Normality of oligonucleotide microarray data and implications for parametric statistical analyses.

Peter J Giles; David Kipling

doi:10.1093/bioinformatics/btg311

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Normality of oligonucleotide microarray data and implications for parametric statistical analyses.

Peter J Giles·David Kipling

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Abstract

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.

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Algorithms Data Interpretation, Statistical Gene Expression Profiling Genetic Variation Models, Genetic Models, Statistical Oligonucleotide Array Sequence Analysis Reproducibility of Results Sample Size Sensitivity and Specificity Software Software Validation Comparative Study Evaluation Study Journal Article Research Support, Non-U.S. Gov't Validation Study

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Peter J Giles, David Kipling. 2003-11-22. Normality of oligonucleotide microarray data and implications for parametric statistical analyses.. https://doi.org/10.1093/bioinformatics%2Fbtg311

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