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Bioinformatics. 2009 Nov 1;25(21):2780-6. doi: 10.1093/bioinformatics/btp502. Epub 2009 Aug 18.

Statistical methods for gene set co-expression analysis.

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  • 1Department of Statistics, University of Wisconsin - Madison, Madison, WI 53706, USA.



The power of a microarray experiment derives from the identification of genes differentially regulated across biological conditions. To date, differential regulation is most often taken to mean differential expression, and a number of useful methods for identifying differentially expressed (DE) genes or gene sets are available. However, such methods are not able to identify many relevant classes of differentially regulated genes. One important example concerns differentially co-expressed (DC) genes.


We propose an approach, gene set co-expression analysis (GSCA), to identify DC gene sets. The GSCA approach provides a false discovery rate controlled list of interesting gene sets, does not require that genes be highly correlated in at least one biological condition and is readily applied to data from individual or multiple experiments, as we demonstrate using data from studies of lung cancer and diabetes.


The GSCA approach is implemented in R and available at approximately kendzior/GSCA/.



Supplementary data are available at Bioinformatics online.

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