ArticleJournal of bioinformatics and computational biology2016
Global peak alignment for comprehensive two-dimensional gas chromatography mass spectrometry using point matching algorithms.
Article in Journal of bioinformatics and computational biology, 2016. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 papers.
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Who cites it
3 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
- Coherent Point Drift Peak Alignment Algorithms Using Distance and Similarity Measures for Two-Dimensional Gas Chromatography Mass Spectrometry Data.Journal of chemometrics · 2020Article
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5 authors.
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Abstract
Comprehensive two-dimensional gas chromatography coupled with mass spectrometry (GC[Formula: see text][Formula: see text][Formula: see text]GC-MS) has been used to analyze multiple samples in a metabolomics study. However, due to some uncontrollable experimental conditions, such as the differences in temperature or pressure, matrix effects on samples and stationary phase degradation, there is always a shift of retention times in the two GC columns between samples. In order to correct the retention time shifts in GC[Formula: see text][Formula: see text][Formula: see text]GC-MS, the peak alignment is a crucial data analysis step to recognize the peaks generated by the same metabolite in different samples. Two approaches have been developed for GC[Formula: see text][Formula: see text][Formula: see text]GC-MS data alignment: profile alignment and peak matching alignment. However, these existing alignment methods are all based on a local alignment, resulting that a peak may not be correctly aligned in a dense chromatographic region where many peaks are present in a small region. False alignment will result in false discovery in the downstream statistical analysis. We, therefore, develop a global comparison-based peak alignment method using point matching algorithm (PMA-PA) for both homogeneous and heterogeneous data. The developed algorithm PMA-PA first extracts feature points (peaks) in the chromatography and then searches globally the matching peaks in the consecutive chromatography by adopting the projection of rigid and nonrigid transformation. PMA-PA is further applied to two real experimental data sets, showing that PMA-PA is a promising peak alignment algorithm for both homogenous and heterogeneous data in terms of [Formula: see text]1 score, although it uses only peak location information.
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Registered trials
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