mzQuality: An Open-Source Software Tool for Quality Monitoring and Reporting of Targeted Mass Spectrometry Measurements

Analyzing metabolites using mass spectrometry provides
valuable
insight into an individual’s health or disease status. However,
various sources of experimental variation can be introduced during
sample handling, preparation, and measurement, which can negatively
affect the data. Quality assurance and quality control practices are
essential to ensuring accurate and reproducible metabolomics data.
These practices include measuring reference samples to monitor instrument
stability, blank samples to evaluate the background signal, and strategies
to correct for changes in instrumental performance. In this context,
we introduce mzQuality, a user-friendly, open-source R-Shiny app designed
to assess and correct technical variations in mass spectrometry-based
metabolomics data. It processes peak-integrated data independently
of vendor software and provides essential quality control features,
including batch correction, outlier detection, and background signal
assessment, and it visualizes trends in signal or retention time.
We demonstrate its functionality using a data set of 419 samples measured
across six batches, including quality control samples. mzQuality visualizes
data through sample plots, PCA plots, and violin plots, which illustrate
its ability to reduce the effect of experiment variation. Compound
quality is further assessed by evaluating the relative standard deviation
of quality control samples and the background signal from blank samples.
Based on these quality metrics, compounds are classified into confidence
levels. mzQuality provides an accessible solution to improve the data
quality without requiring prior programming skills. Its customizable
settings integrate seamlessly into research workflows, enhancing the
accuracy and reproducibility of the metabolomics data. Additionally,
with an R-compatible output, the data are ready for statistical analysis
and biological interpretation.