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Experimental Designs and ANOVA for Microarray Data

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Book cover Handbook of Statistical Bioinformatics

Part of the book series: Springer Handbooks of Computational Statistics ((SHCS))

Abstract

Microarray experiments are complex, multistep processes that represent a considerable investment of time and resources. Proper experimental design and analysis are critical to the success of a microarray experiment, and must be considered early in the planning of the experiment. Many aspects of experimental design from low-throughput experiments, such as randomization, replication, and blocking, remain applicable to microarray experiments as well. Similarly, the analysis of variance (ANOVA) remains a valid approach for analyzing data from most microarray experiments. However, the high-dimensional nature of microarrays introduces additional considerations into the design and analysis. This chapter provides an overview of the unique statistical challenges presented by microarrays and describes computational methods for implementing these statistical algorithms.

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Author information

Authors and Affiliations

  1. University of Alabama-Birmingham, Birmingham, Alabama, USA

    Richard E. Kennedy & Xiangqin Cui

Authors
  1. Richard E. Kennedy
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  2. Xiangqin Cui
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Corresponding author

Correspondence to Xiangqin Cui .

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Editors and Affiliations

  1. , Institute of Statistics, National Chiao Tung University, Ta Hsueh Road 1001, Hsinchu, 30050, Taiwan, Taiwan R.O.C.

    Henry Horng-Shing Lu

  2. , Department of Empirical Inference, MPI for Intelligent Systems, Spemannstraße 38, Tübingen, 72076, Germany

    Bernhard Schölkopf

  3. School of Medicine, Dept. Epidemiology & Public Health, Yale University, College Street 60, New Haven, 06520, Connecticut, USA

    Hongyu Zhao

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Kennedy, R.E., Cui, X. (2011). Experimental Designs and ANOVA for Microarray Data. In: Lu, HS., Schölkopf, B., Zhao, H. (eds) Handbook of Statistical Bioinformatics. Springer Handbooks of Computational Statistics. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-16345-6_8

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