WAVELETS AND PROJECTING SPECTRUM BINNING FOR PROTEOMIC DATA PROCESSING - Quantitative Medical Data Analysis Using Mathematical Tools and Statistical Techniques - page 159

CHAPTER 8
WAVELETS AND PROJECTING SPECTRUM BINNING
FOR PROTEOMIC DATA PROCESSING
Don Hong a , Huiming Li b , Ming Li c , and Yu Shyr c;d
a Department of Mathematical Sciences, Middle Tennessee State University,
Murfreesboro, Tennessee, USA
E-mail: [e-mail]
b ITS, Peabody College, Vanderbilt University, Nashville, Tennessee, USA
c Department of Biostatistics, Vanderbilt University, Nashville, Tennessee, USA
d Department of Statistics, National Cheng Kung University, Taiwan, ROC
High throughput mass spectrometry (MS) has been motivated greatly
from recent developments in both chemistry and biology. Its technology
has been extended to proteomics as a tool in rapid protein identica-
tion and is emerging as a leading technology in the proteomics revolu-
tion. However, key challenges still remain in the processing of proteomic
MS data. It is substantial to develop a comprehensive set of mathe-
matical and computational tools for proteomic MS data analysis. The
processing goal is to eectively and correctly obtain the true informa-
tion from the raw MS data for further statistical analysis. To provide a
nal peak list for future statistical analysis, the whole processing proce-
dure usually takes the following steps: data registration (calibration), de-
noising (smoothing), baseline correction, normalization, peak detection,
and peak alignment (binning). In this chapter, a wavelet-based approach
for data denoising is discussed and a so-called projecting spectrum bin-
ning (PSB) method for proteomic MS cross samples peaks alignment is
introduced. Applications to real MS datasets for dierent cancer research
projects in Vanderbilt Ingram Cancer Center show that the approach is
ecient and satisfactory.
1. Introduction
Proteomics is the study of the function of all expressed proteins. Mass
Spectrometry (MS) technology makes it possible to study various biological
samples at their protein level. High-throughput Mass Spectrometry, includ-
ing Matrix-Assisted Laser Desorption/Ionization, Time-of-Flight (MALDI-
TOF) and Surface-Enhanced Laser Desorption/Ionization Time-of-Flight