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Bioinformatics Vol. 19 no. 17 2003
pages 2254-2262
© 2003 Oxford University Press

Normality of oligonucleotide microarray data and implications for parametric statistical analyses

Peter J. Giles and David Kipling *

Department of Pathology, University of Wales College of Medicine, Heath Park, Cardiff CF14 4XN, UK

Motivation: Experimental limitations have resulted in the popularity of parametric statistical tests as a method for identifying differentially regulated genes in microarray data sets. However, these tests assume that the data follow a normal distribution. To date, the assumption that replicate expression values for any gene are normally distributed, has not been critically addressed for Affymetrix GeneChip data.

Results: The normality of the expression values calculated using four different commercial and academic software packages was investigated using a data set consisting of the same target RNA applied to 59 human Affymetrix U95A GeneChips using a combination of statistical tests and visualization techniques. For the majority of probe sets obtained from each analysis suite, the expression data showed a good correlation with normality. The exception was a large number of low-expressed genes in the data set produced using Affymetrix Microarray Suite 5.0, which showed a striking non-normal distribution. In summary, our data provide strong support for the application of parametric tests to GeneChip data sets without the need for data transformation.

Contact: KiplingD{at}cardiff.ac.uk

* To whom correspondence should be addressed.


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