Signatures for Mass Spectrometry Data Quality

Brett G. Amidan, Daniel J. Orton, Brian L. LaMarche, Matthew E. Monroe, Ronald J. Moore, Alexander M. Venzin, Richard D. Smith, Landon H. Sego, Mark F. Tardiff, Samuel H. Payne
2014 Journal of Proteome Research  
Ensuring data quality and proper instrument functionality is a prerequisite for scientific investigation. Manual quality assurance is time-consuming and subjective. Metrics for describing liquid chromatography mass spectrometry (LC−MS) data have been developed; however, the wide variety of LC−MS instruments and configurations precludes applying a simple cutoff. Using 1150 manually classified quality control (QC) data sets, we trained logistic regression classification models to predict whether
more » ... data set is in or out of control. Model parameters were optimized by minimizing a loss function that accounts for the tradeoff between false positive and false negative errors. The classifier models detected bad data sets with high sensitivity while maintaining high specificity. Moreover, the composite classifier was dramatically more specific than single metrics. Finally, we evaluated the performance of the classifier on a separate validation set where it performed comparably to the results for the testing/training data sets. By presenting the methods and software used to create the classifier, other groups can create a classifier for their specific QC regimen, which is highly variable lab-to-lab. In total, this manuscript presents 3400 LC−MS data sets for the same QC sample (whole cell lysate of Shewanella oneidensis), deposited to the ProteomeXchange with identifiers PXD000320−PXD000324.
doi:10.1021/pr401143e pmid:24611607 pmcid:PMC4104976 fatcat:c3lwq3kmnbbyvb2myrycpmwyyy