Title
Unsupervised quality assessment of mass spectrometry proteomics experiments by multivariate quality control metrics
Author
Faculty/Department
Faculty of Sciences. Biology
Faculty of Sciences. Chemistry
Faculty of Sciences. Mathematics and Computer Science
Faculty of Pharmaceutical, Biomedical and Veterinary Sciences . Biomedical Sciences
Publication type
article
Publication
Subject
Chemistry
Biology
Human medicine
Computer. Automation
Source (journal)
Journal of proteome research
Volume/pages
15(2016) :4 , p. 1300-1307
ISSN
1535-3893
ISI
000373519900019
Carrier
E
Target language
English (eng)
Full text (Publishers DOI)
Affiliation
University of Antwerp
Abstract
Despite many technological and computational advances, the results of a mass spectrometry proteomics experiment are still subject to a large variability. For the understanding and evaluation of how technical variability affects the results of an experiment, several computationally derived quality control metrics have been introduced. However, despite the availability of these metrics, a systematic approach to quality control is often still lacking because the metrics are not fully understood and are hard to interpret. Here, we present a toolkit of powerful techniques to analyze and interpret multivariate quality control metrics to assess the quality of mass spectrometry proteomics experiments. We show how unsupervised techniques applied to these quality control metrics can provide an initial discrimination between low-quality experiments and high-quality experiments prior to manual investigation. Furthermore, we provide a technique to obtain detailed information on the quality control metrics that are related to the decreased performance, which can be used as actionable information to improve the experimental setup. Our toolkit is released as open-source and can be downloaded from https://bitbucket.org/proteinspector/qc_analysis/.
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