EigenRF:一种改进的代谢基因正常化方法,在差异化代谢物的重要性排名上进行可重复性评估的得分
Chencheng Tang1,2, Dongfang Huang1, Xudong Xing1
1State Key Laboratory of Natural Medicines, China Pharmaceutical University, Nanjing 211198, China. mrxing_xudong@126.com.
Analytical methods : advancing methods and applications
|November 19, 2024
概括
新的 EigenRF 算法改善了代谢学数据的规范化,提高了差异代谢物的识别和可靠性,用于生物标志物发现. 它还提供了更好的可重复性评估来对这些重要的生物标志物进行排名.
科学领域:
- 代谢学 代谢学 代谢学
- 生物信息学是一种生物信息学.
- 生物标志物发现发现
背景情况:
- 代谢学研究依赖于确定生物标志物发现的差异性代谢物.
- 实验变异可能会影响代谢物识别的准确性和可重复性.
- 现有的规范化方法改善了分类,但需要进一步提高准确性和可重复性评估.
研究的目的:
- 引入EigenRF算法,以改善代谢学数据的正常化.
- 提高差异代谢物分析的分类能力和可重复性.
- 为差异化代谢产物的重要性排名提供可靠的评估指标.
主要方法:
- 开发了EigenRF算法,这是对EigenMS规范化方法的进步.
- 引入了局部一致性 (LC) 和整体差异 (OD) 评分,以评估代谢物重要性排名的可重复性.
- 在三个公开可用的代谢学数据集上验证了 EigenRF 方法.
主要成果:
- 与以前的方法相比,EigenRF证明了对差异性代谢物的增强分类能力.
- 该算法在识别和排名重要的差异代谢物时显示出更好的可重复性.
- LC和OD分数为评估排名可重复性提供了双重视角.
结论:
- 在代谢学研究中,EigenRF显著提高了差异化代谢物的可靠性.
- 该方法有助于探索复杂样本中生物变化的分子机制.
- 为了更广泛的可访问性,EigenRF算法作为R包提供.
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