NECTAR:一种用于描述和纠正QToF质谱成像数据中的噪声的新算法
Ariadna González-Fernández1, Alex Dexter1, Chelsea J Nikula1
1National Physical Laboratory, Teddington, Middlesex TW11 0LW, U.K.
Journal of the American Society for Mass Spectrometry
|October 11, 2023
概括
一个新的算法,NECTAR,有效地减少了质谱成像 (MSI) 数据中的噪声. 它识别和删除随机,化学和背景噪声,改善峰值选择以进行更好的分析.
科学领域:
- 分析化学 分析化学
- 生物医学成像技术 生物医学成像技术
- 数据科学数据科学数据科学
背景情况:
- 质谱成像 (MSI) 产生了大量数据集,具有显著的噪音.
- 有效的降噪对于准确的峰值识别和数据解释至关重要.
- 现有的方法可能无法充分解决MSI QToF数据中的各种噪音类型.
研究的目的:
- 开发和验证一个新的算法,NECTAR (NoisE Correction AlgoRithm),用于描述和纠正MSI QToF数据中的噪声.
- 为了区分和量化随机,化学和背景噪声.
- 为了提高光谱基线和噪声水平的确定可靠性.
主要方法:
- 在MSI QToF数据中随机,化学和背景噪声的统计特征.
- 开发NECTAR算法用于噪声校正.
- 从小鼠胰腺组织获取的MALDI和DESI QToF数据中应用NECTAR.
- 使用主要组件分析 (PCA) 和t分布式随机邻方嵌入 (t-SNE) 分析被拒绝的数据.
主要成果:
- 随机噪声主导较低的m/z值 (~50-400Da),而化学噪声则在较高的m/z值 (>400Da) 中占主导地位.
- 尼克塔尔将化学噪声降低了大约3倍.
- 降低噪声导致了更可靠的光谱基线和噪声水平估计.
- 处理后的分析证实有效地去除了不信息的峰值和选择了相关的峰值.
结论:
- 在MSI QToF数据中,NECTAR有效地描述和减少多种噪音类型.
- 该算法提高了信号与噪声的比率,促进了更准确的数据分析.
- NECTAR可以从复杂的MSI数据集中提升生物相关峰值的选择.
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