ArticleAnalytical chemistry2010
A statistically rigorous test for the identification of parent-fragment pairs in LC-MS datasets.
Article in Analytical chemistry, 2010. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 19 papers.
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Who cites it
19 citing papers in PubMed.
- Improving the Detection of Analyte Degeneracies in Untargeted Liquid Chromatography-Tandem Mass Spectrometry Data.Analytical chemistry · 2025Article
- Picky with peakpicking: assessing chromatographic peak quality with simple metrics in metabolomics.BMC bioinformatics · 2023Article
- Cumulative Neutral Loss Model for Fragment Deconvolution in Electrospray Ionization High-Resolution Mass Spectrometry Data.Analytical chemistry · 2023Article
- Critical review on data processing algorithms in non-target screening: challenges and opportunities to improve result comparability.Analytical and bioanalytical chemistry · 2023Review
- R-MetaboList 2: A Flexible Tool for Metabolite Annotation from High-Resolution Data-Independent Acquisition Mass Spectrometry Analysis.Metabolites · 2019Article
- Annotation: A Computational Solution for Streamlining Metabolomics Analysis.Analytical chemistry · 2018Review
- Defining and Detecting Complex Peak Relationships in Mass Spectral Data: The Mz.unity Algorithm.Analytical chemistry · 2016Article
- A roadmap for the XCMS family of software solutions in metabolomics.Current opinion in chemical biology · 2016Review
- Using fragmentation trees and mass spectral trees for identifying unknown compounds in metabolomics.Trends in analytical chemistry : TRAC · 2015Article
- Joint Analysis of Dependent Features within Compound Spectra Can Improve Detection of Differential Features.Frontiers in bioengineering and biotechnology · 2015Article
- Orders of magnitude extension of the effective dynamic range of TDC-based TOFMS data through maximum likelihood estimation.Journal of the American Society for Mass Spectrometry · 2014Article
- MetAssign: probabilistic annotation of metabolites from LC-MS data using a Bayesian clustering approach.Bioinformatics (Oxford, England) · 2014Article
- Global metabolic profiling of animal and human tissues via UPLC-MS.Nature protocols · 2013Article
- Prospects for a statistical theory of LC/TOFMS data.Journal of the American Society for Mass Spectrometry · 2012Article
- LC-MS-based metabolomics.Molecular bioSystems · 2012Review
- Metabolite identification and quantitation in LC-MS/MS-based metabolomics.Trends in analytical chemistry : TRAC · 2012Article
- CAMERA: an integrated strategy for compound spectra extraction and annotation of liquid chromatography/mass spectrometry data sets.Analytical chemistry · 2012Article
- Does the mass spectrometer define the marker? A comparison of global metabolite profiling data generated simultaneously via UPLC-MS on two different mass spectrometers.Analytical chemistry · 2010Article
- Construction of confidence regions for isotopic abundance patterns in LC/MS data sets for rigorous determination of molecular formulas.Analytical chemistry · 2010Article
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4 authors.
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Abstract
Untargeted global metabolic profiling by liquid chromatography-mass spectrometry generates numerous signals that are due to unknown compounds and whose identification forms an important challenge. The analysis of metabolite fragmentation patterns, following collision-induced dissociation, provides a valuable tool for identification, but can be severely impeded by close chromatographic coelution of distinct metabolites. We propose a new algorithm for identifying related parent-fragment pairs and for distinguishing these from signals due to unrelated compounds. Unlike existing methods, our approach addresses the problem by means of a hypothesis test that is based on the distribution of the recorded ion counts, and thereby provides a statistically rigorous measure of the uncertainty involved in the classification problem. Because of technological constraints, the test is of primary use at low and intermediate ion counts, above which detector saturation causes substantial bias to the recorded ion count. The validity of the test is demonstrated through its application to pairs of coeluting isotopologues and to known parent-fragment pairs, which results in test statistics consistent with the null distribution. The performance of the test is compared with a commonly used Pearson correlation approach and found to be considerably better (e.g., false positive rate of 6.25%, compared with a value of 50% for the correlation for perfectly coeluting ions). Because the algorithm may be used for the analysis of high-mass compounds in addition to metabolic data, we expect it to facilitate the analysis of fragmentation patterns for a wide range of analytical problems.
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