Abstract
Summary: Cell differentiation processes are achieved through a continuum of hierarchical intermediate cell states that might be captured by single-cell RNA seq. Existing computational approaches for the assessment of cell-state hierarchies from single-cell data can be formalized under a general framework composed of (i) a metric to assess cell-to-cell similarities (with or without a dimensionality reduction step) and (ii) a graph-building algorithm (optionally making use of a cell clustering step). The Sincell R package implements a methodological toolbox allowing flexible workflows under such a framework. Furthermore, Sincell contributes new algorithms to provide cell-state hierarchies with statistical support while accounting for stochastic factors in single-cell RNA seq. Graphical representations and functional association tests are provided to interpret hierarchies. The functionalities of Sincell are illustrated in a real case study, which demonstrates its ability to discriminate noisy from stable cell-state hierarchies.Availability and implementation: Sincell is an open-source R/Bioconductor package available at http://bioconductor.org/packages/sincell. A detailed manual and a vignette are provided with the package.Contact: antonio.rausell@isb-sib.chSupplementary information: Supplementary data are available at Bioinformatics online.
Highlights
Unbiased profiling of individual cells through single-cell RNA-seq allows assessing heterogeneity of transcriptional states within a cell population (Wu et al, 2014)
In the context of a cell population’s differentiation or activation process, such transcriptional heterogeneity might reflect a continuum of intermediate cell states and lineages resulting from dynamic regulatory programs
Sincell implements algorithms to provide statistical support to the cell-state hierarchies derived from single-cell RNA-seq
Summary
Unbiased profiling of individual cells through single-cell RNA-seq allows assessing heterogeneity of transcriptional states within a cell population (Wu et al, 2014). A number of algorithms have been used to assess cell-state hierarchies from single-cell data (Amir et al, 2013; Bendall et al, 2014; Buettner et al, 2015; Jaitin et al, 2014; Moignard et al, 2015; Qiu et al, 2011; Trapnell et al, 2014). These approaches can be formalized under a general framework (Supplementary Table S1). Sincell implements algorithms to provide statistical support to the cell-state hierarchies derived from single-cell RNA-seq. To help interpret hierarchies in functional terms, Sincell provides graphical representations of cell-to-cell similarities in low-dimensional space as well as different graph displays of hierarchies, coloring cells, e.g. by expression levels of a marker of choice. Gene set collections (e.g. Gene Ontology terms) can be systematically evaluated
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