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Statistical Methods in Proteomics

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Springer Handbook of Engineering Statistics

Part of the book series: Springer Handbooks ((SHB))

Abstract

Proteomics technologies are rapidly evolving and attracting great attention in the post-genome era. In this chapter, we review two key applications of proteomics techniques: disease biomarker discovery and protein/peptide identification. For each of the applications, we state the major issues related to statistical modeling and analysis, review related work, discuss their strengths and weaknesses, and point out unsolved problems for future research.

We organize this chapter as follows. Section 34.1 briefly introduces mass spectrometry (MS) and tandem MS/MS with a few sample plots showing the data format. Section 34.2 focuses on MS data preprocessing. We first review approaches in peak identification and then address the problem of peak alignment. After that, we point out unsolved problems and propose a few possible solutions.

Section 34.3 addresses the issue of feature selection. We start with a simple example showing the effect of a large number of features. Then we address the interaction of different features and discuss methods of reducing the influence of noise. We finish this section with some discussion on the application of machine learning methods in feature selection. Section 34.4 addresses the problem of sample classification. We describe the random forest method in detail in Sect. 34.5.

In Sect. 34.6 we address protein/peptide identification. We first review database searching methods in Sect. 34.6.1 and then focus on de novo MS/MS sequencing in Sect. 34.6.2. After reviewing major protein/peptide identification programs like SEQUEST and MASCOT in Sect. 34.6.3, we conclude the section by pointing out some major issues that need to be addressed in protein/peptide identification.

Proteomics technologies are considered the major player in the analysis and understanding of protein function and biological pathways. The development of statistical methods and software for proteomics data analysis will continue to be the focus of proteomics for years to come.

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Abbreviations

CART:

classification and regression tree

CID:

collision-induced dissociation

CV:

cross-validation

DP:

dynamic programming

MS:

mass spectrometry

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Author information

Authors and Affiliations

  1. Department of Molecular Biophysics and Biochemistry, Yale Center for Statistical Genomics and Proteomics, Yale University, 300 George Street, 06511, New Haven, CT, USA

    Weichuan Yu

  2. Division of Biostatistics, University of Minnesota, School of Public Health, A460 Mayo Building, MMC 303, 420 Delaware St SE, 55455, Minneapolis, MN, USA

    Baolin Wu

  3. Department of Epidemiology and Public Health, Yale University, School of Medicine, 60 College Street, 06520, New Haven, CT, USA

    Tao Huang

  4. Department of Applied Mathematics, Yale University, 300 George Street, 06511, New Heaven, CT, USA

    Xiaoye Li

  5. Molecular Biophysics and Biochemistry, Yale University, 300 George Street, G005, 06520, New Haven, CT, USA

    Kenneth Williams

  6. Department of Epidemiology and Public Health, Yale University School of Medicine, 60 College Street, 06520-8034, New Haven, CT, USA

    Hongyu Zhao

Authors
  1. Weichuan Yu
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  2. Baolin Wu
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  3. Tao Huang
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  4. Xiaoye Li
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  5. Kenneth Williams
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  6. Hongyu Zhao
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Corresponding authors

Correspondence to Weichuan Yu , Baolin Wu , Tao Huang , Xiaoye Li , Kenneth Williams or Hongyu Zhao .

Editor information

Editors and Affiliations

  1. Department of Industrial and Systems Engineering, Rutgers the State University of New Jersey, 96 Frelinghuysen Road, 08854, Piscataway, NJ, USA

    Hoang Pham Prof.

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Yu, W., Wu, B., Huang, T., Li, X., Williams, K., Zhao, H. (2006). Statistical Methods in Proteomics. In: Pham, H. (eds) Springer Handbook of Engineering Statistics. Springer Handbooks. Springer, London. https://doi.org/10.1007/978-1-84628-288-1_34

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  • DOI: https://doi.org/10.1007/978-1-84628-288-1_34

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