Nov 10, 2010

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

PloS One
Junwei WangTieliu Shi

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 ...Continue Reading

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  • Citations35

References

Mentioned in this Paper

TP53 gene
SHOX gene
Neuro-Oncological Ventral Antigen 2
Biochemical Pathway
Beta-Actin
Fluctuation
Microarray Analysis
Synovial Sarcoma
Fibroblast Growth Factor Receptor 1
DVL3 gene

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