在染色图中用于消除噪声的无声化算法的自动调整
Emery Bosten1, Peter Van Broeck2, Deirdre Cabooter3
1Department for Pharmaceutical and Pharmacological Sciences, Pharmaceutical Analysis, University of Leuven (KU Leuven), Herestraat 49, Leuven 3000, Belgium; Janssen Pharmaceutica, Department of Pharmaceutical Development and Manufacturing Sciences, Turnhoutseweg 30, Beerse, Belgium.
染色体无声化的自动化方法改善了数据分析. 新型中位数估计器提高了性能,特别是以稀疏性为基础的方法,导致了更好的分析物检测和量化.
科学领域:
- 分析化学 分析化学
- 信号处理 信号处理
- 染色体学 染色体学 是一种染色学.
背景情况:
- 染色体质量对于准确的分析物检测和量化至关重要.
- 传统的超参数调整用于无声化算法是手动和主观的.
- 现有的自动化方法往往是特定于某一方法的,或者需要先前的知识.
研究的目的:
- 引入和评估用于染色体无声化的自动化,普遍适用的参数调方法.
- 为交叉验证,自相关和残余方差方法提出新的中位数估计器实施方案.
- 用各种无声化算法来评估这些调方法的性能.
主要方法:
- 研究了L和V曲线,k倍交叉验证,自相关函数和参数调节的剩余方差估计.
- 针对特定染色体形状 (平坦基线,尖峰) 实施的中位数估计器.
- 使用Savitzky-Golay过器,Whittaker光滑器和两种基于稀疏性的调整方法进行了测试.
主要成果:
- 估计者的中位数显著改善了无声化和信息保存 (模拟数据高达4倍,实验数据高达10倍).
- 剩余差异,k倍交叉验证和自相对应方法产生了类似的低根-平均平方误差.
- 基于稀疏性的脱色方法始终优于其他方法,证明最适合染色图.
结论:
- 自动参数调整方法,特别是中位数估计器,可以增强染色体无声化.
- 基于稀疏性的信号处理方法对染色体分析非常有效.
- 这些方法提供客观,普遍适用的解决方案,以提高染色学数据质量.
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