Wee Andrew, Grayden David B, Zhu Yonggang, Petkovic-Duran Karolina, Smith David
Department of Civil and Environmental Engineering, The University of Melbourne, Parkville, Australia.
Electrophoresis. 2008 Nov;29(20):4215-25. doi: 10.1002/elps.200800096.
Contactless conductivity detector technology has unique advantages for microfluidic applications. However, the low S/N and varying baseline makes the signal analysis difficult. In this paper, a continuous wavelet transform-based peak detection algorithm was developed for CE signals from microfluidic chips. The Ridger peak detection algorithm is based on the MassSpecWavelet algorithm by Du et al. [Bioinformatics 2006, 22, 2059-2065], and performs a continuous wavelet transform on data, using a wavelet proportional to the first derivative of a Gaussian function. It forms sequences of local maxima and minima in the continuous wavelet transform, before pairing sequences of maxima to minima to define peaks. The peak detection algorithm was tested against the Cromwell, MassSpecWavelet, and Linear Matrix-assisted laser desorption/ionization-time-of-flight-mass spectrometer Peak Indication and Classification algorithms using experimental data. Its sensitivity to false discovery rate curve is superior to other techniques tested.
非接触式电导检测器技术在微流体应用中具有独特优势。然而,低信噪比和变化的基线使得信号分析变得困难。本文针对微流控芯片的CE信号,开发了一种基于连续小波变换的峰值检测算法。脊峰检测算法基于Du等人的MassSpecWavelet算法[《生物信息学》2006年,22卷,2059 - 2065页],对数据进行连续小波变换,使用与高斯函数一阶导数成比例的小波。它在连续小波变换中形成局部最大值和最小值序列,然后将最大值序列与最小值序列配对以定义峰值。使用实验数据,将该峰值检测算法与克伦威尔、MassSpecWavelet和线性基质辅助激光解吸/电离飞行时间质谱仪峰值指示与分类算法进行了测试。其对错误发现率曲线的敏感性优于其他测试技术。