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

Zhicheng Ji,Hongkai Ji

doi:10.1093/nar/gkw430

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.

Highlights

Single-cell RNA-seq is a transformative technology that allows researchers to measure transcriptomes of individual cells [1,2]
With the ability to measure the transcriptome of each individual cell, single-cell RNA-seq is capable of generating a higher resolution view of the gene expression landscape in a heterogeneous cell population [9,10,11]
We evaluated Tools for Single Cell Analysis (TSCAN) using the three data sets, human skeletal muscle myoblasts (HSMM), LPS and quiescent neural stem cells (qNSC), described above

Summary

Introduction

Single-cell RNA-seq is a transformative technology that allows researchers to measure transcriptomes of individual cells [1,2]. Unlike single-cell RNA-seq, conventional RNAseq ( referred to as ‘bulk RNA-seq’) [3,4] or microarray [5,6] experiments are used to measure average gene expression of a cell population. With the ability to measure the transcriptome of each individual cell, single-cell RNA-seq is capable of generating a higher resolution view of the gene expression landscape in a heterogeneous cell population [9,10,11]. This can lead to a more accurate molecular characterization of a complex biological phenomenon [12]

Methods

Results

Conclusion