title: Data-adaptive test statistics for microarray data.
abstract: BACKGROUND
  An important task in microarray data analysis is the selection of genes that are differentially expressed between different tissue samples, such as healthy and diseased. However, microarray data contain an enormous number of dimensions (genes) and very few samples (arrays), a mismatch which poses fundamental statistical problems for the selection process that have defied easy resolution.
  
  
  RESULTS
  In this paper, we present a novel approach to the selection of differentially expressed genes in which test statistics are learned from data using a simple notion of reproducibility in selection results as the learning criterion. Reproducibility, as we define it, can be computed without any knowledge of the 'ground-truth', but takes advantage of certain properties of microarray data to provide an asymptotically valid guide to expected loss under the true data-generating distribution. We are therefore able to indirectly minimize expected loss, and obtain results substantially more robust than conventional methods. We apply our method to simulated and oligonucleotide array data.
  
  
  BACKGROUND
  By request to the corresponding author.
authors.affiliation: Department of Engineering Science, University of Oxford, UK. sach@robots.ox.ac.uk
authors.forename: Sach
authors.initials: S
authors.lastname: Mukherjee
authors.forename: Stephen J
authors.initials: SJ
authors.lastname: Roberts
authors.forename: Mark J
authors.initials: MJ
authors.lastname: van der Laan
mesh_terms: D000465:Algorithms; D003627:Data Interpretation, Statistical; D030541:Databases, Genetic; D020869:Gene Expression Profiling / Q000379:methods*; D016247:Information Storage and Retrieval / Q000379:methods*; D020411:Oligonucleotide Array Sequence Analysis / Q000379:methods*; D015203:Reproducibility of Results; D012680:Sensitivity and Specificity