TSCAN: Pseudo-time reconstruction and evaluation in single-cell RNA-seq analysis

Zhicheng Ji, Hong Kai Ji

Research output: Contribution to journalArticle

Abstract

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.

Original languageEnglish (US)
Pages (from-to)e117
JournalNucleic Acids Research
Volume44
Issue number13
DOIs
StatePublished - Jul 27 2016

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Single-Cell Analysis
RNA
Gene Expression
Transcriptome
Computer Simulation
Cluster Analysis
Software

ASJC Scopus subject areas

  • Genetics

Cite this

TSCAN : Pseudo-time reconstruction and evaluation in single-cell RNA-seq analysis. / Ji, Zhicheng; Ji, Hong Kai.

In: Nucleic Acids Research, Vol. 44, No. 13, 27.07.2016, p. e117.

Research output: Contribution to journalArticle

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