摘要
从三线性分解算法对液相色谱-质谱联用仪(LC-MS)多样本测定数据分辨的适用性角度入手,探讨了双线性和三线性分解算法的实际应用效果、存在的问题及其解决方案。本文选择含有低丰度肽段和高干扰背景信号的代表性测定数据进行测试。结果表明,双线性方法不具有分辨唯一性,不能分离LC-MS多样品测定数据存在的背景干扰,从而不适用于低丰度肽段问题的分析。常规的三线性分解算法难以满足质谱信号具有的数学特性———稀疏性,分辨结果并不完全可靠。本文提出了具有非负约束的交替三线性分解(non-negative alternating trilineardecomposition,NNATLD)新算法用于LC-MS多样本测定数据分辨及数学分离,能够很好地适应质谱的数学特性,且具有计算资源节约和收敛速度快等特点。
The applicability of different trilinear decomposition algorithm to LC-MS data measured from multiple samples is discussed in this paper.An actual LC-MS data set contained a low abundance peptide was adopted to make a test for these algorithms.The bilinear method was not able to handle this type of low abundance situations,and made a mathematical separation as expected.It is found out that the famous trilinear decomposition algorithm could not be used in the LC-MS data directly.The most probable reason is the sparsity property of the pure MS spectra,which means they have positive response values at some m/z coordinates where the ions emerged and zero values elsewhere.A novel algorithm named NNATLD（non-negative alternating trilinear decomposition） has been designed by the present authors to make an effective trilinear decomposition for the three-way data set constructed by LC-MS data.The new algorithm adapts the property of MS spectra,saves the computing resources,and converges fast.
出处
《色谱》
CAS
CSCD
北大核心
2013年第6期550-555,共6页
Chinese Journal of Chromatography
基金
国家自然科学基金项目(21175041)
国家科技部"973"计划课题(2012CB910602)
国家自然科学基金创新研究群体科学基金项目(21221003)
关键词
液相色谱-质谱
多样本
定量蛋白质组学
三线性分解
非负交替三线性分解
算法比较
数学分离
liquid chromatography-mass spectrometry（LC-MS）
multiple samples
quantitative proteomics
trilinear decomposition
non-negative alternating trilinear decomposition（NNATLD）
algorithm comparison
mathematical separation
