Campus Units
Statistics
Document Type
Article
Publication Version
Published Version
Publication Date
2004
Journal or Book Title
Genome Research
Volume
14
First Page
997
Last Page
1001
DOI
10.1101/gr.2156804
Abstract
Genomic methods have made statistical multiple-test methods important to geneticists and molecular biologists. These tests apply to identification of quantitative trait loci and measurement of changes in RNA or DNA abundance by microarray methods. Recently developed multiple-test methods provide more statistical power when many of the tested null hypotheses are false. At the same time, these methods can provide stringent control of errors in cases when most or all of the tested null hypotheses are true. These methods control errors in a different way from previous hypothesis tests, controlling or estimating quantities called the posterior error rate (PER), false discovery rate (FDR), or proportion of false positives (PFP), rather than the type I error. In this study, we attempt to clarify the relationships among these methods and demonstrate how the proportion of true null hypotheses among all tested hypotheses plays an important role.
Creative Commons License
This work is licensed under a Creative Commons Attribution-NonCommercial 4.0 International License
Copyright Owner
Cold Spring Harbor Laboratory Press
Copyright Date
2004
Language
en
File Format
application/pdf
Recommended Citation
Manly, Kenneth F.; Nettleton, Dan; and Hwang, J. T. Gene, "Genomics, Prior Probability, and Statistical Tests of Multiple Hypotheses" (2004). Statistics Publications. 227.
https://lib.dr.iastate.edu/stat_las_pubs/227
Included in
Cell and Developmental Biology Commons, Molecular Genetics Commons, Statistical Methodology Commons, Statistical Theory Commons
Comments
This article is published as Manly, Kenneth F., Dan Nettleton, and JT Gene Hwang. "Genomics, prior probability, and statistical tests of multiple hypotheses." Genome research 14, no. 6 (2004): 997-1001. doi: 10.1101/gr.2156804. Posted with permission.