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R package for the statistical assessment of cell state hierarchies from single-cell RNA-seq data

Bioconductor version: Release (3.5)

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 might be formalized under a general workflow composed of i) a metric to assess cell-to-cell similarities (combined or not with a dimensionality reduction step), and ii) a graph-building algorithm (optionally making use of a cells-clustering step). Sincell R package implements a methodological toolbox allowing flexible workflows under such 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.

Author: Miguel Julia <migueljuliamolina at>, Amalio Telenti <atelenti at>, Antonio Rausell <antonio.rausell at>

Maintainer: Miguel Julia <migueljuliamolina at>, Antonio Rausell<antonio.rausell at>

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PDF R Script Sincell: Analysis of cell state hierarchies from single-cell RNA-seq
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biocViews BiomedicalInformatics, CellBiology, Clustering, FunctionalGenomics, GeneExpression, GeneSetEnrichment, GraphAndNetwork, RNASeq, Sequencing, Software, SystemsBiology, Visualization
Version 1.8.0
In Bioconductor since BioC 3.1 (R-3.2) (2.5 years)
License GPL (>= 2)
Depends R (>= 3.0.2), igraph
Imports Rcpp (>= 0.11.2), entropy, scatterplot3d, MASS, TSP, ggplot2, reshape2, fields, proxy, parallel, Rtsne, fastICA, cluster, statmod
LinkingTo Rcpp
Suggests BiocStyle, knitr, biomaRt, stringr, monocle
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