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dc.contributor.authorLi, Qike
dc.contributor.authorSchissler, A. Grant
dc.contributor.authorGardeux, Vincent
dc.contributor.authorBerghout, Joanne
dc.contributor.authorAchour, Ikbel
dc.contributor.authorKenost, Colleen
dc.contributor.authorLi, Haiquan
dc.contributor.authorZhang, Hao Helen
dc.contributor.authorLussier, Yves A.
dc.date.accessioned2019-04-18T18:36:55Z
dc.date.available2019-04-18T18:36:55Z
dc.date.issued2017-01-01
dc.identifier.citationLi, Q., Schissler, A. G., Gardeux, V., Berghout, J., Achour, I., Kenost, C., ... & Lussier, Y. A. (2017). kMEn: Analyzing noisy and bidirectional transcriptional pathway responses in single subjects. Journal of biomedical informatics, 66, 32-41.en_US
dc.identifier.issn1532-0480
dc.identifier.pmid28007582
dc.identifier.doi10.1016/j.jbi.2016.12.009
dc.identifier.urihttp://hdl.handle.net/10150/632079
dc.description.abstractMotivation: Understanding dynamic, patient-level transcriptomic response to therapy is an important step forward for precision medicine. However, conventional transcriptome analysis aims to discover cohort-level change, lacking the capacity to unveil patient-specific response to therapy. To address this gap, we previously developed two N-of-l-pathways methods, Wilcoxon and Mahalanobis distance, to detect unidirectionally responsive transcripts within a pathway using a pair of samples from a single subject. Yet, these methods cannot recognize bidirectionally (up and down) responsive pathways. Further, our previous approaches have not been assessed in presence of background noise and are not designed to identify differentially expressed mRNAs between two samples of a patient taken in different contexts (e.g. cancer vs non cancer), which we termed responsive transcripts (RTs). Methods: We propose a new N-of-l-pathways method, k-Means Enrichment (kMEn), that detects bidirectionally responsive pathways, despite background noise, using a pair of transcriptomes from a single patient. kMEn identifies transcripts responsive to the stimulus through k-means clustering and then tests for an over-representation of the responsive genes within each pathway. The pathways identified by kMEn are mechanistically interpretable pathways significantly responding to a stimulus. Results: In similar to 9000 simulations varying six parameters, superior performance of kMEn over previous single-subject methods is evident by: (i) improved precision-recall at various levels of bidirectional response and (ii) lower rates of false positives (1-specificity) when more than 10% of genes in the genome are differentially expressed (background noise). In a clinical proof-of-concept, personal treatment specific pathways identified by kMEn correlate with therapeutic response (p-value < 0.01). Conclusion: Through improved single-subject transcriptome dynamics of bidirectionally-regulated signals, kMEn provides a novel approach to identify mechanism-level biomarkers. (C) 2016 Published by Elsevier Inc.en_US
dc.description.sponsorshipNIH [K22LM008308]; NSF [DMS-1309507, DMS-1418172]; NCI of the University of Arizona Cancer Center [P30CA023074]en_US
dc.language.isoenen_US
dc.publisherACADEMIC PRESS INC ELSEVIER SCIENCEen_US
dc.rights© 2016 Published by Elsevier Inc.en_US
dc.subjectHIV treatment responseen_US
dc.subjectN-of-1-pathwaysen_US
dc.subjectPathway analysisen_US
dc.subjectPrecision medicineen_US
dc.subjectSingle subject analysisen_US
dc.subjectk-means clusteringen_US
dc.titlekMEn: Analyzing noisy and bidirectional transcriptional pathway responses in single subjectsen_US
dc.typeArticleen_US
dc.contributor.departmentUniv Arizona, Ctr Biomed Informat & Biostaten_US
dc.contributor.departmentUniv Arizona, Inst Bio5en_US
dc.contributor.departmentUniv Arizona, Dept Meden_US
dc.contributor.departmentUniv Arizona, Grad Interdisciplinary Program Staten_US
dc.contributor.departmentUniv Arizona, Dept Mathen_US
dc.contributor.departmentUniv Arizona, Canc Ctren_US
dc.identifier.journalJOURNAL OF BIOMEDICAL INFORMATICSen_US
dc.description.note12 month embargo; available online 19 December 2016.en_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.journaltitleJournal of biomedical informatics
refterms.dateFOA2017-12-19T00:00:00Z


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