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An adaptive approach to denoising tandem mass spectra

机译:串联质谱去噪的自适应方法

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摘要

In our recently developed denoising method [1], a linear combination of five features is used to adjust the peak intensities in tandem mass spectra. Although the method shows a promise, the coefficients (weights) of the linear combination were fixed and determined empirically. In this paper, we propose an adaptive approach for estimating these weights. The proposed approach: (1) calculates the score for each peak in a data set with the empirically determined weights in [1], (2) selects the training dataset based on the scores of peaks, (3) applies the LDA (Linear discriminant analysis) to the training dataset and take the solution of LDA as the new weights, (4) calculates the score again with new weights, (5) repeats (2) – (4) until weights have no significant change. After getting the final weights, the proposed approach follows the methods developed in [1]. The proposed approach is applied to two tandem mass spectra datasets: ISB (with low resolution) and TOV-Q (with high resolution) to evaluate its performance. The results show that about 66% of peaks (likely noise peaks) can be removed and that the number of peptides identified by Mascot increases by 14% and 21% for ISB and TOV-Q dataset, respectively, comparing to the previous work.
机译:在我们最近开发的去噪方法中[1],使用五个特征的线性组合来调整串联质谱图中的峰强度。尽管该方法显示出了希望,但线性组合的系数(权重)是固定的并凭经验确定。在本文中,我们提出了一种自适应方法来估计这些权重。提出的方法:(1)使用[1]中凭经验确定的权重计算数据集中每个峰的分数,(2)根据峰的分数选择训练数据集,(3)应用LDA(线性判别式)分析)到训练数据集,并将LDA的解作为新的权重,(4)使用新的权重再次计算分数,(5)重复(2)–(4),直到权重没有明显变化为止。在获得最终权重后,所提出的方法遵循[1]中开发的方法。所提出的方法应用于两个串联质谱数据集:ISB(低分辨率)和TOV-Q(高分辨率)以评估其性能。结果表明,与先前的工作相比,对于ISB和TOV-Q数据集,可以去除约66%的峰(可能是噪声峰),并且通过Mascot鉴定的肽数分别增加了14%和21%。

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