Statistical Analysis in Proteomics

  • Klaus Jung

Part of the Methods in Molecular Biology book series (MIMB, volume 1362)

Table of contents

  1. Front Matter
    Pages i-x
  2. Proteomics, Study Design, and Data Processing

    1. Front Matter
      Pages 1-1
    2. Christof Lenz, Hassan Dihazi
      Pages 3-27
    3. Tsung-Heng Tsai, Minkun Wang, Habtom W. Ressom
      Pages 63-76
    4. HyungJun Cho, Soo-Heang Eo
      Pages 91-102
  3. Group Comparisons

    1. Front Matter
      Pages 103-103
    2. Suruchi Aggarwal, Amit Kumar Yadav
      Pages 119-128
    3. Juhee Lee, Peter Müller, Yitan Zhu, Yuan Ji
      Pages 129-141
  4. Classification Methods

    1. Front Matter
      Pages 157-157
    2. David Conde, Miguel A. Fernández, Bonifacio Salvador, Cristina Rueda
      Pages 159-174
    3. Julia Kuligowski, David Pérez-Guaita, Guillermo Quintás
      Pages 175-184
    4. Frank-Michael Schleif
      Pages 185-195
  5. Data Integration

  6. Special Topics

    1. Front Matter
      Pages 225-225

About this book

Introduction

This valuable collection aims to provide a collection of frequently used statistical methods in the field of proteomics. Although there is a large overlap between statistical methods for the different ‘omics’ fields, methods for analyzing data from proteomics experiments need their own specific adaptations. To satisfy that need, Statistical Analysis in Proteomics focuses on the planning of proteomics experiments, the preprocessing and analysis of the data, the integration of proteomics data with other high-throughput data, as well as some special topics. Written for the highly successful Methods in Molecular Biology series, the chapters contain the kind of detail and expert implementation advice that makes for a smooth transition to the laboratory.

 

Practical and authoritative, Statistical Analysis in Proteomics serves as an ideal reference for statisticians involved in the planning and analysis of proteomics experiments, beginners as well as advanced researchers, and also for biologists, biochemists, and medical researchers who want to learn more about the statistical opportunities in the analysis of proteomics data.

Keywords

Data analysis High-throughput data Omics Preprocessing Statistical methods

Editors and affiliations

  • Klaus Jung
    • 1
  1. 1.Department of Medical Statistics, University Medical Center GöttingenGöttingenGermany

Bibliographic information

  • DOI https://doi.org/10.1007/978-1-4939-3106-4
  • Copyright Information Springer Science+Business Media New York 2016
  • Publisher Name Humana Press, New York, NY
  • eBook Packages Springer Protocols
  • Print ISBN 978-1-4939-3105-7
  • Online ISBN 978-1-4939-3106-4
  • Series Print ISSN 1064-3745
  • Series Online ISSN 1940-6029
  • About this book
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