P-value calibration for multiple testing problems in genomics

Ferguson John P. () and Palejev Dean
Additional contact information
Ferguson John P.: Department of Nephrology, Graduate Entry Medical School, University of Limerick, Clinical Academic Liaison Building, St Nessans Road, Limerick, Ireland
Palejev Dean: Institute of Mathematics and Informatics, Bulgarian Academy of Sciences, Acad. Georgi Bonchev Str., Block 8, Sofia 1113, Bulgaria

Statistical Applications in Genetics and Molecular Biology, 2014, vol. 13, issue 6, 659-673

Abstract: Conservative statistical tests are often used in complex multiple testing settings in which computing the type I error may be difficult. In such tests, the reported p-value for a hypothesis can understate the evidence against the null hypothesis and consequently statistical power may be lost. False Discovery Rate adjustments, used in multiple comparison settings, can worsen the unfavorable effect. We present a computationally efficient and test-agnostic calibration technique that can substantially reduce the conservativeness of such tests. As a consequence, a lower sample size might be sufficient to reject the null hypothesis for true alternatives, and experimental costs can be lowered. We apply the calibration technique to the results of DESeq, a popular method for detecting differentially expressed genes from RNA sequencing data. The increase in power may be particularly high in small sample size experiments, often used in preliminary experiments and funding applications.

Keywords: calibration; conditional likelihood; conservative test; FDR correction; p-value (search for similar items in EconPapers)
Date: 2014
References: View references in EconPapers View complete reference list from CitEc
Citations:

Downloads: (external link)
https://doi.org/10.1515/sagmb-2013-0074 (text/html)
For access to full text, subscription to the journal or payment for the individual article is required.

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:bpj:sagmbi:v:13:y:2014:i:6:p:15:n:3

Ordering information: This journal article can be ordered from
https://www.degruyter.com/journal/key/sagmb/html

DOI: 10.1515/sagmb-2013-0074

Access Statistics for this article

Statistical Applications in Genetics and Molecular Biology is currently edited by Michael P. H. Stumpf

More articles in Statistical Applications in Genetics and Molecular Biology from De Gruyter
Bibliographic data for series maintained by Peter Golla ().