G-Aligner: a graph-based feature alignment method for untargeted LC-MS-based metabolomics.

ABSTRACT:

Background

Liquid chromatography-mass spectrometry is widely used in untargeted metabolomics for composition profiling. In multi-run analysis scenarios, features of each run are aligned into consensus features by feature alignment algorithms to observe the intensity variations across runs. However, most of the existing feature alignment methods focus more on accurate retention time correction, while underestimating the importance of feature matching. None of the existing methods can comprehensively consider feature correspondences among all runs and achieve optimal matching.

Results

To comprehensively analyze feature correspondences among runs, we propose G-Aligner, a graph-based feature alignment method for untargeted LC-MS data. In the feature matching stage, G-Aligner tre

SUBMITTER: Wang R 

PROVIDER: S-EPMC10644574 | biostudies-literature | 2023 Nov

REPOSITORIES: biostudies-literature