Liquid chromatography–mass
spectrometry (LC-MS) is a powerful
and widely used technique for measuring the abundance of chemical
species in living systems. Its sensitivity, analytical specificity,
and direct applicability to biofluids and tissue extracts impart great
promise for the discovery and mechanistic characterization of biomarker
panels for disease detection, health monitoring, patient stratification,
and treatment personalization. Global metabolic profiling applications
yield complex data sets consisting of multiple feature measurements
for each chemical species observed. While this multiplicity can be
useful in deriving enhanced analytical specificity and chemical identities
from LC-MS data, data set inflation and quantitative imprecision among
related features is problematic for statistical analyses and interpretation.
This Perspective provides a critical evaluation of global profiling
data fidelity with respect to measurement linearity and the quantitative
response variation observed among components of the spectra. These
elements of data quality are widely overlooked in untargeted metabolomics
yet essential for the generation of data that accurately reflect the
metabolome. Advanced feature filtering informed by linear range estimation
and analyte response factor assessment is advocated as an attainable
means of controlling LC-MS data quality in global profiling studies
and exemplified herein at both the feature and data set level.