Significance analysis of time-course gene expression profiles

Authors:
Fang-Xiang Wu
Affiliations:
Department of Mechanical Engineering and Division of Biomedical Engineering, University of Saskatchewan, Saskatoon, SK, Canada
Venue:
ISBRA'07 Proceedings of the 3rd international conference on Bioinformatics research and applications
Year:
2007

Citing 4
Cited 1

Bioinformatics: the machine learning approach

Bioinformatics: the machine learning approach
Analyzing Gene Expression Time-Courses

IEEE/ACM Transactions on Computational Biology and Bioinformatics (TCBB)
Analyzing time series gene expression data

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Use of within-array replicate spots for assessing differential expression in microarray experiments

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Dynamic-model-based method for selecting significantly expressed genes from time-course expression profiles

IEEE Transactions on Information Technology in Biomedicine

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Abstract

This paper proposes a statistical method for significance analysis of time-course gene expression profiles, called SATgene. The SATgene models time-dependent gene expression profiles by autoregressive equations plus Gaussian noises, and time-independent gene expression profiles by constant numbers plus Gaussian noises. The statistical F-testing for regression analysis is used to calculate the confidence probability (significance level) that a time-course gene expression profile is not time-independent. The user can use this confidence probability to select significantly expressed genes from a time-course gene expression dataset. Both one synthetic dataset and one biological dataset were employed to evaluate the performance of the SATgene, compared to traditional gene selection methods: the pairwise R-fold change method and the standard deviation method. The results show that the SATgene outperforms the traditional methods.