title: Identifying differentially expressed genes from microarray experiments via statistic synthesis.
abstract: BACKGROUND
  A common objective of microarray experiments is the detection of differential gene expression between samples obtained under different conditions. The task of identifying differentially expressed genes consists of two aspects: ranking and selection. Numerous statistics have been proposed to rank genes in order of evidence for differential expression. However, no one statistic is universally optimal and there is seldom any basis or guidance that can direct toward a particular statistic of choice.
  
  
  RESULTS
  Our new approach, which addresses both ranking and selection of differentially expressed genes, integrates differing statistics via a distance synthesis scheme. Using a set of (Affymetrix) spike-in datasets, in which differentially expressed genes are known, we demonstrate that our method compares favorably with the best individual statistics, while achieving robustness properties lacked by the individual statistics. We further evaluate performance on one other microarray study.
authors.affiliation: Departments of Medicine, Center for Bioinformatics and Molecular Biostatistics, University of California San Francisco, CA 94143, USA.
authors.forename: Yee Hwa
authors.initials: YH
authors.lastname: Yang
authors.forename: Yuanyuan
authors.initials: Y
authors.lastname: Xiao
authors.forename: Mark R
authors.initials: MR
authors.lastname: Segal
mesh_terms: D000465:Algorithms; D003198:Computer Simulation; D003627:Data Interpretation, Statistical; D020869:Gene Expression Profiling / Q000379:methods*; D008957:Models, Genetic; D015233:Models, Statistical; D020411:Oligonucleotide Array Sequence Analysis / Q000379:methods*; D017422:Sequence Analysis, DNA / Q000379:methods*; D012984:Software