Generalization of DNA microarray dispersion properties: microarray equivalent of t-distribution

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Generalization of DNA microarray dispersion properties: microarray equivalent of t-distribution
J P Novak; Seon-Young Kim; J Xu; O Modlich; D J Volsky; D Honys; J L Slonczewski; D A Bell; F R Blattner; E Blumwald; M Boerma; M Cosio; Z Gatalica; M Hajduch; J Hidalgo; R R McInnes; M C Miller; M Penkowa; M S Rolph; J Sottosanto; R St-Arnaud; M J Szego; D Twell; C Wang
Bibliographic Citation
Biology Direct, vol. 1, pp. 27-27
Publication Year
Background: DNA microarrays are a powerful technology that can provide a wealth of gene expression data for disease studies, drug development, and a wide scope of other investigations. Because of the large volume and inherent variability of DNA microarray data, many new statistical methods have been developed for evaluating the significance of the observed differences in gene expression. However, until now little attention has been given to the characterization of dispersion of DNA microarray data. Results: Here we examine the expression data obtained from 682 Affymetrix GeneChips® with 22 different types and we demonstrate that the Gaussian (normal) frequency distribution is characteristic for the variability of gene expression values. However, typically 5 to 15% of the samples deviate from normality. Furthermore, it is shown that the frequency distributions of the difference of expression in subsets of ordered, consecutive pairs of genes (consecutive samples) in pair-wise comparisons of replicate experiments are also normal. We describe a consecutive sampling method, which is employed to calculate the characteristic function approximating standard deviation and show that the standard deviation derived from the consecutive samples is equivalent to the standard deviation obtained from individual genes. Finally, we determine the boundaries of probability intervals and demonstrate that the coefficients defining the intervals are independent of sample characteristics, variability of data, laboratory conditions and type of chips. These coefficients are very closely correlated with Student's t-distribution. Conclusion: In this study we as certained that the non-systematic variations possess Gaussian distribution, determined the probability intervals and demonstrated that the Kα coefficients defining these intervals are invariant; these coefficients offer a convenient universal measure of dispersion of data. The fact that the Kα distributions are so close to t-distribution and independent of conditions and type of arrays suggests that the quantitative data provided by Affymetrix technology give "true" representation of physical procees, involved in measurement of RNA abundance.
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