中文摘要：

摘要：谱峰对齐是基于核磁共振的代谢组学数据预处理过程中的一个重要环节，谱峰对齐效果直接影响后续的多变量统计分析。提出了一种基于高斯平滑的谱峰对齐算法（GPA）。算法通过调节高斯卷积函数的窗口大小，实现波谱信号的多尺度平滑，进而由粗到细、逐步实现波谱信号的谱峰对齐。真实的核磁共振波谱实验结果表明：GPA算法可以快速准确地实现谱峰对齐，且对齐后的波谱信号在平均相似度、后续统计模型的解释能力等综合性能上的表现明显优于相关优化解缠（COW）和多尺度谱峰对齐（MSPA）等常用谱峰对齐算法。

英文摘要：

Peak alignment is an important step during metabolomics data pretreatment process based on nucle- ar magnetic resonance （NMR） and its effect plays a direct role on subsequent multivariate statistical analysis. A peak alignment algorithm based on Gaussian smoothing （GPA） is presented. Spectrum signals can be smoothed on multiple scales by adjusting sizes of the windows of Gaussian convolution function. And peak alignment can be re- alized step by step from coarse to fine. The true experiment results of NMR spectrum show that peak alignment can be realized quickly and accurately by GPA algorithm. Comparing with common peak alignment algorithms such as correlation optimized warping （COW） and multi-scale peak alignment （MSPA）, the aligned spectrum signals are su- perior at integrated performances such as average similarity and explanation performances of subsequent statistical models obviously.