Chapter 6. Bioinformatic Strategies for cDNA-Microarray Data Processing

  1. Andreas Scherer Founder/CEO of Spheromics
  1. Jessica Fahlén1,
  2. Mattias Landfors2,
  3. Eva Freyhult3,
  4. Max Bylesjö4,
  5. Johan Trygg4,
  6. Torgeir R Hvidsten5 and
  7. Patrik Rydén2

Published Online: 2 NOV 2009

DOI: 10.1002/9780470685983.ch6

Batch Effects and Noise in Microarray Experiments: Sources and Solutions

Batch Effects and Noise in Microarray Experiments: Sources and Solutions

How to Cite

Fahlén, J., Landfors, M., Freyhult, E., Bylesjö, M., Trygg, J., Hvidsten, T. R. and Rydén, P. (2009) Bioinformatic Strategies for cDNA-Microarray Data Processing, in Batch Effects and Noise in Microarray Experiments: Sources and Solutions (ed A. Scherer), John Wiley & Sons, Ltd, Chichester, UK. doi: 10.1002/9780470685983.ch6

Editor Information

  1. Spheromics, Kontiolahti, Finland

Author Information

  1. 1

    Department of Statistics, Umeå University, Umeå, Sweden

  2. 2

    Department of Mathematics and Mathematical Statistics, Umeå University, Umeå, Sweden

  3. 3

    Department of Clinical Microbiology, Umeå University, Umeå, Sweden

  4. 4

    Computational Life Science Cluster, Chemical Biology Center, KBC, Umeå University, Umeå, Sweden

  5. 5

    Umeå Plant Science Centre, Department of Plant Physiology, Umeå University, Umeå, Sweden

Publication History

  1. Published Online: 2 NOV 2009
  2. Published Print: 30 OCT 2009

ISBN Information

Print ISBN: 9780470741382

Online ISBN: 9780470685983

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Keywords:

  • microarray;
  • data analysis;
  • pre-processing;
  • evaluation;
  • spike-in;
  • bias;
  • sensitivity;
  • false positive rate

Summary

Pre-processing plays a vital role in cDNA-microarray data analysis. Without proper pre-processing it is likely that the biological conclusions will be misleading. However, there are many alternatives and in order to choose a proper pre-processing procedure it is necessary to understand the effect of different methods. This chapter discusses several pre-processing steps, including image analysis, background correction, normalization, and filtering. Spike-in data are used to illustrate how different procedures affect the analytical ability to detect differentially expressed genes and estimate their regulation. The result shows that pre-processing has a major impact on both the experiment's sensitivity and its bias. However, general recommendations are hard to give, since pre-processing consists of several actions that are highly dependent on each other. Furthermore, it is likely that pre-processing have a major impact on downstream analysis, such as clustering and classification, and pre-processing methods should be developed and evaluated with this in mind.