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This package is for version 3.4 of Bioconductor; for the stable, up-to-date release version, see scde.

Single Cell Differential Expression

Bioconductor version: 3.4

The scde package implements a set of statistical methods for analyzing single-cell RNA-seq data. scde fits individual error models for single-cell RNA-seq measurements. These models can then be used for assessment of differential expression between groups of cells, as well as other types of analysis. The scde package also contains the pagoda framework which applies pathway and gene set overdispersion analysis to identify and characterize putative cell subpopulations based on transcriptional signatures. The overall approach to the differential expression analysis is detailed in the following publication: "Bayesian approach to single-cell differential expression analysis" (Kharchenko PV, Silberstein L, Scadden DT, Nature Methods, doi: 10.1038/nmeth.2967). The overall approach to subpopulation identification and characterization is detailed in the following pre-print: "Characterizing transcriptional heterogeneity through pathway and gene set overdispersion analysis" (Fan J, Salathia N, Liu R, Kaeser G, Yung Y, Herman J, Kaper F, Fan JB, Zhang K, Chun J, and Kharchenko PV, Nature Methods, doi:10.1038/nmeth.3734).

Author: Peter Kharchenko [aut, cre], Jean Fan [aut]

Maintainer: Jean Fan <jeanfan at>

Citation (from within R, enter citation("scde")):


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PDF   Reference Manual


biocViews Bayesian, DifferentialExpression, RNASeq, Software, StatisticalMethod, Transcription
Version 2.2.0
In Bioconductor since BioC 3.3 (R-3.3) (1 year)
License GPL-2
Depends R (>= 3.0.0), flexmix
Imports Rcpp (>= 0.10.4), RcppArmadillo (>= 0.5.400.2.0), mgcv, Rook, rjson, MASS, Cairo, RColorBrewer, edgeR, quantreg, methods, nnet, RMTstat, extRemes, pcaMethods, BiocParallel, parallel
LinkingTo Rcpp, RcppArmadillo
Suggests knitr, cba, fastcluster, WGCNA, GO.db,, rmarkdown
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Imports Me
Suggests Me
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