Department of Biostatistics and Bioinformatics, Rollins School of Public Health, Emory University, Atlanta, GA, USA.
BMC Bioinformatics. 2010 Nov 12;11:559. doi: 10.1186/1471-2105-11-559.
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.
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.
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: http://www.sph.emory.edu/apLCMS/.
液相色谱-质谱联用（LC-MS）是分析复杂生物样本中代谢物的主要技术之一。峰建模是 LC-MS 数据预处理的关键组成部分之一。
为了定量具有高噪声水平的不对称峰，我们使用双高斯函数开发了一种估计程序。此外，为了准确地定量部分重叠的峰，我们使用双高斯混合模型结合统计模型选择开发了一种解卷积方法。
通过广泛的模拟和真实数据，我们证明了双高斯混合模型在峰定量和解卷积方面优于高斯混合模型和核平滑法与信号求和相结合的方法。该方法在 R 包 apLCMS 中实现：http://www.sph.emory.edu/apLCMS/。
