Computational Variation: An Underinvestigated Quantitative Variability Caused by Automated Data Processing in Untargeted Metabolomics

Computational tools are commonly
used in untargeted metabolomics
to automatically extract metabolic features from liquid chromatography-mass
spectrometry (LC-MS) raw data. However, due to the incapability of
software to accurately determine chromatographic peak heights/areas
for features with poor chromatographic peak shape, automated data
processing in untargeted metabolomics faces additional quantitative
variation (i.e., computational variation) besides the well-recognized
analytical and biological variations. In this work, using multiple
biological samples, we investigated how experimental factors, including
sample concentrations, LC separation columns, and data processing
programs, contribute to computational variation. For example, we found
that the peak height (PH)-based quantification is more precise when
MS-DIAL was used for data processing. We further systematically compared
the different patterns of computational variation between PH- and
peak area (PA)-based quantitative measurements. Our results suggest
that the magnitude of computational variation is highly consistent
at a given concentration. Hence, we proposed a quality control (QC)
sample-based correction workflow to minimize computational variation
by automatically selecting PH or PA-based measurement for each intensity
value. This bioinformatic solution was demonstrated in a metabolomic
comparison of leukemia patients before and after chemotherapy. Our
novel workflow can be effectively applied on 652 out of 915 metabolic
features, and over 31% (206 out of 652) of corrected features showed
distinctly changed statistical significance. Overall, this work highlights
computational variation, a considerable but underinvestigated quantitative
variability in omics-scale quantitative analyses. In addition, the
proposed bioinformatic solution can minimize computational variation,
thus providing a more confident statistical comparison among biological
groups in quantitative metabolomics.