ArticleJournal of the American Society for Mass Spectrometry2014
Implementation of statistical process control for proteomic experiments via LC MS/MS.
Article in Journal of the American Society for Mass Spectrometry, 2014. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 21 papers.
What it found
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
21 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
- iDIA-QC: AI-empowered data-independent acquisition mass spectrometry-based quality control.Nature communications · 2025Article
- 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
- A framework for quality control in quantitative proteomics.bioRxiv : the preprint server for biology · 2024Article
- Quality Control-A Stepchild in Quantitative Proteomics: A Case Study for the Human CSF Proteome.Biomolecules · 2023Article
- Methods for Proteomic Analyses of Mycobacteria.Methods in molecular biology (Clifton, N.J.) · 2021Article
- The Skyline ecosystem: Informatics for quantitative mass spectrometry proteomics.Mass spectrometry reviews · 2020Review
- 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
- Quality Control Analysis in Real-time (QC-ART): A Tool for Real-time Quality Control Assessment of Mass Spectrometry-based Proteomics Data.Molecular & cellular proteomics : MCP · 2018Article
- QCloud: A cloud-based quality control system for mass spectrometry-based proteomics laboratories.PloS one · 2018Article
- Article
- MSstatsQC: Longitudinal System Suitability Monitoring and Quality Control for Targeted Proteomic Experiments.Molecular & cellular proteomics : MCP · 2017Article
- An Automated Pipeline to Monitor System Performance in Liquid Chromatography-Tandem Mass Spectrometry Proteomic Experiments.Journal of proteome research · 2016Article
- Innovative Strategies for Decreasing Blood Collection Wait Times for Patients in Early-Phase Cancer Clinical Trials.Journal of oncology practice · 2016Article
- Methods for SWATH™: Data Independent Acquisition on TripleTOF Mass Spectrometers.Methods in molecular biology (Clifton, N.J.) · 2016Article
- Multiplexed, Scheduled, High-Resolution Parallel Reaction Monitoring on a Full Scan QqTOF Instrument with Integrated Data-Dependent and Targeted Mass Spectrometric Workflows.Analytical chemistry · 2015Article
- Multiplexed peptide analysis using data-independent acquisition and Skyline.Nature protocols · 2015Article
- Article
Corrections and comments
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Authors and funding
8 authors.
Funding
Abstract
Statistical process control (SPC) is a robust set of tools that aids in the visualization, detection, and identification of assignable causes of variation in any process that creates products, services, or information. A tool has been developed termed Statistical Process Control in Proteomics (SProCoP) which implements aspects of SPC (e.g., control charts and Pareto analysis) into the Skyline proteomics software. It monitors five quality control metrics in a shotgun or targeted proteomic workflow. None of these metrics require peptide identification. The source code, written in the R statistical language, runs directly from the Skyline interface, which supports the use of raw data files from several of the mass spectrometry vendors. It provides real time evaluation of the chromatographic performance (e.g., retention time reproducibility, peak asymmetry, and resolution), and mass spectrometric performance (targeted peptide ion intensity and mass measurement accuracy for high resolving power instruments) via control charts. Thresholds are experiment- and instrument-specific and are determined empirically from user-defined quality control standards that enable the separation of random noise and systematic error. Finally, Pareto analysis provides a summary of performance metrics and guides the user to metrics with high variance. The utility of these charts to evaluate proteomic experiments is illustrated in two case studies.
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What OpenQuestion holds
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.