A Hidden Markov Model Approach to Testing Multiple Hypotheses on a Tree-Transformed Gene Ontology Graph

dc.contributor.author Liang, Kun
dc.contributor.author Nettleton, Dan
dc.contributor.department Statistics
dc.date 2019-09-12T01:22:04.000
dc.date.accessioned 2020-07-02T06:57:24Z
dc.date.available 2020-07-02T06:57:24Z
dc.date.copyright Fri Jan 01 00:00:00 UTC 2010
dc.date.issued 2010-12-01
dc.description.abstract <p>Gene category testing problems involve testing hundreds of null hypotheses that correspond to nodes in a directed acyclic graph. The logical relationships among the nodes in the graph imply that only some configurations of true and false null hypotheses are possible and that a test for a given node should depend on data from neighboring nodes. We developed a method based on a hidden Markov model that takes the whole graph into account and provides coherent decisions in this structured multiple hypothesis testing problem. The method is illustrated by testing Gene Ontology terms for evidence of differential expression.</p>
dc.description.comments <p>This is a manuscript of an article published as Liang, Kun, and Dan Nettleton. "A hidden Markov model approach to testing multiple hypotheses on a tree-transformed gene ontology graph." <em>Journal of the American Statistical Association</em> 105, no. 492 (2010): 1444-1454. doi: <a href="https://doi.org/10.1198/jasa.2010.tm10195">10.1198/jasa.2010.tm10195</a>. Posted with permission.</p>
dc.format.mimetype application/pdf
dc.identifier archive/lib.dr.iastate.edu/stat_las_pubs/241/
dc.identifier.articleid 1247
dc.identifier.contextkey 14912920
dc.identifier.s3bucket isulib-bepress-aws-west
dc.identifier.submissionpath stat_las_pubs/241
dc.identifier.uri https://dr.lib.iastate.edu/handle/20.500.12876/90556
dc.language.iso en
dc.source.uri https://lib.dr.iastate.edu/cgi/viewcontent.cgi?article=1094&context=stat_las_preprints
dc.subject.disciplines Genetics
dc.subject.disciplines Microarrays
dc.subject.disciplines Statistical Methodology
dc.subject.disciplines Statistical Models
dc.subject.keywords Bayesian data analysis
dc.subject.keywords Differential expression
dc.subject.keywords Directed acyclic graph
dc.subject.keywords False discovery rate
dc.subject.keywords Gene set enrichment analysis
dc.subject.keywords Microarray
dc.subject.keywords Multiple testing
dc.subject.keywords Simultaneous inference
dc.title A Hidden Markov Model Approach to Testing Multiple Hypotheses on a Tree-Transformed Gene Ontology Graph
dc.type article
dc.type.genre article
dspace.entity.type Publication
relation.isOrgUnitOfPublication 264904d9-9e66-4169-8e11-034e537ddbca