title: Selecting differentially expressed genes from microarray experiments.
abstract: High throughput technologies, such as gene expression arrays and protein mass spectrometry, allow one to simultaneously evaluate thousands of potential biomarkers that could distinguish different tissue types. Of particular interest here is distinguishing between cancerous and normal organ tissues. We consider statistical methods to rank genes (or proteins) in regards to differential expression between tissues. Various statistical measures are considered, and we argue that two measures related to the Receiver Operating Characteristic Curve are particularly suitable for this purpose. We also propose that sampling variability in the gene rankings be quantified, and suggest using the "selection probability function," the probability distribution of rankings for each gene. This is estimated via the bootstrap. A real dataset, derived from gene expression arrays of 23 normal and 30 ovarian cancer tissues, is analyzed. Simulation studies are also used to assess the relative performance of different statistical gene ranking measures and our quantification of sampling variability. Our approach leads naturally to a procedure for sample-size calculations, appropriate for exploratory studies that seek to identify differentially expressed genes.
authors.affiliation: Department of Biostatistics, University of Washington, Seattle, Washington 98195-7232, USA. mspepe@u.washington.edu
authors.forename: Margaret Sullivan
authors.initials: MS
authors.lastname: Pepe
authors.forename: Gary
authors.initials: G
authors.lastname: Longton
authors.forename: Garnet L
authors.initials: GL
authors.lastname: Anderson
authors.forename: Michel
authors.initials: M
authors.lastname: Schummer
mesh_terms: D014408:Biomarkers, Tumor / Q000096:biosynthesis / Q000235:genetics; D003198:Computer Simulation; D005260:Female; D020869:Gene Expression Profiling / Q000379:methods*; D015972:Gene Expression Regulation, Neoplastic; D006801:Humans; D009369:Neoplasms / Q000235:genetics* / Q000378:metabolism; D020411:Oligonucleotide Array Sequence Analysis / Q000379:methods*; D010051:Ovarian Neoplasms / Q000378:metabolism; D040901:Proteomics / Q000379:methods; D012372:ROC Curve; D018401:Sample Size; D018709:Statistics, Nonparametric
chemical_list: D014408:Biomarkers, Tumor