omicsMIC:一个全面的基准分析平台,用于对基于质谱的Omics数据中的计算方法进行可靠的比较
Weiqiang Lin1, Jiadong Ji2, Kuan-Jui Su1
1Tulane Center for Biomedical Informatics and Genomics, Deming Department of Medicine, School of Medicine, Tulane University, New Orleans, LA 70112, USA.
bioRxiv : the preprint server for biology
|September 25, 2023
概括
在omics数据中缺失的值阻碍了生物标志物的发现. omicsMIC平台评估了28种归算方法,帮助研究人员选择基于质谱的最佳策略进行OMIC数据分析.
科学领域:
- 生物医学数据分析
- 计算生物学是一种计算生物学.
- 基于质谱学的奥米克学.
背景情况:
- 质谱学对于蛋白质学,脂质学和代谢学至关重要.
- 在omics数据中缺失的值对生物过程阐明和生物标记物识别构成重大挑战.
- 现有的归算方法缺乏全面的比较分析,使研究人员的选择变得复杂.
研究的目的:
- 开发一个平台来比较基于质谱的OMIC数据的归算方法.
- 为模拟和评估各种归算技术的性能提供一个框架.
- 为研究人员提供数据驱动的洞察力,以便选择适当的归算策略.
主要方法:
- 开发omicsMIC,一个用于omics数据归算的交互平台.
- 包括28种不同的归算方法进行综合评估.
- 实时可视化性能评估的归算结果.
主要成果:
- omicsMIC为模拟和评估归算方法提供了一个多功能框架.
- 该平台承认数据异质性和数据集特定属性.
- 为研究人员提供了在归算方法选择中进行知情决策的工具.
结论:
- omicsMIC是科学界在基于质谱学的omics研究中的一个有价值的工具.
- 通过解决缺失值,促进了可靠的数据分析.
- 提高生物标志物的全面识别和理解复杂的生物过程.
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