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.
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Wu, FX. (2007). Significance Analysis of Time-Course Gene Expression Profiles. In: Măndoiu, I., Zelikovsky, A. (eds) Bioinformatics Research and Applications. ISBRA 2007. Lecture Notes in Computer Science(), vol 4463. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-72031-7_2
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DOI: https://doi.org/10.1007/978-3-540-72031-7_2
Publisher Name: Springer, Berlin, Heidelberg
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