Chapter 3. Experimental Design

  1. Andreas Scherer Founder/CEO of Spheromics
  1. Peter Grass

Published Online: 2 NOV 2009

DOI: 10.1002/9780470685983.ch3

Batch Effects and Noise in Microarray Experiments: Sources and Solutions

Batch Effects and Noise in Microarray Experiments: Sources and Solutions

How to Cite

Grass, P. (2009) Experimental Design, in Batch Effects and Noise in Microarray Experiments: Sources and Solutions (ed A. Scherer), John Wiley & Sons, Ltd, Chichester, UK. doi: 10.1002/9780470685983.ch3

Editor Information

  1. Spheromics, Kontiolahti, Finland

Author Information

  1. Novartis Institutes of Biomedical Research, Novartis Pharma AG, Basel, Switzerland

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:

  • experimental design;
  • experimental error;
  • biological variation;
  • systematic error;
  • bias;
  • batch effect;
  • sample size;
  • power of test

Summary

The task of experimental design is to design a study in as economical a way as possible and simultaneously to optimize the information content of the experimental data. Design concepts include the different types of variation, including experimental error, biological variation and systematic error, called bias. A batch effect refers to systematic errors due to technical reasons. The conclusion drawn from the study results should be valid for an entire population, but a study is conducted in a sample with a limited number of experimental units (patients) where several observational units (measurements) are taken from each under the same experimental condition. Measures to increase precision and accuracy of an experiment include randomization, blocking, and replication. An increased sample size leads to a greater statistical power. Blinding, crossing, choice of controls, symmetry and simplicity of design are further means to increase precision.