TSCAN: Pseudo-time reconstruction and evaluation in single-cell RNA-seq analysis.
TSCAN: Pseudo-time reconstruction and evaluation in single-cell RNA-seq analysis.
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DOI：
10.1093/nar/gkw430
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发表时间：
2016-07-27
影响因子：
14.9
通讯作者：
Ji H
中科院分区：
文献类型：
--
作者：
Ji Z;Ji H
在分析单细胞RNA-seq数据时，构建一个伪时间路径来根据细胞转录组的逐渐转变来排序细胞，这是研究异质细胞群体中基因表达动态的一种有用方法。目前，有限数量的计算工具可用于这项任务，并缺乏定量的方法来比较不同的工具。单细胞分析工具（TSCAN）是一种软件工具，旨在更好地支持单细胞RNA-seq分析中的计算机模拟伪时间重建。TSCAN使用基于簇的最小生成树（MST）方法来对单元进行排序。首先将细胞分组为簇，然后构建MST以连接簇中心。将每个细胞投影到树上得到伪时间，有序的细胞序列可用于研究基因表达沿着伪时间的动态变化。在MST构建之前对细胞进行聚类，降低了树空间的复杂性。这通常导致改进的细胞排序。它还允许用户根据先验知识方便地调整排序。TSCAN具有图形用户界面（GUI），以支持数据可视化和用户交互。此外，定量的措施，客观地评价和比较不同的伪时间重建方法。TSCAN可在https://github.com/zji90/TSCAN上获得，并作为Bioconductor包提供。
When analyzing single-cell RNA-seq data, constructing a pseudo-temporal path to order cells based on the gradual transition of their transcriptomes is a useful way to study gene expression dynamics in a heterogeneous cell population. Currently, a limited number of computational tools are available for this task, and quantitative methods for comparing different tools are lacking. Tools for Single Cell Analysis (TSCAN) is a software tool developed to better support in silico pseudo-Time reconstruction in Single-Cell RNA-seq ANalysis. TSCAN uses a cluster-based minimum spanning tree (MST) approach to order cells. Cells are first grouped into clusters and an MST is then constructed to connect cluster centers. Pseudo-time is obtained by projecting each cell onto the tree, and the ordered sequence of cells can be used to study dynamic changes of gene expression along the pseudo-time. Clustering cells before MST construction reduces the complexity of the tree space. This often leads to improved cell ordering. It also allows users to conveniently adjust the ordering based on prior knowledge. TSCAN has a graphical user interface (GUI) to support data visualization and user interaction. Furthermore, quantitative measures are developed to objectively evaluate and compare different pseudo-time reconstruction methods. TSCAN is available at https://github.com/zji90/TSCAN and as a Bioconductor package.