局部不对称高斯适配算法用于增强的峰值检测液体染色学-高分辨率质谱数据
Shengsi Zou1, Qingxiao Cui1, Jinyue Liu1
1School of Chemistry and Molecular Engineering & Research Center of Analysis and Test, East China University of Science and Technology, Shanghai 200237, China.
这项研究引入了一种新的局部不对称高斯拟合 (LAGF) 算法,用于在液态染色学质谱学 (LC-MS) 中增强特征检测. LAGF提高了用于代谢分析的化合物识别的准确性和效率.
科学领域:
- 分析化学 分析化学
- 代谢学 代谢学 代谢学
- 生物技术是生物技术.
背景情况:
- 特性检测对于液态染色体质谱 (LC-MS) 数据预处理至关重要.
- 现有的方法经常受到复杂的参数调整和高假阳性率的影响,使复杂的化合物识别复杂化.
研究的目的:
- 引入一种新的算法,局部不对称高斯拟合 (LAGF),用于在LC-MS中改进峰值检测.
- 为了提高特征检测效率和准确性在代谢学工作流程.
主要方法:
- 开发了局部不对称高斯拟合 (LAGF) 算法用于峰值检测.
- 集成的LAGF采用"数据点 bin"方法,用于使用1Da bin的提取离子染色图 (EIC) 的提取.
- 为自适应性峰值建模自动确定峰值参数 (中心,高度,标准偏差).
- 过的特征使用0.5.5的适合度值.
主要成果:
- 与传统工具相比,LAGF在确定系数 (R2) 和峰值区域的相对标准偏差方面表现优越.
- 该算法在分析不同LC模式的标准混合物和血清样本方面表现出高效率和准确性.
- 通过1Da数据点分类减少了计算时间,从而实现了高效的批量代谢学分析.
结论:
- 对于LC-MS中的特征检测,LAGF提供了一个强大而高效的解决方案,最大限度地降低参数复杂性和错误阳性.
- 有互动界面的LAGF的开源可用性有助于在有针对性和非有针对性的LC-MS分析中采用它.
- LAGF提高了化合物识别的准确性和在代谢学中的整体数据处理效率.
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