Please use this identifier to cite or link to this item: http://scholarbank.nus.edu.sg/handle/10635/42142
Title: Identifying differentially expressed genes in time-course microarray experiment without replicate
Authors: Han, X.
Sung, W.-K. 
Feng, L.
Keywords: Differentially expressed gene
PEM
Time course microarray experiment
Issue Date: 2007
Source: Han, X.,Sung, W.-K.,Feng, L. (2007). Identifying differentially expressed genes in time-course microarray experiment without replicate. Journal of Bioinformatics and Computational Biology 5 (2 A) : 281-296. ScholarBank@NUS Repository.
Abstract: Replication of time series in microarray experiments is costly. To analyze time series data with no replicate, many model-specific approaches have been proposed. However, they fail to identify the genes whose expression patterns do not fit the pre-defined models. Besides, modeling the temporal expression patterns is difficult when the dynamics of gene expression in the experiment is poorly understood. We propose a method called Partial Energy ratio for Microarray (PEM) for the analysis of time course microarray data. In the PEM method, we assume the gene expressions vary smoothly in the temporal domain. This assumption is comparatively weak and hence the method is general enough to identify genes expressed in unexpected patterns. To identify the differentially expressed genes, a new statistic is developed by comparing the energies of two convoluted profiles. We further improve the statistic for microarray analysis by introducing the concept of partial energy. The PEM statistic can be easily incorporated into the SAM framework for significance analysis. We evaluated the PEM method with an artificial dataset and two published time course cDNA microarray datasets on yeast. The experimental results show the robustness and the generality of the PEM method in identifying the genes of interest. © 2007 Imperial College Press.
Source Title: Journal of Bioinformatics and Computational Biology
URI: http://scholarbank.nus.edu.sg/handle/10635/42142
ISSN: 02197200
Appears in Collections:Staff Publications

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