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 likeliho

SUBMITTER: Kumler W 

PROVIDER: S-EPMC10612323 | biostudies-literature | 2023 Oct

REPOSITORIES: biostudies-literature