title: The 'miss rate' for the analysis of gene expression data.
abstract: Multiple testing issues are important in gene expression studies, where typically thousands of genes are compared over two or more experimental conditions. The false discovery rate has become a popular measure in this setting. Here we discuss a complementary measure, the 'miss rate', and show how to estimate it in practice.
authors.affiliation: Department of Statistics, Stanford University, Stanford, CA 94305, USA. jonathan.taylor@stanford.edu
authors.forename: Jonathan
authors.initials: J
authors.lastname: Taylor
authors.forename: Robert
authors.initials: R
authors.lastname: Tibshirani
authors.forename: Bradley
authors.initials: B
authors.lastname: Efron
mesh_terms: D003198:Computer Simulation; D003627:Data Interpretation, Statistical; D005188:False Negative Reactions; D005189:False Positive Reactions; D015973:Gene Expression Regulation, Leukemic / Q000235:genetics; D006801:Humans; D015470:Leukemia, Myeloid, Acute / Q000235:genetics; D020411:Oligonucleotide Array Sequence Analysis / Q000379:methods*; D054198:Precursor Cell Lymphoblastic Leukemia-Lymphoma / Q000235:genetics