在ToF-SIMS中增强单细胞代谢稳定性的Norm-SVR
Mingru Liu1, Hongzhe Ma1, Xiang Fang1
1Key Laboratory of Mass Spectrometry Imaging and Metabolomics , State Ethnic Affairs Commission, Center for Imaging and Systems Biology, College of Life and Environmental Sciences, Minzu University of China, Beijing 100081, China.
Metabolites
|January 27, 2026
概括
飞行时间二次离子质谱 (ToF-SIMS) 单细胞代谢数据可以使用规范化支向量回归 (Norm-SVR) 来改进. 这种方法有效地减少了批量效应和变异性,提高了广泛分析的数据质量.
科学领域:
- 质谱测量质量谱测量
- 分析化学是一种分析化学.
- 单细胞生物学 单细胞生物学
背景情况:
- 数据稳定性对于飞行时间二次离子质谱 (ToF-SIMS) 单细胞分析至关重要.
- 样本处理和仪器条件等因素会在ToF-SIMS数据中带来不确定性.
- 对于ToF-SIMS数据的现有校正方法是有限的,对于单细胞代谢学没有特定的方法.
研究的目的:
- 评估标准化支向量回归 (Norm-SVR) 对于纠正ToF-SIMS单细胞代谢数据的有效性.
- 为了比较Norm-SVR的性能与传统的总离子强度正常化.
- 为了解决ToF-SIMS单细胞代谢学中缺乏特定数据校正方法的问题.
主要方法:
- 应用规范化支向量回归 (Norm-SVR),一种常用于大规模代谢学数据校正的技术.
- 使用Norm-SVR.的ToF-SIMS单细胞代谢学数据的纠正.
- 对Norm-SVR与总离子强度正常化的比较分析.
主要成果:
- 在ToF-SIMS单细胞代谢数据中,Norm-SVR有效地减少了批量效应.
- 标准-SVR方法显著降低了数据的变化.
- 该研究证明了Norm-SVR在此应用中的有效性和实用性.
结论:
- 诺姆-SVR是一种实用和有效的方法,用于纠正ToF-SIMS单细胞代谢数据.
- 这种方法提高了大规模ToF-SIMS分析数据集的数据质量保证.
- 这些发现为使用ToF-SIMS.更可靠的单细胞代谢分析铺平了道路.
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