MeTEor:一个R Shiny应用程序,用于探索纵向代谢学数据
Gordon Grabert1,2, Daniel Dehncke1,2, Tushar More2,3
1Division Data Science in Biomedicine, Peter L. Reichertz Institute for Medical Informatics of Technische Universität Braunschweig and Hannover Medical School, Braunschweig, Lower Saxony 38106, Germany.
Bioinformatics advances
|December 11, 2024
概括
研究人员现在可以很容易地使用METABOLITE轨迹探索器 (MeTEor),一个R Shiny应用程序,随着时间的推移分析纵向代谢学数据. 这个工具简化了复杂的数据可视化和统计分析,以获得生物学见解.
科学领域:
- 代谢学 代谢学 代谢学
- 系统生物学 系统生物学
- 生物信息学是一种生物信息学.
背景情况:
- 纵向的奥米克数据,特别是代谢学数据,越来越多,提供了对生物过程的深入洞察.
- 分析时间过程代谢学数据给研究人员带来了重大的统计和可视化挑战.
- 了解动态生物系统反应需要有效的工具来进行纵向数据解释.
研究的目的:
- 开发一种可访问的工具,用于对纵向代谢学数据的探索性分析.
- 为研究人员,无论编程专业知识,提供分析时间序列的数据的能力.
- 利用时间代谢学数据,促进对生物系统动态的理解.
主要方法:
- 开发MEtabolite轨迹探索器 (MeTEor) 作为一个R闪亮的应用程序.
- 在MeTEor.中整合一套全面的统计分析方法套件.
- 复制COVID-19患者代谢学数据分析以证明MeTEor的功能.
主要成果:
- MeTEor提供了一个用户友好的界面,用于探索纵向代谢学数据.
- 该应用程序提供了强大的统计方法来分析代谢物的时间变化.
- 通过分析COVID-19队列数据集,成功展示了MeTEor的实用性.
结论:
- MeTEor为更广泛的研究社区民主化了纵向代谢学数据的分析.
- R Shiny应用程序增强了从时间序列代谢学研究中获得生物见解的能力.
- 对于研究动态生物系统的研究人员来说,MeTEor是一个有价值的,易于使用的工具.
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