The 'miss rate' for the analysis of gene expression data.
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.
Taylor
Jonathan
J
Department of Statistics, Stanford University, Stanford, CA 94305, USA. jonathan.taylor@stanford.edu
Tibshirani
Robert
R
Efron
Bradley
B
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