使用 SpecXplore 交互式仪表板进行量身定制的质谱数据探索
Kevin Mildau1,2,3, Henry Ehlers4, Ian Oesterle3,5,6
1Department of Analytical Chemistry, University of Vienna, 1090 Vienna, Austria.
Analytical chemistry
|April 2, 2024
概括
SpecXplore是一个Python仪表板,它增强了用于非目标代谢学的质谱数据探索. 它帮助研究人员通过提供交互式可视化和优先考虑光谱数据来生成感兴趣的化合物的结构假设.
科学领域:
- 计算代谢学是指计算代谢学.
- 生物信息学是一种生物信息学.
- 分析化学是一种分析化学.
背景情况:
- 非定位代谢学提供了全面的小分子分析,但面临着低注释率和抽象光谱数据的挑战.
- 手动注释和确认in-silico预测仍然至关重要,尽管代谢学中的计算进步.
- 探索性数据分析对于管理大型光谱数据集至关重要,可以提供概述,优先级和结构性假设生成.
研究的目的:
- 介绍specXplore,一个用于流体质谱数据探索的交互式Python仪表板.
- 通过互动和互补的可视化来促进质谱相似度矩阵的探索.
- 帮助研究人员从复杂的光谱数据中对感兴趣的化合物产生结构假设.
主要方法:
- 开发specXplore,一个交互式Python仪表板,使用2Dt分布式随机邻居嵌入用于数据可视化.
- 整合互补的可视化,包括部分网络图纸,相似性热图和碎片化概述图.
- 实施ms2deepscore用于对对的光谱相似性计算,可调节值和连接设置以实现灵活的勘探.
主要成果:
- SpecXplore提供了一个交互式平台,用于探索质谱相似度矩阵.
- 仪表板通过链接的可视化来促进本地连接探索.
- 该工具允许动态调整相似度值和连接参数,提高数据探索的灵活性.
结论:
- SpecXplore提供了一种新的方法,用于在代谢学中进行质谱数据探索.
- 交互式可视化和灵活的设置有助于生成用于化合物识别的假设.
- SpecXplore有可能成为研究人员分析复杂光谱数据集的完整工具.
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