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

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Title:	An expert system to identify co-regulated gene groups from time-lagged gene clusters using cell cycle expression data
Authors:	Wu, Li-Ching;Huang, Jhih-Long;Horng, Jorng-Tzong;Huang, Hsien-Da
Contributors:	Department of Bioinformatics
Keywords:	Bioactivity�;�Bioinformatics�;�Data mining�;�Expert systems�;�Gene expression�;�Time series-Biological process;Biological significance;Cell cycle;Co-regulated genes;Experimental conditions;Expression data;Expression patterns;Gene clusters;Gene Expression Data;Microarray data;Multiple cells;P-values;Pearson correlation;Periodic pattern;Regulated genes;Similar pattern;Spearman rank correlation;Time lag;Time-series gene expression data
Date:	2010-03
Issue Date:	2010-04-07 13:21:09 (UTC+0)
Publisher:	Asia University
Abstract:	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. © 2009 Elsevier Ltd. All rights reserved.
Relation:	Expert Systems with Applications 37(3):2202-2213
Appears in Collections:	[生物資訊與醫學工程學系 ] 期刊論文

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