Extended data analysis strategies for high resolution imaging MS: New methods to deal with extremely large image hyperspectral datasets
Extended data analysis strategies for high resolution imaging MS: New methods to deal with extremely large image hyperspectral datasets
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DOI：
10.1016/j.ijms.2006.11.014
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发表时间：
2007-02-01
影响因子：
1.8
通讯作者：
Heeren, Ron M. A.
中科院分区：
文献类型：
--
作者：
Klerk, Leendert A.;Broersen, Alexander;Heeren, Ron M. A.
用现代质谱成像技术产生的高光谱数据集尺寸很大，因此很难分析结果。需要无监督的统计技术来从这些数据集中提取相关信息，并将数据简化为可调查的概览。多元统计通常用于此目的。计算能力和计算机内存限制了可以用这些技术分析数据集的分辨率。我们介绍了使用的数据格式，能够有效地存储稀疏数据集的多元分析。这种格式的内存效率更高，因此它增加了可能的分辨率，同时减少了计算时间。三个多变量技术进行了比较，在两个不同的成像ToF-SIMS实验和一个LDI-ToF成像实验中获得的稀疏型数据和非稀疏数据。对于相同的多变量算法，使用不同的数据格式没有显著的定性差异。所有评价的多变量技术都可以应用于西姆斯和LDI成像数据集。主成分分析被证明是最快的选择，然而，使用VARIMAX优化的计算时间的小幅增加显着提高了分解质量。PARAFAC分析在分离不同的化学成分方面非常有效，但计算需要大量的时间，限制了其作为常规技术的使用。对于分析师来说，多变量分析结果的有效可视化与计算问题一样重要。为此，提出了一种新的可视化技术，将光谱载荷和空间分数结合到一个完整的数据立方体上的三维视图中。(C)2006 Elsevier B. V.保留所有权利。
The large size of the hyperspectral datasets that are produced with modem mass spectrometric imaging techniques makes it difficult to analyze the results. Unsupervised statistical techniques are needed to extract relevant information from these datasets and reduce the data into a surveyable overview. Multivariate statistics are commonly used for this purpose. Computational power and computer memory limit the resolution at which the datasets can be analyzed with these techniques. We introduce the use of a data format capable of efficiently storing sparse datasets for multivariate analysis. This format is more memory-efficient and therefore it increases the possible resolution together with a decrease of computation time. Three multivariate techniques are compared for both sparse-type data and non-sparse data acquired in two different imaging ToF-SIMS experiments and one LDI-ToF imaging experiment. There is no significant qualitative difference in the use of different data formats for the same multivariate algorithms. All evaluated multivariate techniques could be applied on both SIMS and the LDI imaging datasets. Principal component analysis is shown to be the fastest choice; however a small increase of computation time using a VARIMAX optimization increases the decomposition quality significantly. PARAFAC analysis is shown to be very effective in separating different chemical components but the calculations take a significant amount of time, limiting its use as a routine technique. An effective visualization of the results of the multivariate analysis is as important for the analyst as the computational issues. For this reason, a new technique for visualization is presented, combining both spectral loadings and spatial scores into one three-dimensional view on the complete datacube. (C) 2006 Elsevier B.V. All rights reserved.