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

BMC Bioinformatics
Zhuo WangYadong Wang

Abstract

The 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. We 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. The 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 .

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Citations

Jun 28, 2019·Briefings in Bioinformatics·Raphael PetegrossoRui Kuang
Nov 7, 2017·Briefings in Functional Genomics·Mattias Rantalainen
Jun 9, 2018·Cancer Informatics·Chao SimaEdward R Dougherty

Related Concepts

In Silico
RNA
Receiver Operating Characteristic
Statistical Study
Disease Clustering
Sequence Determinations, RNA
Soleus Muscle Structure
MRNA Differential Display
Myoblasts
Single-Cell Analysis

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