CellTree: an R/bioconductor package to infer the hierarchical structure of cell populations from single-cell RNA-seq data

BMC Bioinformatics

Software

Sohiya Yotsukura¹  Hiroyuki Aburatani²  Seitaro Nomura²  David A. duVerle³  Koji Tsuda⁴ 

[1] Bioinformatics Center, Institute for Chemical Research, Kyoto University, Gokasho, Uji, Japan;Genome Science Division, Laboratory of Systems Biology and Medicine, Research Center for Advanced Science and Technology, The University of Tokyo, 4-6-1 Komaba, Tokyo, Japan;Graduate School of Frontier Sciences at the University of Tokyo, 5-1-5 Kashiwa-no-ha, Kashiwa, Japan;Biotechnology Research Institute for Drug Discovery, National Institute of Advanced Industrial Science and Technology, 2-4-7 Aomi, Koto-ku, Tokyo, Japan;Graduate School of Frontier Sciences at the University of Tokyo, 5-1-5 Kashiwa-no-ha, Kashiwa, Japan;Center for Materials Research by Information Integration, National Institute for Materials Science, 1-2-1 Sengen, Tsukuba, Japan;Biotechnology Research Institute for Drug Discovery, National Institute of Advanced Industrial Science and Technology, 2-4-7 Aomi, Koto-ku, Tokyo, Japan;

关键词: Single-cell RNA-seq;    Cell differentiation;    Cell heterogeneity;    Human stem cell;   

DOI  :  10.1186/s12859-016-1175-6

 received in 2015-12-30, accepted in 2016-08-11,  发布年份 2016

来源: Springer

【 摘 要 】

BackgroundSingle-cell RNA sequencing is fast becoming one the standard method for gene expression measurement, providing unique insights into cellular processes. A number of methods, based on general dimensionality reduction techniques, have been suggested to help infer and visualise the underlying structure of cell populations from single-cell expression levels, yet their models generally lack proper biological grounding and struggle at identifying complex differentiation paths.ResultsHere we introduce cellTree: an R/Bioconductor package that uses a novel statistical approach, based on document analysis techniques, to produce tree structures outlining the hierarchical relationship between single-cell samples, while identifying latent groups of genes that can provide biological insights.ConclusionsWith cellTree, we provide experimentalists with an easy-to-use tool, based on statistically and biologically-sound algorithms, to efficiently explore and visualise single-cell RNA data. The cellTree package is publicly available in the online Bionconductor repository at: http://bioconductor.org/packages/cellTree/.

【 授权许可】

CC BY   
© The Author(s) 2016

【 图 表 】

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