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首页> 美国卫生研究院文献>ACS AuthorChoice >Interactive VisualExploration of 3D Mass SpectrometryImaging Data Using Hierarchical Stochastic Neighbor Embedding RevealsSpatiomolecular Structures at Full Data Resolution
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Interactive VisualExploration of 3D Mass SpectrometryImaging Data Using Hierarchical Stochastic Neighbor Embedding RevealsSpatiomolecular Structures at Full Data Resolution

机译：互动视觉3D质谱探索显示使用分层随机邻居嵌入的成像数据完整数据分辨率下的空间分子结构

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摘要

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 theidentification of molecular ions that characterize these regions ofinterest; furthermore, through clearly separating measurement artifacts,the HSNE analysis exhibits a degree of robustness to measurement batcheffects, spatially correlated noise, and mass spectral misalignment.
机译：质谱成像（MSI）的技术进步已引起人们对3D MSI的日益增长的兴趣。但是，庞大的3D MSI数据集使其高效的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。分层随机邻居嵌入（HSNE）是旨在减少大型数据集的分层和多尺度表示的一种非线性降维技术，它是最近的一项发展，它能够分析数百万个数据点，并具有可管理的时间和存储复杂性。我们证明，HSNE可用于以完整的质谱和空间分辨率分析大型3D MSI数据集。为了对这项技术进行基准测试并证明其广泛的适用性，我们分析了许多可公开获得的3D MSI数据集，这些数据集来自各种生物系统并跨越了不同的质谱电离技术。我们证明，HSNE能够快速识别这些大型高维数据集中的感兴趣区域，并有助于鉴定表征这些区域的分子离子利益;此外，通过清楚地分离测量伪影，HSNE分析对测量批次显示出一定程度的鲁棒性效应，空间相关噪声和质谱失准。

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