Journal Article

Statistical methods for gene set co-expression analysis

YounJeong Choi and Christina Kendziorski

in Bioinformatics

Volume 25, issue 21, pages 2780-2786
Published in print November 2009 | ISSN: 1367-4803
Published online August 2009 | e-ISSN: 1460-2059 | DOI:

More Like This

Show all results sharing this subject:

  • Bioinformatics and Computational Biology


Show Summary Details


Motivation: 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.

Results: 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.

Availability: The GSCA approach is implemented in R and available at∼kendzior/GSCA/.


Supplementary information: Supplementary data are available at Bioinformatics online.

Journal Article.  6189 words.  Illustrated.

Subjects: Bioinformatics and Computational Biology

Users without a subscription are not able to see the full content. Please, subscribe or login to access all content.