Accessible and reproducible mass spectrometry imaging data analysis in Galaxy
Accessible and reproducible mass spectrometry imaging data analysis in Galaxy
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DOI：
10.1093/gigascience/giz143
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发表时间：
2019-12-01
期刊：
影响因子：
9.2
通讯作者：
Schilling, Oliver
中科院分区：
文献类型：
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作者：
Foell, Melanie Christine;Moritz, Lennart;Schilling, Oliver
背景：质谱仪成像在生物和翻译研究中的应用越来越多，因为它能够确定一个样品中数百种分析物的空间分布。由于处于蛋白质组学/代谢组学和成像的交界处，所获得的数据集既大又复杂，通常使用专有软件或内部脚本进行分析，这阻碍了重复性。实现可重复数据分析的开源软件解决方案通常需要编程技能，因此许多质谱学成像(MSI)研究人员无法访问。发现：我们在Galaxy框架中集成了18个专用的质谱学成像工具，以实现可访问、可重现和透明的数据分析。我们的工具基于Cardinal、MALDIquant和SCRICKIT-IMAGE，支持所有主要的MSI分析步骤，如质量控制、可视化、预处理、统计分析和图像联合配准。此外，我们为蛋白质组学和代谢组学中的用例创建了动手培训材料。为了证明我们工具的实用性，我们重新分析了一个公开可用的N-连锁葡聚糖成像数据集。通过在线提供整个分析历史，我们重点介绍了Galaxy框架如何促进透明和可重复的研究。结论：Galaxy框架已成为一个强大的分析平台，便于使用和访问，并具有高度的再现性和透明度。
Background: Mass spectrometry imaging is increasingly used in biological and translational research because it has the ability to determine the spatial distribution of hundreds of analytes in a sample. Being at the interface of proteomics/metabolomics and imaging, the acquired datasets are large and complex and often analyzed with proprietary software or in-house scripts, which hinders reproducibility. Open source software solutions that enable reproducible data analysis often require programming skills and are therefore not accessible to many mass spectrometry imaging (MSI) researchers. Findings: We have integrated 18 dedicated mass spectrometry imaging tools into the Galaxy framework to allow accessible, reproducible, and transparent data analysis. Our tools are based on Cardinal, MALDIquant, and scikit-image and enable all major MSI analysis steps such as quality control, visualization, preprocessing, statistical analysis, and image co-registration. Furthermore, we created hands-on training material for use cases in proteomics and metabolomics. To demonstrate the utility of our tools, we re-analyzed a publicly available N-linked glycan imaging dataset. By providing the entire analysis history online, we highlight how the Galaxy framework fosters transparent and reproducible research. Conclusion: The Galaxy framework has emerged as a powerful analysis platform for the analysis of MSI data with ease of use and access, together with high levels of reproducibility and transparency.