scholarly journals Coherent point drift peak alignment algorithms using distance and similarity measures for two‐dimensional gas chromatography mass spectrometry data

2020 ◽  
Vol 34 (8) ◽  
Author(s):  
Zeyu Li ◽  
Seongho Kim ◽  
Sikai Zhong ◽  
Zichun Zhong ◽  
Ikuko Kato ◽  
...  
Keyword(s):  
Mass Spectrometry ◽  
Gas Chromatography ◽  
Similarity Measures ◽  
Mass Spectrometry Data ◽  
Gas Chromatography Mass Spectrometry ◽  
Two Dimensional ◽  
Peak Alignment ◽  
Chromatography Mass Spectrometry ◽  
Alignment Algorithms ◽  
Coherent Point Drift

scholarly journals Comparative Analysis of Mass Spectral Similarity Measures on Peak Alignment for Comprehensive Two-Dimensional Gas Chromatography Mass Spectrometry

2013 ◽  
Vol 2013 ◽  
pp. 1-12 ◽  
Author(s):  
Seongho Kim ◽  
Xiang Zhang
Keyword(s):  
Mass Spectrometry ◽  
Gas Chromatography ◽  
Partial Correlation ◽  
Similarity Measures ◽  
Gas Chromatography Mass Spectrometry ◽  
Two Dimensional ◽  
Peak Alignment ◽  
Spectral Similarity ◽  
Mass Spectral ◽  
Chromatography Mass Spectrometry

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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