Normality of oligonucleotide microarray data and implications for parametric statistical analyses

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 expr...

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Bibliographic Details
Published in:Bioinformatics 2003-11, Vol.19 (17), p.2254-2262
Main Authors: Giles, Peter J., Kipling, David
Format: Article
Language:English
Subjects:
Algorithms
Biological and medical sciences
Data Interpretation, Statistical
Fundamental and applied biological sciences. Psychology
Gene Expression Profiling - methods
General aspects
Genetic Variation
Mathematics in biology. Statistical analysis. Models. Metrology. Data processing in biology (general aspects)
Models, Genetic
Models, Statistical
Oligonucleotide Array Sequence Analysis - methods
Oligonucleotide Array Sequence Analysis - standards
Reproducibility of Results
Sample Size
Sensitivity and Specificity
Software
Software Validation
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Summary: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.
ISSN:1367-4803
1460-2059
1367-4811
DOI:10.1093/bioinformatics/btg311