Traditional methods of analysing gene expression data often include a statistical test to find differentially expressed genes, or use of a clustering algorithm to find groups of genes that behave similarly across a dataset. However, these methods may miss groups of genes which form differential co-expression patterns under different subsets of experimental conditions. We have developed coXpress as a means of identifying groups of genes that are differentially co-expressed. The utility of coXpress is demonstrated using two publicly available microarray datasets. Our software identifies several groups of genes that are highly correlated under one set of biologically related experiments, but which show little or no correlation in a second set of experiments. The software uses a re-sampling method to calculate a p-value for each group, and provides several methods for the visualisation of differentially co-expressed genes.
CoXpress is available as an R package for both Windows and Linux, and is released under the GNU General Public License. The latest release is available here (please read the README.txt).
CoXpress is an R package, so you will need to have the R statistical software installed on your computer. This is also available free here.
Linux users should download the latest .tar.gz file and, after installing R, type:
R CMD INSTALL coXpress_%.%.tar.gz
(where %.% is the version of the file you have downloaded.
Windows users should download the latest .zip file, and install using the menu bar:
Packages -> Install package(s) from local zip files...
A tutorial can be found here.
Michael Watson Informatics Group Institute for Animal Health Compton Laboratory Compton Newbury RG20 7NN firstname.lastname@example.orgExample Code CoXpress has been submitted to BMC Bioinformatics on 21/7/2006. The example code to recreate the analysis in that paper can be found here.