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Mass spectrometry data processing using zero-crossing lines in multi-scale of Gaussian derivative wavelet
- Source :
- Bioinformatics
- Publication Year :
- 2010
- Publisher :
- Oxford University Press (OUP), 2010.
-
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 Contact: heng@uta.edu
- Subjects :
- Statistics and Probability
Shrinkage estimator
Noise reduction
Gaussian
Normal Distribution
Derivative
Biochemistry
Noise (electronics)
Eccb 2010 Conference Proceedings September 26 to September 29, 2010, Ghent, Belgium
symbols.namesake
Wavelet
Statistics
Molecular Biology
Mathematics
Other Bioinformatics Applications
Zero crossing
Original Papers
Computer Science Applications
Computational Mathematics
ROC Curve
Computational Theory and Mathematics
Spectrometry, Mass, Matrix-Assisted Laser Desorption-Ionization
symbols
Algorithm
Algorithms
Energy (signal processing)
Subjects
Details
- ISSN :
- 13674811 and 13674803
- Volume :
- 26
- Database :
- OpenAIRE
- Journal :
- Bioinformatics
- Accession number :
- edsair.doi.dedup.....3fa102a02eab88d531f83701c4010123