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dc.contributor.authorDoğu, Eralp
dc.contributor.authorMohammad-Taheri, Sara
dc.contributor.authorAbbatiello, Susan E.
dc.contributor.authorBereman, Michael S.
dc.contributor.authorMacLean, Brendan
dc.contributor.authorSchilling, Birgit
dc.contributor.authorVitek, Olga
dc.date.accessioned2020-11-20T14:52:36Z
dc.date.available2020-11-20T14:52:36Z
dc.date.issued2017
dc.identifier.issn1535-9476
dc.identifier.issn1535-9484
dc.identifier.urihttps://doi.org/10.1074/mcp.M116.064774
dc.identifier.urihttps://hdl.handle.net/20.500.12809/1923
dc.descriptionWOS: 000404597500013en_US
dc.descriptionPubMed ID: 28483925en_US
dc.description.abstractSelected Reaction Monitoring (SRM) is a powerful tool for targeted detection and quantification of peptides in complex matrices. An important objective of SRM is to obtain peptide quantifications that are (1) suitable for the investigation, and (2) reproducible across laboratories and runs. The first objective is achieved by system suitability tests (SST), which verify that mass spectrometric instrumentation performs as specified. The second objective is achieved by quality control (QC), which provides in-process quality assurance of the sample profile. A common aspect of SST and QC is the longitudinal nature of the data. Although SST and QC have received a lot of attention in the proteomic community, the currently used statistical methods are limited. This manuscript improves upon the statistical methodology for SST and QC that is currently used in proteomics. It adapts the modern methods of longitudinal statistical process control, such as simultaneous and time weighted control charts and change point analysis, to SST and QC of SRM experiments, discusses their advantages, and provides practical guidelines. Evaluations on simulated data sets, and on data sets from the Clinical Proteomics Technology Assessment for Cancer (CPTAC) consortium, demonstrated that these methods substantially improve our ability of real time monitoring, early detection and prevention of chromatographic and instrumental problems. We implemented the methods in an open-source R-based software package MSstatsQC and its web-based graphical user interface. They are available for use stand-alone, or for integration with automated pipelines. Although the examples focus on targeted proteomics, the statistical methods in this manuscript apply more generally to quantitative proteomics.en_US
dc.description.sponsorshipTIER 3 Award from Northeastern University; NSF CAREERNational Science Foundation (NSF)NSF - Office of the Director (OD) [DBI-1054826]; NCRR Shared Instrumentation program for instrumentation [S10 RR027953]; NATIONAL CENTER FOR RESEARCH RESOURCESUnited States Department of Health & Human ServicesNational Institutes of Health (NIH) - USANIH National Center for Research Resources (NCRR) [S10RR027953] Funding Source: NIH RePORTER; Direct For Biological SciencesNational Science Foundation (NSF)NSF - Directorate for Biological Sciences (BIO) [1501900] Funding Source: National Science Foundationen_US
dc.description.sponsorshipThis work was supported in part by the TIER 3 Award from Northeastern University and by the NSF CAREER award DBI-1054826 to O.V. The Buck Institute acknowledges support from the NCRR Shared Instrumentation program for instrumentation (grant S10 RR027953 to Bradford W. Gibson).en_US
dc.item-language.isoengen_US
dc.publisherAmer Soc Biochemistry Molecular Biology Incen_US
dc.item-rightsinfo:eu-repo/semantics/openAccessen_US
dc.titleMSstatsQC: Longitudinal System Suitability Monitoring and Quality Control for Targeted Proteomic Experimentsen_US
dc.item-typearticleen_US
dc.contributor.departmentMÜ, Fen Fakültesi, İstatistik Bölümüen_US
dc.contributor.institutionauthorDoğu, Eralp
dc.identifier.doi10.1074/mcp.M116.064774
dc.identifier.volume16en_US
dc.identifier.issue7en_US
dc.identifier.startpage1335en_US
dc.identifier.endpage1347en_US
dc.relation.journalMolecular & Cellular Proteomicsen_US
dc.relation.publicationcategoryMakale - Uluslararası Hakemli Dergi - Kurum Öğretim Elemanıen_US


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