Abstract
More and more studies have shown that many complex diseases are contributed jointly by alterations of numerous genes. Genes often coordinate together as a functional biological pathway or network and are highly correlated. Differential coexpression analysis, as a more comprehensive technique to the differential expression analysis, was raised to research gene regulatory networks and biological pathways of phenotypic changes through measuring gene correlation changes between disease and normal conditions. In this paper, we propose a gene differential coexpression analysis algorithm in the level of gene sets and apply the algorithm to a publicly available type 2 diabetes (T2D) expression dataset. Firstly, we calculate coexpression biweight midcorrelation coefficients between all gene pairs. Then, we select informative correlation pairs using the “differential coexpression threshold” strategy. Finally, we identify the differential coexpression gene modules using maximum clique concept and k-clique algorithm. We apply the proposed differential coexpression analysis method on simulated data and T2D data. Two differential coexpression gene modules about T2D were detected, which should be useful for exploring the biological function of the related genes.
Highlights
DNA microarray has been widely used as measurement tools in gene expression data analysis [1,2,3,4]
The analysis of gene expression data can be divided into three levels: first, analysis of the expression level of individual genes, determining its function based on gene expression level changes under different experimental conditions: for example, the tumor type specific genes are identified according to the significance of difference in gene expression using the statistical hypothesis testing analysis method; second, study of gene interaction and coregulation through the combination of genes and grouping; and, third, an attempt to deduce the potential gene regulatory networks mechanism and explain the observed gene expression data
We proposed a new approach for gene differential coexpression analysis in gene modules level based on combining biweight midcorrelation, differential coexpression threshold strategy, and maximum clique concept and k-clique analysis
Summary
DNA microarray has been widely used as measurement tools in gene expression data analysis [1,2,3,4]. Among the microarray data analysis methods, gene differential expression analysis is one of the most widely used types of analysis for disease research. The traditional pathogenicity genes selection methods based on gene expression data treat each gene individually and interaction between them is not considered. Genes and their protein products do not perform their functions in isolation [5, 6], but in cooperation. Expressed genes selection methods often focus only on the size of the single genes and BioMed Research International the relationship of individual genes and disease, ignoring a plurality of pathogenic genes of the complex disease as a gene module with disease related, as well as within the module gene [7]
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