title: Comparison of global tests for functional gene sets in two-group designs and selection of potentially effect-causing genes.
abstract: BACKGROUND
  An important object in the analysis of high-throughput genomic data is to find an association between the expression profile of functional gene sets and the different levels of a group response. Instead of multiple testing procedures which focus on single genes, global tests are usually used to detect a group effect in an entire gene set. In a simulation study, we compare the power and computation times of four different approaches for global testing. The applicability of one of these methods to gene expression data is demonstrated for the first time. In addition, we propose an algorithm for the detection of those genes which might be responsible for a group effect.
  
  
  RESULTS
  We could detect that the power of three of the approaches is comparable in many settings but considerable differences were detected in the computation times. Our proposed gene selection algorithm was able to detect potentially effect-causing genes in artificial sets with high power when many genes were altered with a small effect, while classical multiple testing was more powerful when few genes were altered with a large effect.
  
  
  BACKGROUND
  An R-package called 'RepeatedHighDim' which implements our new global test procedures is made available from http://cran.r-project.org/.
authors: Department of Medical Statistics, University Medical Center Göttingen, Göttingen, Germany.
authors: Klaus
authors: K
authors: Jung
authors: Benjamin
authors: B
authors: Becker
authors: Edgar
authors: E
authors: Brunner
authors: Tim
authors: T
authors: Beissbarth
mesh_terms: D000465:Algorithms; D015179:Colorectal Neoplasms / Q000235:genetics; D020869:Gene Expression Profiling / Q000379:methods*; D055106:Genome-Wide Association Study; D006801:Humans; D020411:Oligonucleotide Array Sequence Analysis / Q000379:methods*