Interactive Visual Exploration of 3D Mass Spectrometry Imaging Data Using Hierarchical Stochastic Neighbor Embedding Reveals Spatiomolecular Structures at Full Data Resolution.
Interactive Visual Exploration of 3D Mass Spectrometry Imaging Data Using Hierarchical Stochastic Neighbor Embedding Reveals Spatiomolecular Structures at Full Data Resolution.
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DOI：
10.1021/acs.jproteome.7b00725
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发表时间：
2018-03-02
影响因子：
4.4
通讯作者：
Lelieveldt BPF
中科院分区：
文献类型：
--
作者：
Abdelmoula WM;Pezzotti N;Hölt T;Dijkstra J;Vilanova A;McDonnell LA;Lelieveldt BPF
质谱成像（MSI）技术的进步促进了人们对3D MSI的兴趣。然而，大尺寸的三维MSI数据集使得它们的有效分析和可视化以及信息分子模式的识别在计算上具有挑战性。分层随机邻域嵌入（HSNE）是一种非线性降维技术，旨在寻找大型数据集的分层和多尺度表示，是最近的一项发展，可以分析数百万个数据点，具有可管理的时间和内存复杂性。我们证明了HSNE可以在全质谱和空间分辨率下分析大型3D MSI数据集。为了对该技术进行基准测试，并证明其广泛的适用性，我们分析了许多公开可用的3D MSI数据集，这些数据集记录于各种生物系统中，跨越不同的质谱电离技术。我们证明，HSNE能够在这些大型高维数据集中快速识别感兴趣的区域，并有助于识别表征这些感兴趣区域的分子离子；此外，通过清晰地分离测量伪像，hse分析对测量批效应、空间相关噪声和质谱偏差具有一定程度的鲁棒性。
Technological advances in mass spectrometry imaging (MSI) have contributed to growing interest in 3D MSI. However, the large size of 3D MSI data sets has made their efficient analysis and visualization and the identification of informative molecular patterns computationally challenging. Hierarchical stochastic neighbor embedding (HSNE), a nonlinear dimensionality reduction technique that aims at finding hierarchical and multiscale representations of large data sets, is a recent development that enables the analysis of millions of data points, with manageable time and memory complexities. We demonstrate that HSNE can be used to analyze large 3D MSI data sets at full mass spectral and spatial resolution. To benchmark the technique as well as demonstrate its broad applicability, we have analyzed a number of publicly available 3D MSI data sets, recorded from various biological systems and spanning different mass-spectrometry ionization techniques. We demonstrate that HSNE is able to rapidly identify regions of interest within these large high-dimensionality data sets as well as aid the identification of molecular ions that characterize these regions of interest; furthermore, through clearly separating measurement artifacts, the HSNE analysis exhibits a degree of robustness to measurement batch effects, spatially correlated noise, and mass spectral misalignment.
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影响因子：
3.7
作者：
Jones EA;van Remoortere A;van Zeijl RJ;Hogendoorn PC;Bovée JV;Deelder AM;McDonnell LA
通讯作者：
McDonnell LA
影响因子：
48
作者：
Andersson, Malin;Groseclose, M. Reid;Caprioli, Richard M.
通讯作者：
Caprioli, Richard M.
影响因子：
48
作者：
Giesen, Charlotte;Wang, Hao A. O.;Bodenmiller, Bernd
通讯作者：
Bodenmiller, Bernd
Rho相关激酶1/2的抑制作用减弱了鼠胃癌的肿瘤生长。
影响因子：
4.8
作者：
Hinsenkamp, Isabel;Schulz, Sandra;Roscher, Mareike;Suhr, Anne-Maria;Meyer, Bjoern;Munteanu, Bogdan;Fuchser, Jens;Schoenberg, Stefan O.;Ebert, Matthias P. A.;Waengler, Bjoern;Hopf, Carsten;Burgermeister, Elke
通讯作者：
Burgermeister, Elke
影响因子：
3
作者：
Ji S
通讯作者：
Ji S