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Unscaled Bayes factors for multiple hypothesis testing in microarray experiments

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dc.affiliation.dptoUC3M. Departamento de EstadĂ­sticaes
dc.contributor.authorBertolino, Francesco
dc.contributor.authorCabras, Stefano
dc.contributor.authorCastellanos, MarĂ­a Eugenia
dc.contributor.authorRacugno, Walter
dc.contributor.funderComunidad de Madrides
dc.date.accessioned2021-07-07T08:59:29Z
dc.date.available2021-07-07T08:59:29Z
dc.date.issued2015-12-01
dc.description.abstractMultiple hypothesis testing collects a series of techniques usually based on p-values as a summary of the available evidence from many statistical tests. In hypothesis testing, under a Bayesian perspective, the evidence for a specified hypothesis against an alternative, conditionally on data, is given by the Bayes factor. In this study, we approach multiple hypothesis testing based on both Bayes factors and p-values, regarding multiple hypothesis testing as a multiple model selection problem. To obtain the Bayes factors we assume default priors that are typically improper. In this case, the Bayes factor is usually undetermined due to the ratio of prior pseudo-constants. We show that ignoring prior pseudo-constants leads to unscaled Bayes factor which do not invalidate the inferential procedure in multiple hypothesis testing, because they are used within a comparative scheme. In fact, using partial information from the p-values, we are able to approximate the sampling null distribution of the unscaled Bayes factor and use it within Efron's multiple testing procedure. The simulation study suggests that under normal sampling model and even with small sample sizes, our approach provides false positive and false negative proportions that are less than other common multiple hypothesis testing approaches based only on p-values. The proposed procedure is illustrated in two simulation studies, and the advantages of its use are showed in the analysis of two microarray experiments.en
dc.description.sponsorshipAuthors F. Bertolino, S. Cabras and W. Racugno were partially supported by the Italian Ministry of Education, University and Research. M.E. Castellanos was partially supported by the Spanish Ministry of Science and Technology, under grant MTM2010-19528, CAM of Spain under grant S2009/esp-1594 and the visiting professor program of Regione Autonoma della Sardegna of Italy.en
dc.format.extent14
dc.identifier.bibliographicCitationBertolino, F., Cabras, S., Castellanos, M. E. & Racugno, W. (2015). Unscaled Bayes factors for multiple hypothesis testing in microarray experiments. Statistical Methods in Medical Research, 24(6), pp. 1030-1043.en
dc.identifier.doihttps://doi.org/10.1177%2F0962280212437827
dc.identifier.issn0962-2802
dc.identifier.publicationfirstpage1030
dc.identifier.publicationissue6
dc.identifier.publicationlastpage1043
dc.identifier.publicationtitleStatistical Methods in Medical Researchen
dc.identifier.publicationvolume24
dc.identifier.urihttps://hdl.handle.net/10016/33012
dc.identifier.uxxiAR/0000017830
dc.language.isoeng
dc.publisherSAGE
dc.relation.projectIDComunidad de Madrid. S2009/ESP-1594es
dc.rights© The Authors, 2015.en
dc.rights.accessRightsopen accessen
dc.subject.ecienciaEstadĂ­sticaes
dc.subject.ecienciaInformĂ¡ticaes
dc.subject.ecienciaMatemĂ¡ticases
dc.subject.otherFalse discovery rateen
dc.subject.otherImproper priorsen
dc.subject.otherLocal false discovery rateen
dc.titleUnscaled Bayes factors for multiple hypothesis testing in microarray experimentsen
dc.typeresearch article*
dc.type.hasVersionAM*
dspace.entity.typePublication
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