ArticleBioData mining2009
Statistical quality assessment and outlier detection for liquid chromatography-mass spectrometry experiments.
Article in BioData mining, 2009. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 10 papers.
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Who cites it
10 citing papers in PubMed.
- QCAnalytical chemistry · 2024Article
- Specter: linear deconvolution for targeted analysis of data-independent acquisition mass spectrometry proteomics.Nature methods · 2018Article
- Effects of delayed NSAID administration after experimental eccentric contraction injury - A cellular and proteomics study.PloS one · 2017Article
- QCScreen: a software tool for data quality control in LC-HRMS based metabolomics.BMC bioinformatics · 2015Article
- Comparison of the Mahalanobis distance and Pearson's χ² statistic as measures of similarity of isotope patterns.Journal of the American Society for Mass Spectrometry · 2014Article
- Quantitative characterization of cellular membrane-receptor heterogeneity through statistical and computational modeling.PloS one · 2014Article
- LC-MS-based metabolomics.Molecular bioSystems · 2012Review
- Generalized Linear and Mixed Models for Label-Free Shotgun Proteomics.Statistics and its interface · 2012Article
- Improved quality control processing of peptide-centric LC-MS proteomics data.Bioinformatics (Oxford, England) · 2011Article
- SVM model for quality assessment of medium resolution mass spectra from 18O-water labeling experiments.Journal of proteome research · 2011Article
Corrections and comments
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Authors and funding
6 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
backgroundQuality assessment methods, that are common place in engineering and industrial production, are not widely spread in large-scale proteomics experiments. But modern technologies such as Multi-Dimensional Liquid Chromatography coupled to Mass Spectrometry (LC-MS) produce large quantities of proteomic data. These data are prone to measurement errors and reproducibility problems such that an automatic quality assessment and control become increasingly important.
resultsWe propose a methodology to assess the quality and reproducibility of data generated in quantitative LC-MS experiments. We introduce quality descriptors that capture different aspects of the quality and reproducibility of LC-MS data sets. Our method is based on the Mahalanobis distance and a robust Principal Component Analysis.
conclusionWe evaluate our approach on several data sets of different complexities and show that we are able to precisely detect LC-MS runs of poor signal quality in large-scale studies.
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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.