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

Autor: David duVerle, Hiroyuki Aburatani, Sohiya Yotsukura, Seitaro Nomura, Koji Tsuda
Jazyk: angličtina
Rok vydání: 2016
Předmět:
Zdroj: BMC Bioinformatics
ISSN: 1471-2105
Popis: Background Single-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. Results Here 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. Conclusions With 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/. Electronic supplementary material The online version of this article (doi:10.1186/s12859-016-1175-6) contains supplementary material, which is available to authorized users.
Databáze: OpenAIRE