DTWscore: differential expression and cell clustering analysis for time-series single-cell RNA-seq data

BMC Bioinformatics

Methodology Article

Li Xu¹  Nan Wang²  Zhuo Wang²  Shuilin Jin²  Deliang Wu²  Chiping Zhang²  Xiurui Zhang²  Guiyou Liu³  Yang Hu³  Qinghua Jiang³  Yadong Wang⁴ 

[1] College of Computer Science and Technology, Harbin Engineering University, Harbin, Nantong Street, 150001, Heilongjiang, China;Department of Mathematics, Harbin Institute of Technology, Harbin, 150001, Heilongjiang, West Dazhi Street, China;School of Computer Science and Technology, Harbin Institute of Technology, Harbin, 150001, Heilongjiang, West Dazhi Street, China;School of Computer Science and Technology, Harbin Institute of Technology, Harbin, Nantong Street, 150001, Heilongjiang, China;

关键词: Single-cell RNA-seq;    Time-series data;    Dynamic time warping;   

DOI  :  10.1186/s12859-017-1647-3

 received in 2016-09-16, accepted in 2017-04-25,  发布年份 2017

来源: Springer

【 摘 要 】

BackgroundThe development of single-cell RNA sequencing has enabled profound discoveries in biology, ranging from the dissection of the composition of complex tissues to the identification of novel cell types and dynamics in some specialized cellular environments. However, the large-scale generation of single-cell RNA-seq (scRNA-seq) data collected at multiple time points remains a challenge to effective measurement gene expression patterns in transcriptome analysis.ResultsWe present an algorithm based on the Dynamic Time Warping score (DTWscore) combined with time-series data, that enables the detection of gene expression changes across scRNA-seq samples and recovery of potential cell types from complex mixtures of multiple cell types.ConclusionsThe DTWscore successfully classify cells of different types with the most highly variable genes from time-series scRNA-seq data. The study was confined to methods that are implemented and available within the R framework. Sample datasets and R packages are available at https://github.com/xiaoxiaoxier/DTWscore.

【 授权许可】

CC BY   
© The Author(s) 2017

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