Nguyen Nha, Huang Heng, Oraintara Soontorn, Vo An
Department of Electrical Engineering, University of Texas at Arlington, TX, USA.
Comput Syst Bioinformatics Conf. 2008;7:85-96.
Mass Spectrometry (MS) is increasingly being used to discover disease related proteomic patterns. The peak detection step is one of most important steps in the typical analysis of MS data. Recently, many new algorithms have been proposed to increase true position rate with low false position rate in peak detection. Most of them follow two approaches: one is denoising approach and the other one is decomposing approach. In the previous studies, the decomposition of MS data method shows more potential than the first one. In this paper, we propose a new method named GaborLocal which can detect more true peaks with a very low false position rate. The Gaussian local maxima is employed for peak detection, because it is robust to noise in signals. Moreover, the maximum rank of peaks is defined at the first time to identify peaks instead of using the signal-to-noise ratio and the Gabor filter is used to decompose the raw MS signal. We perform the proposed method on the real SELDI-TOF spectrum with known polypeptide positions. The experimental results demonstrate our method outperforms other common used methods in the receiver operating characteristic (ROC) curve.
质谱分析法（MS）越来越多地被用于发现与疾病相关的蛋白质组学模式。峰检测步骤是质谱数据分析典型流程中最重要的步骤之一。最近，人们提出了许多新算法，以在峰检测中提高真阳性率并降低假阳性率。它们大多遵循两种方法：一种是去噪方法，另一种是分解方法。在先前的研究中，质谱数据分解方法比第一种方法显示出更大的潜力。在本文中，我们提出了一种名为GaborLocal的新方法，它可以以非常低的假阳性率检测到更多的真实峰。采用高斯局部最大值进行峰检测，因为它对信号中的噪声具有鲁棒性。此外，首次定义了峰的最大秩来识别峰，而不是使用信噪比，并且使用Gabor滤波器对原始质谱信号进行分解。我们在具有已知多肽位置的真实SELDI-TOF光谱上执行所提出的方法。实验结果表明，我们的方法在接收器操作特性（ROC）曲线方面优于其他常用方法。
