The functional false discovery rate with applications to genomics

Xiongzhi Chen; David G Robinson; John D Storey

doi:10.1093/biostatistics/kxz010

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The functional false discovery rate with applications to genomics

Journal article

Open access

Peer reviewed

The functional false discovery rate with applications to genomics

Xiongzhi Chen, David G Robinson and John D Storey

Biostatistics (Oxford, England), Vol.22(1), pp.68-81

01/28/2021

DOI: https://doi.org/10.1093/biostatistics/kxz010

Handle:

https://hdl.handle.net/2376/108929

PMID: 31135886

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Abstract

q-value

RNA-seq

FDR

Multiple hypothesis testing

Local false discovery rate

Sequencing depth

SCI01530

Genetics of gene expression

eQTL

AcademicSubjects

Kernel density estimation

Functional data analysis

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.

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Title: The functional false discovery rate with applications to genomics
Creators: Xiongzhi Chen - Lewis-Sigler Institute for Integrative Genomics, Princeton University
David G Robinson - Lewis-Sigler Institute for Integrative Genomics, Princeton University
John D Storey - Lewis-Sigler Institute for Integrative Genomics, Princeton University
Publication Details: Biostatistics (Oxford, England), Vol.22(1), pp.68-81
Academic Unit: Mathematics and Statistics, Department of
Publisher: Oxford University Press
Grant note: R01 HG002913; R01 HG006448 / ; N00014-12-1-0764 / ;
Identifiers: 99900547110401842
Language: English
Resource Type: Journal article