ArticleJournal of chemometrics2020
Coherent Point Drift Peak Alignment Algorithms Using Distance and Similarity Measures for Two-Dimensional Gas Chromatography Mass Spectrometry Data.
Article in Journal of chemometrics, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers.
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Who cites it
2 citing papers in PubMed.
- Current Application of Advancing Spectroscopy Techniques in Food Analysis: Data Handling with Chemometric Approaches.Foods (Basel, Switzerland) · 2023Review
- DA_2DCHROM - a data alignment tool for applications on real GC × GC-TOF samples.Analytical and bioanalytical chemistry · 2023Article
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Authors and funding
6 authors.
Funding
Abstract
The peak alignment is a vital preprocessing step before downstream analysis, such as biomarker discovery and pathway analysis, for two-dimensional gas chromatography mass spectrometry (2DGCMS)-based metabolomics data. Due to uncontrollable experimental conditions, e.g., the differences in temperature or pressure, matrix effects on samples, and stationary phase degradation, a shift of retention times among samples inevitably occurs during 2DGCMS experiments, making it difficult to align peaks. Various peak alignment algorithms have been developed to correct retention time shifts for homogeneous, heterogeneous or both type of mass spectrometry data. However, almost all existing algorithms have been focused on a local alignment and are suffering from low accuracy especially when aligning dense biological data with many peaks. We have developed four global peak alignment (GPA) algorithms using coherent point drift (CPD) point matching algorithms: retention time-based CPD-GPA (RT),
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