Jump to ContentJump to Main Navigation

Statistical Applications in Genetics and Molecular Biology

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

6 Issues per year

IMPACT FACTOR increased in 2014: 1.127
5-year IMPACT FACTOR: 1.537
Rank 47 out of 122 in category Statistics & Probability in the 2014 Thomson Reuters Journal Citation Report/Science Edition

SCImago Journal Rank (SJR) 2014: 0.740
Source Normalized Impact per Paper (SNIP) 2014: 0.470
Impact per Publication (IPP) 2014: 0.926

Mathematical Citation Quotient (MCQ) 2014: 0.17


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

Citing Articles

Here you can find all Crossref-listed publications in which this article is cited. If you would like to receive automatic email messages as soon as this article is cited in other publications, simply activate the “Citation Alert” on the top of this page.

Donghyeon Yu, Johan Lim, Feng Liang, Kyunga Kim, Byung Soo Kim, and Woncheol Jang
Computational Statistics & Data Analysis, 2012, Volume 56, Number 3, Page 510

Comments (0)

Please log in or register to comment.