Display Settings:

Format

Send to:

Choose Destination
See comment in PubMed Commons below
PLoS One. 2010 Oct 29;5(10):e13721. doi: 10.1371/journal.pone.0013721.

Systematical detection of significant genes in microarray data by incorporating gene interaction relationship in biological systems.

Author information

  • 1The Center for Bioinformatics and Computational Biology, The Institute of Biomedical Sciences, The School of Life Sciences, East China Normal University, Shanghai, China.

Abstract

Many methods, including parametric, nonparametric, and Bayesian methods, have been used for detecting differentially expressed genes based on the assumption that biological systems are linear, which ignores the nonlinear characteristics of most biological systems. More importantly, those methods do not simultaneously consider means, variances, and high moments, resulting in relatively high false positive rate. To overcome the limitations, the SWang test is proposed to determine differentially expressed genes according to the equality of distributions between case and control. Our method not only latently incorporates functional relationships among genes to consider nonlinear biological system but also considers the mean, variance, skewness, and kurtosis of expression profiles simultaneously. To illustrate biological significance of high moments, we construct a nonlinear gene interaction model, demonstrating that skewness and kurtosis could contain useful information of function association among genes in microarrays. Simulations and real microarray results show that false positive rate of SWang is lower than currently popular methods (T-test, F-test, SAM, and Fold-change) with much higher statistical power. Additionally, SWang can uniquely detect significant genes in real microarray data with imperceptible differential expression but higher variety in kurtosis and skewness. Those identified genes were confirmed with previous published literature or RT-PCR experiments performed in our lab.

PMID:
21060778
[PubMed - indexed for MEDLINE]
PMCID:
PMC2966410
Free PMC Article

Images from this publication.See all images (3)Free text

Figure 1
Figure 2
Figure 3
PubMed Commons home

PubMed Commons

0 comments
How to join PubMed Commons

    Supplemental Content

    Full text links

    Icon for Public Library of Science Icon for PubMed Central
    Loading ...
    Write to the Help Desk