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使用交互式动态光谱相似性伪彩对质谱图像进行快速可视化探索。

Fast visual exploration of mass spectrometry images with interactive dynamic spectral similarity pseudocoloring.

机构信息

International Research Training Group "Computational Methods for the Analysis of the Diversity and Dynamics of Genomes", Bielefeld University, 33615, Bielefeld, Germany.

Biodata Mining Group, Faculty of Technology, Bielefeld University, 33615, Bielefeld, Germany.

出版信息

Sci Rep. 2021 Feb 25;11(1):4606. doi: 10.1038/s41598-021-84049-4.

Abstract

Mass Spectrometry Imaging (MSI) is an established and still evolving technique for the spatial analysis of molecular co-location in biological samples. Nowadays, MSI is expanding into new domains such as clinical pathology. In order to increase the value of MSI data, software for visual analysis is required that is intuitive and technique independent. Here, we present QUIMBI (QUIck exploration tool for Multivariate BioImages) a new tool for the visual analysis of MSI data. QUIMBI is an interactive visual exploration tool that provides the user with a convenient and straightforward visual exploration of morphological and spectral features of MSI data. To improve the overall quality of MSI data by reducing non-tissue specific signals and to ensure optimal compatibility with QUIMBI, the tool is combined with the new pre-processing tool ProViM (Processing for Visualization and multivariate analysis of MSI Data), presented in this work. The features of the proposed visual analysis approach for MSI data analysis are demonstrated with two use cases. The results show that the use of ProViM and QUIMBI not only provides a new fast and intuitive visual analysis, but also allows the detection of new co-location patterns in MSI data that are difficult to find with other methods.

摘要

质谱成像(MSI)是一种成熟且不断发展的技术,用于对生物样本中分子共定位进行空间分析。如今,MSI 正在扩展到临床病理学等新领域。为了增加 MSI 数据的价值,需要具有直观和独立于技术的视觉分析软件。在这里,我们介绍 QUIMBI(用于多维生物图像的快速探索工具),这是一种用于 MSI 数据的可视化分析的新工具。QUIMBI 是一种交互式视觉探索工具,为用户提供了一种方便、直接的 MSI 数据形态和光谱特征的视觉探索方式。为了通过减少非组织特异性信号来提高 MSI 数据的整体质量,并确保与 QUIMBI 的最佳兼容性,该工具与本工作中介绍的新预处理工具 ProViM(用于 MSI 数据的可视化和多元分析的处理)相结合。使用两个用例演示了所提出的 MSI 数据分析的可视化分析方法的特点。结果表明,使用 ProViM 和 QUIMBI 不仅提供了新的快速直观的视觉分析,而且还可以检测到其他方法难以发现的 MSI 数据中的新共定位模式。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/b9d1/7907387/f489a4947ab5/41598_2021_84049_Fig1_HTML.jpg

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