title: Statistical methods for gene set co-expression analysis.
abstract: BACKGROUND
  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.
  
  
  BACKGROUND
  The GSCA approach is implemented in R and available at www.biostat.wisc.edu/ approximately kendzior/GSCA/.
  
  
  BACKGROUND
  kendzior@biostat.wisc.edu
  
  
  BACKGROUND
  Supplementary data are available at Bioinformatics online.
authors.affiliation: Department of Statistics, University of Wisconsin - Madison, Madison, WI 53706, USA.
authors.forename: YounJeong
authors.initials: Y
authors.lastname: Choi
authors.forename: Christina
authors.initials: C
authors.lastname: Kendziorski
mesh_terms: D019295:Computational Biology / Q000379:methods*; D003924:Diabetes Mellitus, Type 2 / Q000235:genetics; D020869:Gene Expression Profiling / Q000379:methods*; D006801:Humans; D008175:Lung Neoplasms / Q000235:genetics; D015233:Models, Statistical; D020411:Oligonucleotide Array Sequence Analysis
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