Dana-Farber Cancer Institute/Harvard School of Public Health, Department of Biostatistics and Computational Biology/Department of Biostatistics, Boston, Massachusetts, USA.
OMICS. 2010 Jun;14(3):283-95. doi: 10.1089/omi.2009.0119.
Proteomic profiling by MALDI TOF mass spectrometry (MS) is an effective method for identifying biomarkers from human serum/plasma, but the process is complicated by the presence of noise in the spectra. In MALDI TOF MS, the major noise source is chemical noise, which is defined as the interference from matrix material and its clusters. Because chemical noise is nonstationary and nonwhite, wavelet-based denoising is more effective than conventional noise reduction schemes based on Fourier analysis. However, current wavelet-based denoising methods for mass spectrometry do not fully consider the characteristics of chemical noise. In this article, we propose new wavelet-based high-frequency noise reduction and baseline correction methods that were designed based on the discrete stationary wavelet transform. The high-frequency noise reduction algorithm adaptively estimates the time-varying threshold for each frequency subband from multiple realizations of chemical noise and removes noise from mass spectra of samples using the estimated thresholds. The baseline correction algorithm computes the monotonically decreasing baseline in the highest approximation of the wavelet domain. The experimental results demonstrate that our algorithms effectively remove artifacts in mass spectra that are due to chemical noise while preserving informative features as compared to commonly used denoising methods.
基于 MALDI-TOF-MS 的蛋白质组学分析是一种从人血清/血浆中鉴定生物标志物的有效方法，但由于光谱中存在噪声，该过程变得复杂。在 MALDI-TOF-MS 中，主要的噪声源是化学噪声，它被定义为基质材料及其团簇的干扰。由于化学噪声是非平稳和非白色的，基于小波的去噪比基于傅里叶分析的传统降噪方案更有效。然而，目前基于小波的质谱去噪方法并没有充分考虑化学噪声的特性。在本文中，我们提出了新的基于小波的高频降噪和基线校正方法，这些方法是基于离散平稳小波变换设计的。高频降噪算法自适应地从多个化学噪声实现中估计每个频率子带的时变阈值，并使用估计的阈值从样品的质谱中去除噪声。基线校正算法在小波域的最高逼近中计算单调递减的基线。实验结果表明，与常用的去噪方法相比，我们的算法在保留有价值特征的同时，有效地去除了由于化学噪声而产生的质谱伪影。
