title: Robust estimators for expression analysis.
abstract: BACKGROUND
  We consider the problem of estimating values associated with gene expression from oligonucleotide arrays. Such estimates should linearly track concentration, yield non-negative results, have statistical guarantees of robustness against outliers, and allow estimates of significance and variance.
  
  
  RESULTS
  A hierarchy of simple models is used to design robust estimators meeting these goals for both stand alone and comparative experiments. This algorithm has been validated against an extensive panel of known spike experiments, and shows comparable performance to existing standards.
authors.affiliation: Affymetrix, 3380 Central Expressway, Santa Clara, CA 95051, USA. earl_hubbell@affymetrix.com
authors.forename: Earl
authors.initials: E
authors.lastname: Hubbell
authors.forename: Wei-Min
authors.initials: WM
authors.lastname: Liu
authors.forename: Rui
authors.initials: R
authors.lastname: Mei
mesh_terms: D000465:Algorithms; D015342:DNA Probes; D015870:Gene Expression / Q000235:genetics*; D020869:Gene Expression Profiling / Q000379:methods*; D005786:Gene Expression Regulation / Q000235:genetics; D006801:Humans; D008957:Models, Genetic; D015233:Models, Statistical; D020411:Oligonucleotide Array Sequence Analysis / Q000379:methods*; D011786:Quality Control; D012333:RNA, Messenger / Q000235:genetics; D015203:Reproducibility of Results; D012680:Sensitivity and Specificity; D013269:Stochastic Processes; D015003:Yeasts / Q000235:genetics
chemical_list: D015342:DNA Probes; D012333:RNA, Messenger