A Bayesian decision procedure for testing multiple hypotheses in DNA microarray experiments

Gómez-Villegas Miguel A. (), Salazar Isabel and Sanz Luis
Additional contact information
Gómez-Villegas Miguel A.: Dpto. de Estadística e I.O., Facultad de Ciencias Matemáticas, Plaza de las Ciencias, 3, Universidad Complutense de Madrid, 28040–Madrid, Spain
Salazar Isabel: Dpto. de Producción Animal, Facultad de Veterinaria, Universidad Complutense de Madrid, 28040–Madrid, Spain
Sanz Luis: Dpto. de Estadística e I.O., Facultad de Ciencias Matemáticas, Plaza de las Ciencias, 3, Universidad Complutense de Madrid, 28040–Madrid, Spain

Statistical Applications in Genetics and Molecular Biology, 2014, vol. 13, issue 1, 49-65

Abstract: DNA microarray experiments require the use of multiple hypothesis testing procedures because thousands of hypotheses are simultaneously tested. We deal with this problem from a Bayesian decision theory perspective. We propose a decision criterion based on an estimation of the number of false null hypotheses (FNH), taking as an error measure the proportion of the posterior expected number of false positives with respect to the estimated number of true null hypotheses. The methodology is applied to a Gaussian model when testing bilateral hypotheses. The procedure is illustrated with both simulated and real data examples and the results are compared to those obtained by the Bayes rule when an additive loss function is considered for each joint action and the generalized loss 0–1 function for each individual action. Our procedure significantly reduced the percentage of false negatives whereas the percentage of false positives remains at an acceptable level.

Keywords: Bayes rule; Bayesian decision; multiple hypothesis; posterior expected loss; false positives; false negatives (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-2012-0076 (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:1:p:49-65:n:4

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

DOI: 10.1515/sagmb-2012-0076

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 ().