简化表型分类和突出特征候选者:非向离子运动光谱-质谱 (IMS-MS) 数据的选方法
Jessie R Chappel1, Kaylie I Kirkwood-Donelson2, James N Dodds3
1Bioinformatics Research Center, Department of Biological Sciences, North Carolina State University, Raleigh, North Carolina 27606, United States.
Analytical chemistry
|September 18, 2024
概括
这项研究引入了一种用于非目标分析 (NTA) 的快速选方法,使用汇总的离子移动性谱学-质谱学 (IMS-MS) 光谱. 这种方法通过有效地识别复杂数据集中的关键分子特征来提高样本分类的准确性.
科学领域:
- 分析化学 分析化学
- 生物分子分析
- 数据科学数据科学数据科学
背景情况:
- 非目标分析 (NTA) 对于发现复杂样品中未知的分子特征至关重要.
- NTA有助于识别与表型相关的特征和分类样本类型.
- 目前的NTA方法需要广泛的数据分析和手动注释.
研究的目的:
- 为LC-IMS-MS平台的NTA数据开发一种快速选方法.
- 为了实现有效的样本分类,并突出潜在的特征.
- 为了减少NTA数据分析的劳动密集性.
主要方法:
- 聚合和压缩LC-IMS-MS光谱为每个样本的单一总和光谱.
- 使用拉索和支持向量机来进行表型分类.
- 通过检查选定的光谱坐标内的特征来进行分子注释.
主要成果:
- 总结的IMS-MS选方法成功地在脂质和暴露性NTA数据中识别了分辨光谱坐标.
- 开发了能够将样品分类的选模型,与使用注释数据的模型相比,精度提高了25%.
- 促进了表型分子注释的评估.
结论:
- 开发的方法为NTA数据提供了快速有效的选工具.
- 这种方法可以提高样本分类的准确性,并有助于特征发现.
- 该技术适用于各种NTA数据集,包括临床和环境样本.
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