compcodeR is now available from Bioconductor (from version 2.14). For the latest version of the package, please go via Bioconductor.
compcodeR is an R package for comparing the results of multiple differential expression analysis methods applied to a common RNAseq data set. It also contains functionalities for simulating realistic count matrices and interfaces to several of the most widely used differential expression analysis methods for RNAseq data.
The latest version of compcodeR is available from Bioconductor (from version 2.14).
For more information about how to use compcodeR, consult the manual or the vignette.
A collection of synthetic and real benchmarking data is available here. A number of differential expression methods have been applied to these data sets, and the results are available here.
| Version | Date | Link |
|---|---|---|
| 0.99.3 | 10/4 2014 | compcodeR_0.99.3.tar.gz |
| 0.99.1 | 2/4 2014 | compcodeR_0.99.1.tar.gz |
| 0.2.0 | 4/3 2014 | compcodeR_0.2.0.tar.gz |
| 0.1.0 | 19/11 2013 | compcodeR_0.1.0.tar.gz |
For general instructions regarding installation of R packages, see the help page of the install.packages() function in R.
To install compcodeR successfully, you need to have a recent version of R (>= 3.0.2). Moreover, compcodeR depends on several packages that must be installed on your system. As of version 0.99.3, these packages are the following:
Available from CRAN: sm, knitr (>=1.2), markdown, ROCR, lattice (>=0.16), gplots, gtools, gdata, caTools, KernSmooth, MASS, ggplot2, stringr, modeest, vioplot
Available from Bioconductor: edgeR, limma
Moreover, to perform the differential expression analysis you need to have the corresponding package installed on your system. Most of these packages are available through Bioconductor.
To use the GUI, you need to install rpanel (available through CRAN).