An expert system to identify co-regulated gene groups from time-lagged gene clusters using cell cycle expression data

Authors:
Li-Ching Wu;Jhih-Long Huang;Jorng-Tzong Horng;Hsien-Da Huang
Affiliations:
Institute of System Biology and Bioinformatics, National Central University, Taiwan;Department of Computer Science and Information Engineering, National Central University, Taiwan;Department of Computer Science and Information Engineering, National Central University, Taiwan and Department of Bioinformatics, Asia University, Taiwan;Institute of Bioinformatics, National Chiao-Tung University, Taiwan
Venue:
Expert Systems with Applications: An International Journal
Year:
2010

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Abstract

Motivation: The analysis of time series gene expression data can provide us with the opportunity to find co-regulated genes that show a similar expression patterns under a contiguous subset of experimental conditions. However, these co-regulated genes may behave almost independently under other conditions. Furthermore, the similarity in the expression pattern might be time-shifted. In that case, we need to be concerned with grouping genes that share similar expression patterns under a contiguous subset of conditions and where the similarity in expression pattern might have time lags. In addition, to be considered a time-shifted similar pattern, because co-regulated genes in a biological process may show a periodic pattern in their cell cycle expression, we also should group genes with periodic similar patterns over multiple cell cycles. If this is carried out, we can regard such grouped genes as cell-cycle regulated genes. Results: We propose a method that follows the q-cluster concept [Ji, L., & Tan, K. L. (2005). Identifying time-lagged gene clusters using gene expression data. Bioinformatics, 21(4), 509-516] and further advances this approach towards the identification of cell-cycle regulated genes using cell cycle microarray data. We used our method to cluster a microarray time series of yeast genes to assess the statistically biological significance of the obtained clusters we used the p-value obtained from the hypergeometric distribution. We found that several clusters provided findings suggesting a TF-target relationship. In order to test whether our method could group related genes that other methods have found difficult to group, we compared our method with other measures such as Spearman Rank Correlation and Pearson Correlation. The results of the comparison demonstrate that our method indeed could group known related genes that these measures regard as having only a weak association.