CoXpress: differential co-expression in gene expression data.
BACKGROUND
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. Here we describe coXpress, an R package that allows researchers to identify groups of genes that are differentially co-expressed.


RESULTS
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.


CONCLUSIONS
coXpress can be used to find groups of genes that display differential co-expression patterns in microarray datasets.
Watson
Michael
M
Informatics Group, Institute for Animal Health, Compton, Newbury, Berks RG20 7NN, UK. michael.watson@bbsrc.ac.uk
D000465:Algorithms; D016000:Cluster Analysis; D019295:Computational Biology / Q000379:methods*; D020869:Gene Expression Profiling; D005786:Gene Expression Regulation; D006801:Humans; D015470:Leukemia, Myeloid, Acute / Q000235:genetics; D015233:Models, Statistical; D005810:Multigene Family; D020411:Oligonucleotide Array Sequence Analysis; D010363:Pattern Recognition, Automated; D054198:Precursor Cell Lymphoblastic Leukemia-Lymphoma / Q000378:metabolism; D017422:Sequence Analysis, DNA; D012984:Software