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dc.contributor.authorLi, Qike
dc.contributor.authorSchissler, A. Grant
dc.contributor.authorGardeux, Vincent
dc.contributor.authorAchour, Ikbel
dc.contributor.authorKenost, Colleen
dc.contributor.authorBerghout, Joanne
dc.contributor.authorLi, Haiquan
dc.contributor.authorZhang, Hao Helen
dc.contributor.authorLussier, Yves A.
dc.date.accessioned2017-10-09T23:26:17Z
dc.date.available2017-10-09T23:26:17Z
dc.date.issued2017-05-24
dc.identifier.citationN-of-1-pathways MixEnrich: advancing precision medicine via single-subject analysis in discovering dynamic changes of transcriptomes 2017, 10 (S1) BMC Medical Genomicsen
dc.identifier.issn1755-8794
dc.identifier.pmid28589853
dc.identifier.doi10.1186/s12920-017-0263-4
dc.identifier.urihttp://hdl.handle.net/10150/625841
dc.description.abstractBackground: Transcriptome analytic tools are commonly used across patient cohorts to develop drugs and predict clinical outcomes. However, as precision medicine pursues more accurate and individualized treatment decisions, these methods are not designed to address single-patient transcriptome analyses. We previously developed and validated the N-of-1-pathways framework using two methods, Wilcoxon and Mahalanobis Distance (MD), for personal transcriptome analysis derived from a pair of samples of a single patient. Although, both methods uncover concordantly dysregulated pathways, they are not designed to detect dysregulated pathways with up- and down-regulated genes (bidirectional dysregulation) that are ubiquitous in biological systems. Results: We developed N-of-1-pathways MixEnrich, a mixture model followed by a gene set enrichment test, to uncover bidirectional and concordantly dysregulated pathways one patient at a time. We assess its accuracy in a comprehensive simulation study and in a RNA-Seq data analysis of head and neck squamous cell carcinomas (HNSCCs). In presence of bidirectionally dysregulated genes in the pathway or in presence of high background noise, MixEnrich substantially outperforms previous single-subject transcriptome analysis methods, both in the simulation study and the HNSCCs data analysis (ROC Curves; higher true positive rates; lower false positive rates). Bidirectional and concordant dysregulated pathways uncovered by MixEnrich in each patient largely overlapped with the quasi-gold standard compared to other single-subject and cohort-based transcriptome analyses. Conclusion: The greater performance of MixEnrich presents an advantage over previous methods to meet the promise of providing accurate personal transcriptome analysis to support precision medicine at point of care.
dc.description.sponsorshipNIH [K22LM008308]; NSF [DMS-1309507, DMS-1418172]; University of Arizona Center for Biomedical Informatics and Biostatistics; NCI of the University of Arizona Cancer Center [P30CA023074]en
dc.language.isoenen
dc.publisherBIOMED CENTRAL LTDen
dc.relation.urlhttp://bmcmedgenomics.biomedcentral.com/articles/10.1186/s12920-017-0263-4en
dc.rights© The Author(s). 2017 Open Access This article is distributed under the terms of the Creative Commons Attribution 4.0 International License.en
dc.subjectPrecision Medicineen
dc.subjectSingle-Subject Analysisen
dc.subjectN-of-1-pathwaysen
dc.subjectMixture Modelen
dc.subjectRNA-Seqen
dc.subjectHead and neck squamous cell carcinomas (HNSCCs)en
dc.titleN-of-1-pathways MixEnrich: advancing precision medicine via single-subject analysis in discovering dynamic changes of transcriptomesen
dc.typeArticleen
dc.contributor.departmentUniv Arizona, Ctr Biomed Informat & Biostaten
dc.contributor.departmentUniv Arizona, Inst Bio5en
dc.contributor.departmentUniv Arizona, Dept Meden
dc.contributor.departmentUniv Arizona, Grad Interdisciplinary Program Staten
dc.contributor.departmentUniv Arizona, Dept Mathen
dc.contributor.departmentUniv Arizona, Canc Ctren
dc.identifier.journalBMC Medical Genomicsen
dc.description.noteOpen Access Journal.en
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
dc.eprint.versionFinal published versionen
refterms.dateFOA2018-06-16T01:25:44Z
html.description.abstractBackground: Transcriptome analytic tools are commonly used across patient cohorts to develop drugs and predict clinical outcomes. However, as precision medicine pursues more accurate and individualized treatment decisions, these methods are not designed to address single-patient transcriptome analyses. We previously developed and validated the N-of-1-pathways framework using two methods, Wilcoxon and Mahalanobis Distance (MD), for personal transcriptome analysis derived from a pair of samples of a single patient. Although, both methods uncover concordantly dysregulated pathways, they are not designed to detect dysregulated pathways with up- and down-regulated genes (bidirectional dysregulation) that are ubiquitous in biological systems. Results: We developed N-of-1-pathways MixEnrich, a mixture model followed by a gene set enrichment test, to uncover bidirectional and concordantly dysregulated pathways one patient at a time. We assess its accuracy in a comprehensive simulation study and in a RNA-Seq data analysis of head and neck squamous cell carcinomas (HNSCCs). In presence of bidirectionally dysregulated genes in the pathway or in presence of high background noise, MixEnrich substantially outperforms previous single-subject transcriptome analysis methods, both in the simulation study and the HNSCCs data analysis (ROC Curves; higher true positive rates; lower false positive rates). Bidirectional and concordant dysregulated pathways uncovered by MixEnrich in each patient largely overlapped with the quasi-gold standard compared to other single-subject and cohort-based transcriptome analyses. Conclusion: The greater performance of MixEnrich presents an advantage over previous methods to meet the promise of providing accurate personal transcriptome analysis to support precision medicine at point of care.


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