Picky with peakpicking: assessing chromatographic peak quality with simple metrics in metabolomics

Abstract

Background Chromatographic peakpicking continues to represent a significant bottleneck in automated LC–MS workflows. Uncontrolled false discovery rates and the lack of manually-calibrated quality metrics require researchers to visually evaluate individual peaks, requiring large amounts of time and breaking replicability. This problem is exacerbated in noisy environmental datasets and for novel separation methods such as hydrophilic interaction columns in metabolomics, creating a demand for a simple, intuitive, and robust metric of peak quality. Results Here, we manually labeled four HILIC oceanographic particulate metabolite datasets to assess the performance of individual peak quality metrics. We used these datasets to construct a predictive model calibrated to the likelihood that visual inspection by an MS expert would include a given mass feature in the downstream analysis. We implemented two novel peak quality metrics, a custom signal-to-noise metric and a test of similarity to a bell curve, both calculated from the raw data in the extracted ion chromatogram, and found that these outperformed existing measurements of peak quality. A simple logistic regression model built on two metrics reduced the fraction of false positives in the analysis from 70–80% down to 1–5% and showed minimal overfitting when applied to novel datasets. We then explored the implications of this quality thresholding on the conclusions obtained by the downstream analysis and found that while only 10% of the variance in the dataset could be explained by depth in the default output from the peakpicker, approximately 40% of the variance was explained when restricted to high-quality peaks alone. Conclusions We conclude that the poor performance of peakpicking algorithms significantly reduces the power of both univariate and multivariate statistical analyses to detect environmental differences. We demonstrate that simple models built on intuitive metrics and derived from the raw data are more robust and can outperform more complex models when applied to new data. Finally, we show that in properly curated datasets, depth is a major driver of variability in the marine microbial metabolome and identify several interesting metabolite trends for future investigation.

Metabolites reflect variability introduced by mesoscale eddies in the North Pacific Subtropical Gyre

“…In each case, the differences introduced by SLA were smaller than those of depth when compared directly, with even the high-resolution sampling around the DCM finding slightly more variance explained by the 40 meter difference in sampling depth than the 40 centimeter difference in SLA. This result agrees well with prior research by Heal et al (2021) and Kumler et al (2023) showing the large effect of sampling depth on the metabolome.…”

“…One notable aspect of their clustering was the way in which the deepest samples (DCM plus 20m) grouped most closely to the anticyclonic samples, perhaps indicating that below the DCM the metabolome rapidly approaches a uniform deep-water signal. This result was noted previously in the 175 meter samples of the eddy transect in Kumler et al (2023), where samples from the deep euphotic zone showed greater intradepth similarity than samples from the DCM or 25 meters.…”

“…Samples were also processed via the untargeted workflow detailed in Kumler et al (2023). In short, XCMS (Smith et al 2023) was used for feature detection with CentWave peakpicking, Obiwarp alignment, and peak density correspondence/grouping.…”