title: The optimal discovery procedure for large-scale significance testing, with applications to comparative microarray experiments.
abstract: As much of the focus of genetics and molecular biology has shifted toward the systems level, it has become increasingly important to accurately extract biologically relevant signal from thousands of related measurements. The common property among these high-dimensional biological studies is that the measured features have a rich and largely unknown underlying structure. One example of much recent interest is identifying differentially expressed genes in comparative microarray experiments. We propose a new approach aimed at optimally performing many hypothesis tests in a high-dimensional study. This approach estimates the optimal discovery procedure (ODP), which has recently been introduced and theoretically shown to optimally perform multiple significance tests. Whereas existing procedures essentially use data from only one feature at a time, the ODP approach uses the relevant information from the entire data set when testing each feature. In particular, we propose a generally applicable estimate of the ODP for identifying differentially expressed genes in microarray experiments. This microarray method consistently shows favorable performance over five highly used existing methods. For example, in testing for differential expression between two breast cancer tumor types, the ODP provides increases from 72% to 185% in the number of genes called significant at a false discovery rate of 3%. Our proposed microarray method is freely available to academic users in the open-source, point-and-click EDGE software package.
authors.affiliation: Department of Biostatistics, University of Washington, Seattle, WA 98195, USA. jstorey@u.washington.edu
authors.forename: John D
authors.initials: JD
authors.lastname: Storey
authors.forename: James Y
authors.initials: JY
authors.lastname: Dai
authors.forename: Jeffrey T
authors.initials: JT
authors.lastname: Leek
mesh_terms: D051017:Apoptosis Regulatory Proteins; D019313:BRCA1 Protein / Q000235:genetics; D024682:BRCA2 Protein / Q000235:genetics; D001943:Breast Neoplasms / Q000235:genetics; D003627:Data Interpretation, Statistical; D005189:False Positive Reactions; D005260:Female; D020869:Gene Expression Profiling / Q000379:methods*; D006801:Humans; D020411:Oligonucleotide Array Sequence Analysis / Q000379:methods*; D049490:Systems Biology / Q000379:methods*
chemical_list: D051017:Apoptosis Regulatory Proteins; C118024:BLID protein, human; D019313:BRCA1 Protein; C492913:BRCA1 protein, human; D024682:BRCA2 Protein; C551750:BRCA2 protein, human