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Novak JP, Kim SY, Xu J, et al. Generalization of DNA microarray dispersion properties: microarray equivalent of t-distribution. Biol Direct. 2006;1:27doi: 10.1186/1745-6150-1-27.
Novak, J. P., Kim, S. Y., Xu, J., Modlich, O., Volsky, D. J., Honys, D., Slonczewski, J. L., Bell, D. A., Blattner, F. R., Blumwald, E., Boerma, M., Cosio, M., Gatalica, Z., Hajduch, M., Hidalgo, J., McInnes, R. R., Miller, M. C., Penkowa, M., Rolph, M. S., Sottosanto, J., St-Arnaud, R., Szego, M. J., Twell, D., & Wang, C. (2006). Generalization of DNA microarray dispersion properties: microarray equivalent of t-distribution. Biology direct, 127. https://doi.org/10.1186/1745-6150-1-27
Novak, Jaroslav P, et al. "Generalization of DNA microarray dispersion properties: microarray equivalent of t-distribution." Biology direct vol. 1 (2006): 27. doi: https://doi.org/10.1186/1745-6150-1-27
Novak JP, Kim SY, Xu J, Modlich O, Volsky DJ, Honys D, Slonczewski JL, Bell DA, Blattner FR, Blumwald E, Boerma M, Cosio M, Gatalica Z, Hajduch M, Hidalgo J, McInnes RR, Miller MC, Penkowa M, Rolph MS, Sottosanto J, St-Arnaud R, Szego MJ, Twell D, Wang C. Generalization of DNA microarray dispersion properties: microarray equivalent of t-distribution. Biol Direct. 2006 Sep 07;1:27. doi: 10.1186/1745-6150-1-27. PMID: 16959036; PMCID: PMC1586001.
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Biol Direct. 2006 Sep 07;1:27. doi: 10.1186/1745-6150-1-27.

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

Biology direct

Jaroslav P Novak, Seon-Young Kim, Jun Xu, Olga Modlich, David J Volsky, David Honys, Joan L Slonczewski, Douglas A Bell, Fred R Blattner, Eduardo Blumwald, Marjan Boerma, Manuel Cosio, Zoran Gatalica, Marian Hajduch, Juan Hidalgo, Roderick R McInnes, Merrill C Miller, Milena Penkowa, Michael S Rolph, Jordan Sottosanto, Rene St-Arnaud, Michael J Szego, David Twell, Charles Wang

Affiliations

  1. McGill University and Genome Québec Innovation Centre, 740 Docteur Penfield Avenue, Montreal, Québec, H3A 1A4, Canada. [email protected]

PMID: 16959036 PMCID: PMC1586001 DOI: 10.1186/1745-6150-1-27

Abstract

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 ascertained that the non-systematic variations possess Gaussian distribution, determined the probability intervals and demonstrated that the K(alpha) coefficients defining these intervals are invariant; these coefficients offer a convenient universal measure of dispersion of data. The fact that the K(alpha) 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 processes, involved in measurement of RNA abundance.

REVIEWERS: This article was reviewed by Yoav Gilad (nominated by Doron Lancet), Sach Mukherjee (nominated by Sandrine Dudoit) and Amir Niknejad and Shmuel Friedland (nominated by Neil Smalheiser).

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