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Statistical Applications in Genetics and Molecular Biology

Editor-in-Chief: Stumpf, Michael P.H.

6 Issues per year

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A New Type of Stochastic Dependence Revealed in Gene Expression Data

Lev Klebanov1 / Craig Jordan2 / Andrei Yakovlev3

1Department of Probability and Statistics, Charles University

2University of Rochester

3University of Rochester, Rochester, NY

Citation Information: Statistical Applications in Genetics and Molecular Biology. Volume 5, Issue 1, ISSN (Online) 1544-6115, DOI: 10.2202/1544-6115.1189, March 2006

Publication History

Published Online:

Modern methods of microarray data analysis are biased towards selecting those genes that display the most pronounced differential expression. The magnitude of differential expression does not necessarily indicate biological significance and other criteria are needed to supplement the information on differential expression. Three large sets of microarray data on childhood leukemia were analyzed by an original method introduced in this paper. A new type of stochastic dependence between expression levels in gene pairs was deciphered by our analysis. This modulation-like unidirectional dependence between expression signals arises when the expression of a ``gene-modulator'' is stochastically proportional to that of a ``gene-driver''. A total of more than 35% of all pairs formed from 12550 genes were conservatively estimated to belong to this type. There are genes that tend to form Type A relationships with the overwhelming majority of genes. However, this picture is not static: the composition of Type A gene pairs may undergo dramatic changes when comparing two phenotypes. The ability to identify genes that act as ``modulators'' provides a potential strategy of prioritizing candidate genes.

Keywords: gene expression; microarray analysis; stochastic dependence

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