Finding Significantly Expressed genes from time-course expression profiles

Authors:
Fang-Xiang Wu;Zhonghang Xia;Lei Mu
Affiliations:
Department of Mechanical Engineering, Division of Biomedical Engineering, University of Saskatchewan, 57 Campus Dr., Saskatoon, SK, S7N 5A9, Canada.;Department of Computer Science, Western Kentucky University, 1906 College Heights Blvd, #11076, Bowling Green, KY 42101-1076, USA.;Department of Mechanical Engineering, University of Saskatchewan, 57 Campus Dr., Saskatoon, SK, S7N 5A9, Canada
Venue:
International Journal of Bioinformatics Research and Applications
Year:
2009

Citing 4
Cited 0

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Abstract

This paper proposes a statistical method for finding Significantly Expressed (SE) genes from time-course expression. SE genes are time-dependent while non-SE genes are time-independent. This method models time-dependent gene expression profiles by autoregressive equations plus Gaussian noises, and time-independent ones by Gaussian noises. The statistical F-testing is used to calculate the probability (p-value) that a profile is time-independent. Both a synthetic dataset and a biological dataset were employed to evaluate the performance of this method, measured by the False Discovery Rate (FDR) and the False Non-discovery Rate (FNR). Results show that the proposed method outperforms traditional methods.