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International Journal of Plant Genomics
Volume 2008 (2008), Article ID 584360, 16 pages
Review Article

Statistical Analysis of Efficient Unbalanced Factorial Designs for Two-Color Microarray Experiments

Department of Animal Science, College of Agriculture and Natural Resources, Michigan State University, East Lansing, MI 48824-1225, USA

Received 2 November 2007; Revised 22 January 2008; Accepted 25 April 2008

Academic Editor: Chunguang Du

Copyright © 2008 Robert J. Tempelman. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.


Experimental designs that efficiently embed a fixed effects treatment structure within a random effects design structure typically require a mixed-model approach to data analyses. Although mixed model software tailored for the analysis of two-color microarray data is increasingly available, much of this software is generally not capable of correctly analyzing the elaborate incomplete block designs that are being increasingly proposed and used for factorial treatment structures. That is, optimized designs are generally unbalanced as it pertains to various treatment comparisons, with different specifications of experimental variability often required for different treatment factors. This paper uses a publicly available microarray dataset, as based upon an efficient experimental design, to demonstrate a proper mixed model analysis of a typical unbalanced factorial design characterized by incomplete blocks and hierarchical levels of variability.