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The Functional False Discovery Rate with Applications to Genomics

Xiongzhi Chen, David G. Robinson, John D. Storey
doi: https://doi.org/10.1101/241133
Xiongzhi Chen
*Lewis-Sigler Institute for Integrative Genomics, Princeton University, Princeton, NJ 08544, USA
†Present address: Department of Mathematics and Statistics, Washington State University, Pullman, WA 99163, USA
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David G. Robinson
*Lewis-Sigler Institute for Integrative Genomics, Princeton University, Princeton, NJ 08544, USA
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John D. Storey
*Lewis-Sigler Institute for Integrative Genomics, Princeton University, Princeton, NJ 08544, USA
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  • For correspondence: jstorey@princeton.edu
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Abstract

The false discovery rate 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 false discovery rate. We develop a new framework for formulating and estimating false discovery rates 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 false discovery rate is then treated as a function of this informative variable. We consider two applications in genomics. Our first 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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The copyright holder for this preprint is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made available under a CC-BY-ND 4.0 International license.
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Posted December 30, 2017.
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The Functional False Discovery Rate with Applications to Genomics
Xiongzhi Chen, David G. Robinson, John D. Storey
bioRxiv 241133; doi: https://doi.org/10.1101/241133
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The Functional False Discovery Rate with Applications to Genomics
Xiongzhi Chen, David G. Robinson, John D. Storey
bioRxiv 241133; doi: https://doi.org/10.1101/241133

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