ArticleComputational and mathematical methods in medicine2013
Comparative analysis of mass spectral similarity measures on peak alignment for comprehensive two-dimensional gas chromatography mass spectrometry.
Article in Computational and mathematical methods in medicine, 2013. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 10 papers.
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Who cites it
10 citing papers in PubMed.
- Article
- Coherent Point Drift Peak Alignment Algorithms Using Distance and Similarity Measures for Two-Dimensional Gas Chromatography Mass Spectrometry Data.Journal of chemometrics · 2020Article
- Deep metabolome: Applications of deep learning in metabolomics.Computational and structural biotechnology journal · 2020Review
- Metrics for evaluating the stability and reproducibility of mass spectra.Scientific reports · 2019Article
- Data-driven approach for the prediction and interpretation of core-electron loss spectroscopy.Scientific reports · 2018Article
- Refinement of a Methodology for Untargeted Detection of Serum Albumin Adducts in Human Populations.Chemical research in toxicology · 2017Article
- DISMS2: A flexible algorithm for direct proteome- wide distance calculation of LC-MS/MS runs.BMC bioinformatics · 2017Article
- SWATH Mass Spectrometry Performance Using Extended Peptide MS/MS Assay Libraries.Molecular & cellular proteomics : MCP · 2016Article
- ppcor: An R Package for a Fast Calculation to Semi-partial Correlation Coefficients.Communications for statistical applications and methods · 2015Article
- Improving the quality of biomarker candidates in untargeted metabolomics via peak table-based alignment of comprehensive two-dimensional gas chromatography-mass spectrometry data.Journal of chromatography. A · 2015Article
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2 authors.
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Abstract
Peak alignment is a critical procedure in mass spectrometry-based biomarker discovery in metabolomics. One of peak alignment approaches to comprehensive two-dimensional gas chromatography mass spectrometry (GC×GC-MS) data is peak matching-based alignment. A key to the peak matching-based alignment is the calculation of mass spectral similarity scores. Various mass spectral similarity measures have been developed mainly for compound identification, but the effect of these spectral similarity measures on the performance of peak matching-based alignment still remains unknown. Therefore, we selected five mass spectral similarity measures, cosine correlation, Pearson's correlation, Spearman's correlation, partial correlation, and part correlation, and examined their effects on peak alignment using two sets of experimental GC×GC-MS data. The results show that the spectral similarity measure does not affect the alignment accuracy significantly in analysis of data from less complex samples, while the partial correlation performs much better than other spectral similarity measures when analyzing experimental data acquired from complex biological samples.
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