Visualization, Quantification, and Alignment of Spectral Drift in Population Scale Untargeted Metabolomics Data.

ABSTRACT: Untargeted liquid-chromatography-mass spectrometry (LC-MS)-based metabolomics analysis of human biospecimens has become among the most promising strategies for probing the underpinnings of human health and disease. Analysis of spectral data across population scale cohorts, however, is precluded by day-to-day nonlinear signal drifts in LC retention time or batch effects that complicate comparison of thousands of untargeted peaks. To date, there exists no efficient means of visualization and quantitative assessment of signal drift, correction of drift when present, and automated filtering of unstable spectral features, particularly across thousands of data files in population scale experiments. Herein, we report the development of a set of R-based scripts that allow for pre- and postprocessi

SUBMITTER: Watrous JD 

PROVIDER: S-EPMC5455767 | biostudies-literature | 2017 Feb

REPOSITORIES: biostudies-literature