Abstract
BackgroundBoth 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.ResultsIn 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.ConclusionsBy 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.DECODE is available at the Comprehensive R Archive Network (CRAN): http://cran.r-project.org/web/packages/decode/index.html.Electronic supplementary materialThe online version of this article (doi:10.1186/s12859-015-0582-4) contains supplementary material, which is available to authorized users.
Highlights
Both differential expression (DE) and differential co-expression (DC) analyses are appreciated as useful tools in understanding gene regulation related to complex diseases
DECODE consists of four steps: (1) calculating differential expression (DE), (2) calculating differential co-expression (DC), (3) selecting thresholds to define high or low values of DC and DE variables based on chi-square maximization, and statistically evaluating partitions divided by the thresholds, (4) comparing functional relevance of genes categorized into the partitions of high DC, high DE, or both
After the best associated gene set was identified for each significant partition and the corresponding functional information was obtained, we evaluated whether the functional information based on the high DC and high DE partition (HDC_HDE) criteria was higher than that based on individual high DC (HDC) or High DE (HDE) criteria
Summary
Both differential expression (DE) and differential co-expression (DC) analyses are appreciated as useful tools in understanding gene regulation related to complex diseases. DE analysis considers each gene individually and their potential interactions are ignored Biomolecules such as genes, RNAs and proteins do not act alone; they coordinate as functional modules in biological processes and signalling pathways. They physically aggregate to form nano-machineries such as ribosomes, chaperone and spliceosome to carry out specific functions in the cells [9]. To address the gene independence model in DE analysis, approaches based on gene co-expression, gene sets, and gene clustering have been emerged They were utilized to explore patterns of RNA expression, and intrinsic gene interactions [10,11,12,18,19,20,21,22,23,24,25]
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