statistical methods for gene set coexpression analysis.

statistical methods for gene set coexpression analysis. -...

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BIOINFORMATICS ORIGINAL PAPER Vol. 25 no. 21 2009, pages 2780–2786 doi:10.1093/bioinformatics/btp502 Gene expression Statistical methods for gene set co-expression analysis YounJeong Choi 1 and Christina Kendziorski 2 , 1 Department of Statistics and 2 Department of Biostatistics and Medical Informatics, University of Wisconsin - Madison 1300 University Avenue, Madison, WI 53706, USA Received on April 2, 2009; revised on July 20, 2009; accepted on August 4, 2009 Advance Access publication August 18, 2009 Associate Editor: Martin Bishop ABSTRACT 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 www.biostat.wisc.edu/~kendzior/GSCA/. Contact: [email protected] Supplementary information: Supplementary data are available at Bioinformatics online. 1 INTRODUCTION Amain goal of microarray experiments is to identify individual genes or gene sets differentially regulated across biological conditions. Most often, differential regulation is taken to mean differential expression; and a number of statistical methods for identifying differentially expressed (DE) genes or gene sets are now available (for reviews, see Allison et al ., 2006; Barry et al ., 2008; Ho et al ., 2007; Newton et al ., 2007). Although useful in thousands of studies, these methods are not able to identify many important classes of differentially regulated genes. One example concerns differentially co-expressed (DC) genes. Two genes are DC if their correlation in one biological condition differs from that in another; and statistical methods for identifying DC gene pairs are available (Lai et al ., 2004; Shedden and Taylor, 2005). Generally speaking, a DC gene group is defined similarly, as one in which the correlation structure among the group’s genes in one condition differs from that in another. However, the exact To whom correspondence should be addressed. way in which one defines the gene group, specifies the correlation structure, and quantifies differences varies from study to study.
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statistical methods for gene set coexpression analysis. -...

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