Latest ReleaseVersion 1.99.1
To install the downloaded package, use:
R CMD INSTALL scde_X.Y.Z.tar.gz
Available on Github
To install the development version from Github, use:
require(devtools) devtools::install_github('hms-dbmi/scde', build_vignettes = FALSE)
TroubleshootingInstallation on Mac OS X may require this additional gfortran library:
See the help page for additional support.
curl -O http://r.research.att.com/libs/gfortran-4.8.2-darwin13.tar.bz2 sudo tar fvxz gfortran-4.8.2-darwin13.tar.bz2 -C /
The old release of
scde contains functionalities for error modeling and differential expression analysis detailed in "Bayesian approach to single-cell differential expression analysis" (Kharchenko PV, Silberstein L, Scadden DT, Nature Methods, doi:10.1038/nmeth.2967).
- fixed error in posterior mode calculations in scde.posteriors()
- cleaned up package dependencies, removed deprecated multicore dependency
- fixed a bug in scde.browse.diffexp() where it used a global variable
- optimized memory usage during error model construction
- added support for batch bias correction. See scde.expression.difference() and scde.test.gene.expression.difference() help for details
- added a local web browser interface to view differential expression results. See scde.browse.diffexp() help for details.
- added ability to fit models to a reference transcriptome (usually a bulk measurement). See scde.fit.models.to.reference() method for details
- The initial release implements simplified methods for the basic workflow and significantly speeds up differential expression testing.
- This release implements all the basic functionality that is described in the original publication. It uses explicit handling of posterior distributions to test differential expression.