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Biostatistics Advance Access originally published online on March 10, 2009
Biostatistics 2009 10(3):446-450; doi:10.1093/biostatistics/kxp003
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© The Author 2009. Published by Oxford University Press. All rights reserved. For permissions, please e-mail: journals.permissions@oxfordjournals.org.

A note on oligonucleotide expression values not being normally distributed

Johanna Hardin*

Department of Mathematics, Pomona College, 610 North College Avenue, Claremont, CA 91711, USA
jo.hardin{at}pomona.edu

Jason Wilson

Department of Mathematics, Biola University, La Mirada, CA 90639, USA
jason.wilson{at}biola.edu

* To whom correspondence should be addressed.

Novel techniques for analyzing microarray data are constantly being developed. Though many of the methods contribute to biological discoveries, inability to properly evaluate the novel techniques limits their ability to advance science. Because the underlying distribution of microarray data is unknown, novel methods are typically tested against the assumed normal distribution. However, microarray data are not, in fact, normally distributed, and assuming so can have misleading consequences. Using an Affymetrix technical replicate spike-in data set, we show that oligonucleotide expression values are not normally distributed for any of the standard methods for calculating expression values. The resulting data tend to have a large proportion of skew and heavy tailed genes. Additionally, we show that standard methods can give unexpected and misleading results when the data are not well approximated by the normal distribution. Robust methods are therefore recommended when analyzing microarray data. Additionally, new techniques should be evaluated with skewed and/or heavy-tailed data distributions.

Keywords: Affymetrix; Distributions; Microarray data; Nonnormality

Received July 15, 2008; revised November 12, 2008; accepted for publication January 27, 2009.


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