To install this package, start R and enter:
## try http:// if https:// URLs are not supported
source("https://bioconductor.org/biocLite.R")
biocLite("scde")
In most cases, you don't need to download the package archive at all.
scde
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Single Cell Differential Expression
Bioconductor version: Release (3.5)
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 fas.harvard.edu>
Citation (from within R,
enter citation("scde")):
Installation
To install this package, start R and enter:
## try http:// if https:// URLs are not supported
source("https://bioconductor.org/biocLite.R")
biocLite("scde")
Documentation
| Reference Manual |
Details
| biocViews | Bayesian, DifferentialExpression, RNASeq, Software, StatisticalMethod, Transcription |
| Version | 2.4.1 |
| In Bioconductor since | BioC 3.3 (R-3.3) (1.5 years) |
| 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, org.Hs.eg.db, rmarkdown |
| SystemRequirements | |
| Enhances | |
| URL | http://pklab.med.harvard.edu/scde |
| BugReports | https://github.com/hms-dbmi/scde/issues |
| Depends On Me | |
| Imports Me | |
| Suggests Me | |
| Build Report |
Package Archives
Follow Installation instructions to use this package in your R session.
| Source Package | scde_2.4.1.tar.gz |
| Windows Binary | scde_2.4.1.zip (32- & 64-bit) |
| Mac OS X 10.11 (El Capitan) | scde_2.4.1.tgz |
| Source Repository | git clone https://git.bioconductor.org/packages/scde |
| Package Short Url | http://bioconductor.org/packages/scde/ |
| Package Downloads Report | Download Stats |
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