Batch Effects and Noise in Microarray Experiments: Sources and Solutions

Batch Effects and Noise in Microarray Experiments: Sources and Solutions

Editor(s): Andreas Scherer

Published Online: 2 NOV 2009

Print ISBN: 9780470741382

Online ISBN: 9780470685983

DOI: 10.1002/9780470685983

About this Book

Batch Effects and Noise in Microarray Experiments: Sources and Solutions looks at the issue of technical noise and batch effects in microarray studies and illustrates how to alleviate such factors whilst interpreting the relevant biological information.

Each chapter focuses on sources of noise and batch effects before starting an experiment, with examples of statistical methods for detecting, measuring, and managing batch effects within and across datasets provided online. Throughout the book the importance of standardization and the value of standard operating procedures in the development of genomics biomarkers is emphasized.

Key Features:

  • A thorough introduction to Batch Effects and Noise in Microrarray Experiments.
  • A unique compilation of review and research articles on handling of batch effects and technical and biological noise in microarray data.
  • An extensive overview of current standardization initiatives.
  • All datasets and methods used in the chapters, as well as colour images, are available on www.the-batch-effect-book.org, so that the data can be reproduced.

An exciting compilation of state-of-the-art review chapters and latest research results, which will benefit all those involved in the planning, execution, and analysis of gene expression studies.

Table of contents

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    2. Chapter 5

      Aspects of Technical Bias (pages 51–60)

      Martin Schumacher, Frank Staedtler, Wendell D Jones and Andreas Scherer

    3. Chapter 6

      Bioinformatic Strategies for cDNA-Microarray Data Processing (pages 61–74)

      Jessica Fahlén, Mattias Landfors, Eva Freyhult, Max Bylesjö, Johan Trygg, Torgeir R Hvidsten and Patrik Rydén

    4. Chapter 8

      Variance due to Smooth Bias in Rat Liver and Kidney Baseline Gene Expression in a Large Multi-laboratory Data Set (pages 87–99)

      Michael J Boedigheimer, Jeff W Chou, J Christopher Corton, Jennifer Fostel, Raegan O'Lone, P Scott Pine, John Quackenbush, Karol L Thompson and Russell D Wolfinger

    5. Chapter 9

      Microarray Gene Expression: The Effects of Varying Certain Measurement Conditions (pages 101–111)

      Walter Liggett, Jean Lozach, Anne Bergstrom Lucas, Ron L Peterson, Marc L Salit, Danielle Thierry-Mieg, Jean Thierry-Mieg and Russell D Wolfinger

    6. Chapter 13

      Batch Profile Estimation, Correction, and Scoring (pages 155–165)

      Tzu-Ming Chu, Wenjun Bao, Russell S Thomas and Russell D Wolfinger

    7. Chapter 14

      Visualization of Cross-Platform Microarray Normalization (pages 167–181)

      Xuxin Liu, Joel Parker, Cheng Fan, Charles M Perou and J S Marron

    8. Chapter 16

      Potential Sources of Spurious Associations and Batch Effects in Genome-Wide Association Studies (pages 191–201)

      Huixiao Hong, Leming Shi, James C Fuscoe, Federico Goodsaid, Donna Mendrick and Weida Tong

    9. Chapter 18

      Data, Analysis, and Standardization (pages 215–229)

      Gabriella Rustici, Andreas Scherer and John Quackenbush

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