Nucleic Acids Research Advance Access originally published online on July 17, 2009
Nucleic Acids Research 2009 37(17):5610-5618; doi:10.1093/nar/gkp573
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Nucleic Acids Research, 2009, Vol. 37, No. 17 5610-5618
© 2009 The Author(s)
This is an Open Access article distributed under the terms of the Creative Commons Attribution Non-Commercial License (http://creativecommons.org/licenses/by-nc/2.0/uk/) which permits unrestricted non-commercial use, distribution, and reproduction in any medium, provided the original work is properly cited.
Computational Biology |
Importance of randomization in microarray experimental designs with Illumina platforms
1The Jackson Laboratory, Bar Harbor, ME 04609, USA, 2Institut de Recherches Cliniques, Montreal, QC, Canada and 3Department of Nutrition, University of North Carolina, Chapel Hill, NC 27599, USA
*To whom correspondence should be addressed. Tel: +1 207 288 6715; Fax: +1 207 288 6847; Email: ricardo.a.verdugo{at}gmail.com.
Received March 4, 2009. Revised June 3, 2009. Accepted June 22, 2009.
Measurements of gene expression from microarray experiments are highly dependent on experimental design. Systematic noise can be introduced into the data at numerous steps. On Illumina BeadChips, multiple samples are assayed in an ordered series of arrays. Two experiments were performed using the same samples but different hybridization designs. An experiment confounding genotype with BeadChip and treatment with array position was compared to another experiment in which these factors were randomized to BeadChip and array position. An ordinal effect of array position on intensity values was observed in both experiments. We demonstrate that there is increased rate of false-positive results in the confounded design and that attempts to correct for confounded effects by statistical modeling reduce power of detection for true differential expression. Simple analysis models without post hoc corrections provide the best results possible for a given experimental design. Normalization improved differential expression testing in both experiments but randomization was the most important factor for establishing accurate results. We conclude that lack of randomization cannot be corrected by normalization or by analytical methods. Proper randomization is essential for successful microarray experiments.