2.50
Hdl Handle:
http://hdl.handle.net/2336/66474
Title:
Bayesian testing of many hypothesis x many genes: a study of sleep apnea
Authors:
Jensen, Shane T; Erkan, Ibrahim; Arnardottir, Erna S; Small, Dylan S
Citation:
Ann Appl Stat. 2009: ahead of print
Issue Date:
Jun-2009
Abstract:
Substantial statistical research has recently been devoted to the analysis of large-scale microarray experiments which provide a measure of the simultaneous expression of thousands of genes in a particular condition. A typical goal is the comparison of gene expression between two conditions (eg. diseased vs. non-diseased) to detect genes which show differential expression. Classical hypothesis testing procedures have been applied to this problem and more recent work has employed sophisticated models that allow for the sharing of information across genes.However,many recent gene expression studies have an experimental design with several conditions that requires an even more involved hypothesis testing approach. In this paper, we use a hierarchical Bayesian model to address the situation where there are many hypotheses that must be simultaneously tested for each gene. In addition to having many hypotheses within each gene, our analysis also addresses the more typical multiple comparison issue of testing many genes simultaneously. We illustrate our approach with an application to a study of genes involved in obstructive sleep apnea in humans.
Description:
To access publisher full text version of this article. Please click on the hyperlink in Additional Links field
Additional Links:
http://www.e-publications.org/ims/submission/index.php/AOAS/user/submissionFile/3239?confirm=4c5c4470

Full metadata record

DC FieldValue Language
dc.contributor.authorJensen, Shane T-
dc.contributor.authorErkan, Ibrahim-
dc.contributor.authorArnardottir, Erna S-
dc.contributor.authorSmall, Dylan S-
dc.date.accessioned2009-04-28T11:39:32Z-
dc.date.available2009-04-28T11:39:32Z-
dc.date.issued2009-06-
dc.date.submitted2009-04-28-
dc.identifier.citationAnn Appl Stat. 2009: ahead of printen
dc.identifier.issn1932-6157-
dc.identifier.urihttp://hdl.handle.net/2336/66474-
dc.descriptionTo access publisher full text version of this article. Please click on the hyperlink in Additional Links fielden
dc.description.abstractSubstantial statistical research has recently been devoted to the analysis of large-scale microarray experiments which provide a measure of the simultaneous expression of thousands of genes in a particular condition. A typical goal is the comparison of gene expression between two conditions (eg. diseased vs. non-diseased) to detect genes which show differential expression. Classical hypothesis testing procedures have been applied to this problem and more recent work has employed sophisticated models that allow for the sharing of information across genes.However,many recent gene expression studies have an experimental design with several conditions that requires an even more involved hypothesis testing approach. In this paper, we use a hierarchical Bayesian model to address the situation where there are many hypotheses that must be simultaneously tested for each gene. In addition to having many hypotheses within each gene, our analysis also addresses the more typical multiple comparison issue of testing many genes simultaneously. We illustrate our approach with an application to a study of genes involved in obstructive sleep apnea in humans.en
dc.language.isoenen
dc.publisherInstitute of Mathematical Statisticsen
dc.relation.urlhttp://www.e-publications.org/ims/submission/index.php/AOAS/user/submissionFile/3239?confirm=4c5c4470en
dc.subject.meshSleep Apnea Syndromesen
dc.titleBayesian testing of many hypothesis x many genes: a study of sleep apneaen
dc.typeArticleen
dc.contributor.departmentDepartment of Respiratory Medicine and Sleep, Landspitali University Hospital, 108 Reykjavik, Iceland - E-mail: ernasif@landspitali.isen
dc.identifier.journalAnnals of applied statisticsen
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