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dc.contributor.authorSchissler, A Grant
dc.contributor.authorPiegorsch, Walter W
dc.contributor.authorLussier, Yves A
dc.date.accessioned2019-03-04T21:40:53Z
dc.date.available2019-03-04T21:40:53Z
dc.date.issued2017-05-29
dc.identifier.citationSchissler, A. G., Piegorsch, W. W., & Lussier, Y. A. (2018). Testing for differentially expressed genetic pathways with single-subject N-of-1 data in the presence of inter-gene correlation. Statistical Methods in Medical Research, 27(12), 3797–3813. https://doi.org/10.1177/0962280217712271en_US
dc.identifier.issn1477-0334
dc.identifier.pmid28552011
dc.identifier.doi10.1177/0962280217712271
dc.identifier.urihttp://hdl.handle.net/10150/631770
dc.description.abstractModern precision medicine increasingly relies on molecular data analytics, wherein development of interpretable single-subject ("N-of-1") signals is a challenging goal. A previously developed global framework, N-of-1- pathways, employs single-subject gene expression data to identify differentially expressed gene set pathways in an individual patient. Unfortunately, the limited amount of data within the single-subject, N-of-1 setting makes construction of suitable statistical inferences for identifying differentially expressed gene set pathways difficult, especially when non-trivial inter-gene correlation is present. We propose a method that exploits external information on gene expression correlations to cluster positively co-expressed genes within pathways, then assesses differential expression across the clusters within a pathway. A simulation study illustrates that the cluster-based approach exhibits satisfactory false-positive error control and reasonable power to detect differentially expressed gene set pathways. An example with a single N-of-1 patient's triple negative breast cancer data illustrates use of the methodology.en_US
dc.description.sponsorshipU.S. National Science Foundation [1228509]; U.S. National Institutes of Health [R03ES027394]en_US
dc.language.isoenen_US
dc.publisherSAGE PUBLICATIONS LTDen_US
dc.rights© The Author(s) 2017; article reuse guidelines: sagepub.com/journals-permissionsen_US
dc.subjectGene expression dataen_US
dc.subjectN-of-1en_US
dc.subjectRNA-seqen_US
dc.subjectaffinity propagation clusteringen_US
dc.subjectexemplar learningen_US
dc.subjectgene seten_US
dc.subjectinter-gene correlationen_US
dc.subjectprecision medicineen_US
dc.subjectsingle-subject inferenceen_US
dc.subjecttriple negative breast canceren_US
dc.titleTesting for differentially expressed genetic pathways with single-subject N-of-1 data in the presence of inter-gene correlation.en_US
dc.typeArticleen_US
dc.contributor.departmentUniv Arizona, Interdisciplinary Program Staten_US
dc.contributor.departmentUniv Arizona, Ctr Biomed Informat & Biostat CB2en_US
dc.contributor.departmentUniv Arizona, Inst BIO5en_US
dc.contributor.departmentUniv Arizona, Dept Meden_US
dc.contributor.departmentUniv Arizona, Dept Mathen_US
dc.identifier.journalSTATISTICAL METHODS IN MEDICAL RESEARCHen_US
dc.description.collectioninformationThis item from the UA Faculty Publications collection is made available by the University of Arizona with support from the University of Arizona Libraries. If you have questions, please contact us at repository@u.library.arizona.edu.en_US
dc.eprint.versionFinal accepted manuscripten_US
dc.source.journaltitleStatistical methods in medical research
refterms.dateFOA2019-03-04T21:40:53Z


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