Quantification and deconvolution of asymmetric LC-MS peaks using the bi-Gaussian mixture model and statistical model selection

• Main Authors: Peng Hesen, Yu Tianwei
• Format: Article
• Language: English
• Published: BMC 2010-11-01
• Series: BMC Bioinformatics
• Online Access: http://www.biomedcentral.com/1471-2105/11/559

<p>Abstract</p> <p>Background</p> <p>Liquid chromatography-mass spectrometry (LC-MS) is one of the major techniques for the quantification of metabolites in complex biological samples. Peak modeling is one of the key components in LC-MS data pre-processing.</p> <p>Results</p> <p>To quantify asymmetric peaks with high noise level, we developed an estimation procedure using the bi-Gaussian function. In addition, to accurately quantify partially overlapping peaks, we developed a deconvolution method using the bi-Gaussian mixture model combined with statistical model selection.</p> <p>Conclusions</p> <p>Using extensive simulations and real data, we demonstrated the advantage of the bi-Gaussian mixture model over the Gaussian mixture model and the method of kernel smoothing combined with signal summation in peak quantification and deconvolution. The method is implemented in the R package apLCMS: <url>http://www.sph.emory.edu/apLCMS/</url>.</p>