Please use this identifier to cite or link to this item: http://hdl.handle.net/10397/7533
Title: DECODE: An integrated differential co-expression and differential expression analysis of gene expression data
Authors: Lui, TWH
Tsui, NBY
Chan, LWC 
Wong, CSC 
Siu, PMF 
Yung, BYM 
Issue Date: 2015
Source: BMC bioinformatics, 2015, v. 16, no. 1, 182, p. 1-15
Abstract: Background: Both differential expression (DE) and differential co-expression (DC) analyses are appreciated as useful tools in understanding gene regulation related to complex diseases. The performance of integrating DE and DC, however, remains unexplored. Results: In this study, we proposed a novel analytical approach called DECODE (Differential Co-expression and Differential Expression) to integrate DC and DE analyses of gene expression data. DECODE allows one to study the combined features of DC and DE of each transcript between two conditions. By incorporating information of the dependency between DC and DE variables, two optimal thresholds for defining substantial change in expression and co-expression are systematically defined for each gene based on chi-square maximization. By using these thresholds, genes can be categorized into four groups with either high or low DC and DE characteristics. In this study, DECODE was applied to a large breast cancer microarray data set consisted of two thousand tumor samples. By identifying genes with high DE and high DC, we demonstrated that DECODE could improve the detection of some functional gene sets such as those related to immune system, metastasis, lipid and glucose metabolism. Further investigation on the identified genes and the associated functional pathways would provide an additional level of understanding of complex disease mechanism. Conclusions: By complementing the recent DC and the traditional DE analyses, DECODE is a valuable methodology for investigating biological functions of genes exhibiting disease-associated DE and DC combined characteristics, which may not be easily revealed through DC or DE approach alone.
Publisher: BioMed Central
Journal: BMC bioinformatics 
EISSN: 1471-2105
DOI: 10.1186/s12859-015-0582-4
Rights: © 2015 Lui et al.; licensee BioMed Central. This is an Open Access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/4.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly credited. The Creative Commons Public DomainDedication waiver (http://creativecommons.org/publicdomain/zero/1.0/) applies to the data made available in this article, unless otherwise stated.
The following publication Lui, T. W. H., Tsui, N. B. Y., Chan, L. W. C., Wong, C. S. C., Siu, P. M. F., & Yung, B. Y. M. (2015). DECODE: An integrated differential co-expression and differential expression analysis of gene expression data. BMC Bioinformatics, 16(1), 182, 1-15 is available at https://dx.doi.org/10.1186/s12859-015-0582-4
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