Mass spectrometry data processing using zero-crossing lines in multi-scale of Gaussian derivative wavelet
Year: 2010
Type: article
Abstract: MOTIVATION: Peaks are the key information in mass spectrometry (MS) which has been increasingly used to discover diseases-related proteomic patterns. Peak detection is an essential step for MS-based proteomic data analysis. Recently, several peak detection algorithms have been proposed. However, in these algorithms, there are three major deficiencies: (i) because the noise is often removed, the true signal could also be removed; (ii) baseline removal step may get rid of true peaks and create new false peaks; (iii) in peak quantification step, a threshold of signal-to-noise ratio (SNR) is usually used to remove false peaks; however, noise estimations in SNR calculation are often inaccurate in either time or wavelet domain. In this article, we propose new algorithms to solve these problems. First, we use bivariate shrinkage estimator in stationary wavelet domain to avoid removing true peaks in denoising step. Second, without baseline removal, zero-crossing lines in multi-scale of derivative Gaussian wavelets are investigated with mixture of Gaussian to estimate discriminative parameters of peaks. Third, in quantification step, the frequency, SD, height and rank of peaks are used to detect both high and small energy peaks with robustness to noise. RESULTS: We propose a novel Gaussian Derivative Wavelet (GDWavelet) method to more accurately detect true peaks with a lower false discovery rate than existing methods. The proposed GDWavelet method has been performed on the real Surface-Enhanced Laser Desorption/Ionization Time-Of-Flight (SELDI-TOF) spectrum with known polypeptide positions and on two synthetic data with Gaussian and real noise. All experimental results demonstrate that our method outperforms other commonly used methods. The standard receiver operating characteristic (ROC) curves are used to evaluate the experimental results. AVAILABILITY: http://ranger.uta.edu/~heng/MS/GDWavelet.html or http://www.naaan.org/nhanguyen/archive.htm.
Source: Bioinformatics
Authors: Nha Nguyen, Heng Huang, Soontorn Oraintara, An Vo
Language: English
Subfield: Spectroscopy
Field: Chemistry
Domain: Physical Sciences
Open Access status: hybrid
Estimated APC paid: $3,989
Funder: National Science Foundation
Awards: Collaborative Research: Non-negative Matrix Factorizations for Data Mining: Foundations, Capabilities, and Applications, EAGER: Collaborative Research: Cross-Domain Knowledge Transformation via Matrix Decompositions, Collaborative Research: Matrix-Model Machine Learning: Unifying Machine Learning and Scientific Computing, New Theoretical Foundations of Tensor Applications: Clustering, Error Analysis, Global Convergence, and Robust Formulations, MRI: Development of a Next-Generation Multimodal Data Management Human-Sensing Instrument for Trustworthy Research Collaboration and Quality of Life Improvement
OpenAlex ID: W2137266176