挑选与峰值挑选:通过代谢学中的简单指标来评估色谱峰值质量
William Kumler1, Bryna J Hazelton2,3, Anitra E Ingalls4
1School of Oceanography, University of Washington, Seattle, WA, 98195, USA.
BMC bioinformatics
|October 27, 2023
概括
通过新的质量指标,LC-MS中的自动峰值采集得到了改进. 这些指标减少了假阳性,并提高了检测代谢学数据中的环境差异的能力.
科学领域:
- 代谢学 代谢学 代谢学
- 环境科学 环境科学
- 分析化学 分析化学
背景情况:
- 自动化液体染色学-质谱学 (LC-MS) 工作流被峰值采集阻碍,导致高错误发现率.
- 手动峰值评估耗时,并降低可重现性,特别是在杂的环境数据中,以及使用新的分离方法,如水友相互作用色谱 (HILIC).
研究的目的:
- 开发和验证LC-MS数据的强大,直观的峰值质量指标.
- 提高自动化数据分析在代谢学中的准确性和效率.
主要方法:
- 四个HILIC海洋学颗粒物代谢物数据集的手动标签.
- 从提取的离子染色图开发新的信号对噪声和钟曲线相似度指标.
- 使用这些指标来预测峰值质量的物流回归模型的构建.
主要成果:
- 新的高峰质量指标表现优于现有方法.
- 一个物流回归模型将假阳性从70-80%降低到1-5%,最小的过拟合.
- 在海洋代谢学数据中,高质量的峰值过使与深度相关的解释变异从10%增加到40%.
结论:
- 在LC-MS中峰值采集不佳限制了对环境差异的统计分析能力.
- 从原始数据中获得的简单,直观的指标提供了强大的高峰质量评估.
- 在使用精选数据集时,深度是海洋微生物代谢组变异性的重要因素.
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