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Mol Cell Proteomics. 2017 Jul;16(7):1335-1347. doi: 10.1074/mcp.M116.064774. Epub 2017 May 8.

MSstatsQC: Longitudinal System Suitability Monitoring and Quality Control for Targeted Proteomic Experiments.

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From the ‡College of Computer and Information Science, Northeastern University, Massachusetts 02115.
§College of Science, Mugla Sitki Kocman University 48000, Turkey.
¶Broad Institute of MIT and Harvard, Cambridge, Massachusetts 02142.
‖Department of Biological Sciences, Center for Human Health and the Environment, North Carolina State University, Raleigh, North Carolina 27695.
**Department of Genome Sciences, University of Washington School of Medicine, Seattle, Washington 98195.
‡‡Buck Institute for Research on Aging, Novato, California 94945;
From the ‡College of Computer and Information Science, Northeastern University, Massachusetts 02115;
§§College of Science, Northeastern University, Massachusetts 02115.


Selected 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.

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