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


Ontology type: schema:ScholarlyArticle      Open Access: True


Article Info

DATE

2017-12

AUTHORS

Zhuo Wang, Shuilin Jin, Guiyou Liu, Xiurui Zhang, Nan Wang, Deliang Wu, Yang Hu, Chiping Zhang, Qinghua Jiang, Li Xu, Yadong Wang

ABSTRACT

BACKGROUND: 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. RESULTS: 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. CONCLUSIONS: 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 . More... »

PAGES

270

Identifiers

URI

http://scigraph.springernature.com/pub.10.1186/s12859-017-1647-3

DOI

http://dx.doi.org/10.1186/s12859-017-1647-3

DIMENSIONS

https://app.dimensions.ai/details/publication/pub.1085558901

PUBMED

https://www.ncbi.nlm.nih.gov/pubmed/28535748


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Download the RDF metadata as:  json-ld nt turtle xml License info

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Turtle is a human-readable linked data format.

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RDF/XML is a standard XML format for linked data.

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