期刊
BIOSTATISTICS
卷 22, 期 1, 页码 68-81出版社
OXFORD UNIV PRESS
DOI: 10.1093/biostatistics/kxz010
关键词
eQTL; FDR; Functional data analysis; Genetics of gene expression; Kernel density estimation; Local false discovery rate; Multiple hypothesis testing; q-value; RNA-seq; Sequencing depth
资金
- National Institutes of Health [R01 HG002913, R01 HG006448]
- Office of Naval Research [N00014-12-1-0764]
The FDR measures the proportion of false discoveries among a set of hypothesis tests, typically estimated based on p-values or test statistics; a new framework is developed for formulating and estimating FDRs and q-values when additional information is available.
The false discovery rate (FDR) measures the proportion of false discoveries among a set of hypothesis tests called significant. This quantity is typically estimated based on p-values or test statistics. In some scenarios, there is additional information available that may be used to more accurately estimate the FDR. We develop a new framework for formulating and estimating FDRs and q-values when an additional piece of information, which we call an informative variable, is available. For a given test, the informative variable provides information about the prior probability a null hypothesis is true or the power of that particular test. The FDR is then treated as a function of this informative variable. We consider two applications in genomics. Our first application is a genetics of gene expression (eQTL) experiment in yeast where every genetic marker and gene expression trait pair are tested for associations. The informative variable in this case is the distance between each genetic marker and gene. Our second application is to detect differentially expressed genes in an RNA-seq study carried out in mice. The informative variable in this study is the per-gene read depth. The framework we develop is quite general, and it should be useful in a broad range of scientific applications.
作者
我是这篇论文的作者
点击您的名字以认领此论文并将其添加到您的个人资料中。
推荐
暂无数据