Comparative Analysis of Mass Spectral Similarity Measures on Peak Alignment for Comprehensive GCxGC-MS
News Oct 30, 2013
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.
The article is published online in Computational and Mathematical Methods in Medicine and is free to access.
Chinese researchers have developed interfacially polymerized porous polymer particles for low- abundance glycopeptide separation. These polymer particles - with hydrophilic-hydrophobic heterostructured nanopores - can separate low-abundance glycopeptides from complex biological samples with high-abundance background molecules efficiently.