A geometric approach for the alignment of liquid chromatography -: mass spectrometry data
A geometric approach for the alignment of liquid chromatography -: mass spectrometry data
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DOI：
10.1093/bioinformatics/btm209
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发表时间：
2007-07-01
期刊：
影响因子：
5.8
通讯作者：
Reinert, Knut
中科院分区：
文献类型：
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作者：
Lange, Eva;Groepl, Clemens;Reinert, Knut
动机：液相色谱-质谱联用技术（LC-MS）和液相色谱-质谱联用技术（LC-MS/ MS）已成为分析复杂蛋白质组学样品的重要工具。典型工作流程中的一个重要步骤是组合多个LC-MS实验的结果，以提高所获得测量结果的置信度或比较不同样品的结果。为此，需要估计数据集之间的适当映射或对齐。对齐校正的质量和洗脱时间的变化，这是目前在所有的质谱experiments.Results：我们提出了一种新的算法来对齐LC-MS样品，并匹配相应的离子物种跨样品。我们的算法使用基于姿态聚类的几何技术在两个数据集之间匹配地标信号。质量和保留时间的变化通过根据匹配地标估计的仿射去扭曲函数来校正。我们在一个算法中使用成对去扭曲来对齐多个样本。我们表明，我们的姿势聚类方法是快速和可靠的，与以前的方法相比。它在存在噪声的情况下具有鲁棒性，并且能够准确地对准仅具有少数常见离子种类的样品。此外，我们可以很容易地处理不同类型的LC-MS数据，并采用我们的算法，以新的质谱技术。
Motivation: Liquid chromatography coupled to mass spectrometry (LC-MS) and combined with tandem mass spectrometry (LC-MS/ MS) have become a prominent tool for the analysis of complex proteomic samples. An important step in a typical workflow is the combination of results from multiple LC-MS experiments to improve confidence in the obtained measurements or to compare results from different samples. To do so, a suitable mapping or alignment between the data sets needs to be estimated. The alignment has to correct for variations in mass and elution time which are present in all mass spectrometry experiments.Results: We propose a novel algorithm to align LC-MS samples and to match corresponding ion species across samples. Our algorithm matches landmark signals between two data sets using a geometric technique based on pose clustering. Variations in mass and retention time are corrected by an affine dewarping function estimated from matched landmarks. We use the pairwise dewarping in an algorithm for aligning multiple samples. We show that our pose clustering approach is fast and reliable as compared to previous approaches. It is robust in the presence of noise and able to accurately align samples with only few common ion species. In addition, we can easily handle different kinds of LC-MS data and adopt our algorithm to new mass spectrometry technologies.