Biol. 2019;1935:115-124. doi: 10.1007/978-1-4939-9057-3_8.
In many single-cell RNA-seq (scRNA-seq) experiments, cells represent progressively changing states along a continuous biological process. A useful approach to analyzing data from such experiments is to computationally order cells based on their gradual transition of gene expression. The ordered cells can be viewed as samples drawn from a pseudo-temporal trajectory. Analyzing gene expression dynamics along the pseudotime provides a valuable tool for reconstructing the underlying biological process and generating biological insights. TSCAN is an R package to support in silico reconstruction of cells' pseudotime. This chapter introduces how to apply TSCAN to scRNA-seq data to perform pseudotime analysis.
在许多单细胞RNA测序(scRNA-seq)实验中,细胞代表了沿着连续生物学过程逐渐变化的状态。分析此类实验数据的一种有用方法是根据细胞基因表达的逐渐转变对其进行计算排序。排序后的细胞可视为从伪时间轨迹中抽取的样本。分析沿伪时间的基因表达动态为重建潜在生物学过程和产生生物学见解提供了一个有价值的工具。TSCAN是一个R包,用于支持细胞伪时间的计算机重建。本章介绍如何将TSCAN应用于scRNA-seq数据以进行伪时间分析。