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.
Here researchers from the National Institute of Advanced Industrial Science and Technology, Japan 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.
Myoblast MST. Comparison of minimum spanning trees obtained from
the dataset of differentiating myoblasts by cellTree
(a) and Monocle (b). Cell nodes have been coloured according to their sampling time (in hours). c shows the final tree structure obtained directly by Monocle
CellTree provides experimentalists with an easy-to-use tool, based on statistically and biologically-sound algorithms, to efficiently explore and visualise single-cell RNA data.
Availability – The cellTree package is publicly available in the online Bionconductor repository at: http://bioconductor.org/packages/cellTree/