ArticleMolecular & cellular proteomics : MCP2017
MSstatsQC: Longitudinal System Suitability Monitoring and Quality Control for Targeted Proteomic Experiments.
Article in Molecular & cellular proteomics : MCP, 2017. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 16 papers.
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Who cites it
16 citing papers in PubMed.
- MSstatsQC-ML: A Supervised Machine Learning Approach to Monitor System Suitability and Quality Control in Mass Spectrometry-Based Proteomics.Journal of proteome research · 2026Article
- pmultiqc: An Open-Source, Lightweight, and Metadata-Oriented QC Reporting Library for MS Proteomics.Molecular & cellular proteomics : MCP · 2026Article
- A Framework for Quality Control in Quantitative Proteomics.Journal of proteome research · 2024Article
- Quality Control in the Mass Spectrometry Proteomics Core: A Practical Primer.Journal of biomolecular techniques : JBT · 2024Article
- Comprehensive Overview of Bottom-Up Proteomics Using Mass Spectrometry.ACS measurement science au · 2024Review
- A framework for quality control in quantitative proteomics.bioRxiv : the preprint server for biology · 2024Article
- Article
- A Sensitive and Controlled Data-Independent Acquisition Method for Proteomic Analysis of Cell Therapies.Journal of proteome research · 2022Article
- A system suitability testing platform for untargeted, high-resolution mass spectrometry.Frontiers in molecular biosciences · 2022Article
- The Time Has Come for Quantitative Protein Mass Spectrometry Tests That Target Unmet Clinical Needs.Journal of the American Society for Mass Spectrometry · 2021Article
- Methods for Proteomic Analyses of Mycobacteria.Methods in molecular biology (Clifton, N.J.) · 2021Article
- Mass Spectrometry Advances and Perspectives for the Characterization of Emerging Adoptive Cell Therapies.Molecules (Basel, Switzerland) · 2020Review
- Machine Learning Reveals Protein Signatures in CSF and Plasma Fluids of Clinical Value for ALS.Scientific reports · 2018Article
- Proteomics and Lipidomics in Inflammatory Bowel Disease Research: From Mechanistic Insights to Biomarker Identification.International journal of molecular sciences · 2018Review
- Quality assessment and interference detection in targeted mass spectrometry data using machine learning.Clinical proteomics · 2018Article
- QCloud: A cloud-based quality control system for mass spectrometry-based proteomics laboratories.PloS one · 2018Article
Corrections and comments
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Authors and funding
7 authors.
Funding
Abstract
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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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.