Shrunken p-values for assessing differential expression with applications to genomic data analysis.
In many scientific problems involving high-throughput technology, inference must be made involving several hundreds or thousands of hypotheses. Recent attention has focused on how to address the multiple testing issue; much focus has been devoted toward the use of the false discovery rate. In this article, we consider an alternative estimation procedure titled shrunken p-values for assessing differential expression (SPADE). The estimators are motivated by risk considerations from decision theory and lead to a completely new method for adjustment in the multiple testing problem. In addition, the decision-theoretic framework can be used to derive a decision rule for controlling the number of false positive results. Some theoretical results are outlined. The proposed methodology is illustrated using simulation studies and with application to data from a prostate cancer gene expression profiling study.
Ghosh
Debashis
D
Department of Biostatistics, University of Michigan, 1420 Washington Heights, Ann Arbor, Michigan 48109-2029, USA. ghoshd@umich.edu
D001699:Biometry; D003627:Data Interpretation, Statistical; D003662:Decision Theory; D020869:Gene Expression Profiling / Q000706:statistics & numerical data*; D023281:Genomics / Q000706:statistics & numerical data*; D006801:Humans; D008297:Male; D015233:Models, Statistical; D020411:Oligonucleotide Array Sequence Analysis / Q000706:statistics & numerical data; D011471:Prostatic Neoplasms / Q000235:genetics