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Computational Statistics & Data Analysis
Volume 52, Issue 1, 15 September 2007, Pages 211-220
 
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doi:10.1016/j.csda.2007.02.022    How to Cite or Link Using DOI (Opens New Window)
Copyright © 2007 Published by Elsevier B.V.

Wavelet-based procedures for proteomic mass spectrometry data processing

Shuo Chena, Don Hongb, Corresponding Author Contact Information, E-mail The Corresponding Author and Yu Shyra

aBiostatistics Shared Resource, Vanderbilt Ingram Cancer Center, Vanderbilt University, Nashville, Tennessee, USA bDepartment of Mathematical Sciences, Middle Tennessee State University, Murfreesboro, Tennessee 37132, USA

Available online 5 March 2007.

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Abstract

Proteomics aims at determining the structure, function and expression of proteins. High-throughput mass spectrometry (MS) is emerging as a leading technique in the proteomics revolution. Though it can be used to find disease-related protein patterns in mixtures of proteins derived from easily obtained samples, key challenges remain in the processing of proteomic MS data. Multiscale mathematical tools such as wavelets play an important role in signal processing and statistical data analysis. A wavelet-based algorithm for proteomic data processing is developed. A MATLAB implementation of the software package, called WaveSpect0, is presented including processing procedures of step-interval unification, adaptive stationary discrete wavelet denoising, baseline correction using splines, normalization, peak detection, and a newly designed peak alignment method using clustering techniques. Applications to real MS data sets for different cancer research projects in Vanderbilt Ingram Cancer Center show that the algorithm is efficient and satisfactory in MS data mining.

Keywords: Proteomic data processing; Biomarker discovery; Mass spectrometry; Splines; Wavelets

Article Outline

1. Introduction
2. Wavelets and applications in proteomic MS data analysis
2.1. Wavelets for MALDI-TOF MS Data
2.2. Wavelet denoising strategy
2.3. Analysis on wavelet domain
3. Method: processing procedures
3.1. Step-interval unification and denoising
3.2. Baseline correction and normalization
3.3. Peak detection and alignment
4. Results
4.1. Peak selection based method results
4.2. Wavelet coefficients analysis results
Acknowledgements
References








 
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