title: Statistically designing microarrays and microarray experiments to enhance sensitivity and specificity.
abstract: Gene expression signatures from microarray experiments promise to provide important prognostic tools for predicting disease outcome or response to treatment. A number of microarray studies in various cancers have reported such gene signatures. However, the overlap of gene signatures in the same disease has been limited so far, and some reported signatures have not been reproduced in other populations. Clearly, the methods used for verifying novel gene signatures need improvement. In this article, we describe an experiment in which microarrays and sample hybridization are designed according to the statistical principles of randomization, replication and blocking. Our results show that such designs provide unbiased estimation of differential expression levels as well as powerful tests for them.
authors.affiliation: Department of Statistics, The Ohio State University, 1958 Neil Avenue Columbus, Ohio 43210, USA. Hsu.1@osu.edu
authors.forename: Jason C
authors.initials: JC
authors.lastname: Hsu
authors.forename: Jane
authors.initials: J
authors.lastname: Chang
authors.forename: Tao
authors.initials: T
authors.lastname: Wang
authors.forename: Eiríkur
authors.initials: E
authors.lastname: Steingrímsson
authors.forename: Magnús Karl
authors.initials: MK
authors.lastname: Magnússon
authors.forename: Kristin
authors.initials: K
authors.lastname: Bergsteinsdottir
mesh_terms: D001943:Breast Neoplasms / Q000235:genetics; D005260:Female; D020869:Gene Expression Profiling / Q000379:methods* / Q000706:statistics & numerical data*; D006801:Humans; D008875:Middle Aged; D015233:Models, Statistical; D020411:Oligonucleotide Array Sequence Analysis / Q000379:methods* / Q000592:standards; D011897:Random Allocation; D015203:Reproducibility of Results; D012107:Research Design / Q000592:standards; D012680:Sensitivity and Specificity