A probabilistic framework for microarray data analysis: Fundamental probability models and statistical inference

JOURNAL OF THEORETICAL BIOLOGY

卷:264

Article

Ogunnaike, Babatunde A.^1,2  Gelmi, Claudio A.³  Edwards, Jeremy S.⁴ 

[1] Univ Delaware, Dept Chem Engn, Newark, DE 19716 USA

[2] Univ Delaware, Delaware Biotechnol Inst, Newark, DE 19716 USA

[3] Pontificia Univ Catolica Chile, Dept Chem & Bioproc Engn, Santiago, Chile

[4] Univ New Mexico, Canc Res & Treatment Ctr, Hlth Sci Ctr, Albuquerque, NM 87131 USA

关键词: Mixture models;    Poisson distributions;    Gamma distributions;    Beta distributions;    Gene expression;   

DOI  :  10.1016/j.jtbi.2010.02.021

来源: Elsevier

【 摘 要 】

Gene expression studies generate large quantities of data with the defining characteristic that the number of genes (whose expression profiles are to be determined) exceed the number of available replicates by several orders of magnitude. Standard spot-by-spot analysis still seeks to extract useful information for each gene on the basis of the number of available replicates, and thus plays to the weakness of microarrays. On the other hand, because of the data volume, treating the entire data set as an ensemble, and developing theoretical distributions for these ensembles provides a framework that plays instead to the strength of microarrays. We present theoretical results that under reasonable assumptions, the distribution of microarray intensities follows the Gamma model, with the biological interpretations of the model parameters emerging naturally. We subsequently establish that for each microarray data set, the fractional intensities can be represented as a mixture of Beta densities, and develop a procedure for using these results to draw statistical inference regarding differential gene expression. We illustrate the results with experimental data from gene expression studies on Deinococcus radiodurans following DNA damage using cDNA microarrays. (C) 2010 Elsevier Ltd. All rights reserved.

【 授权许可】

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