Signatures for Mass Spectrometry Data Quality

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 a data set is in or out of control. Model parameters
were optimized by minimizing a loss function that accounts for the
trade-off 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.