Jump to ContentJump to Main Navigation
Show Summary Details
In This Section

Statistical Applications in Genetics and Molecular Biology

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

6 Issues per year


IMPACT FACTOR 2016: 0.646
5-year IMPACT FACTOR: 1.191

CiteScore 2016: 0.94

SCImago Journal Rank (SJR) 2015: 0.954
Source Normalized Impact per Paper (SNIP) 2015: 0.554

Mathematical Citation Quotient (MCQ) 2015: 0.06

Online
ISSN
1544-6115
See all formats and pricing
In This Section
Volume 5, Issue 1 (Mar 2006)

Issues

A New Type of Stochastic Dependence Revealed in Gene Expression Data

Lev Klebanov
  • Department of Probability and Statistics, Charles University
/ Craig Jordan
  • University of Rochester
/ Andrei Yakovlev
  • University of Rochester, Rochester, NY
Published Online: 2006-03-06 | DOI: https://doi.org/10.2202/1544-6115.1189

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

About the article

Published Online: 2006-03-06



Citation Information: Statistical Applications in Genetics and Molecular Biology, ISSN (Online) 1544-6115, DOI: https://doi.org/10.2202/1544-6115.1189. Export Citation

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.

[2]
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.
Log in