Module Based Differential Coexpression Analysis Method for Type 2 Diabetes.
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.
School of Electronics and Information Engineering, Tongji University, Shanghai 201804, China.
Lin
L
Yuan
College of Electrical Engineering and Automation, Anhui University, Hefei 230601, China.
Chun-Hou
CH
Zheng
Institute of Health Sciences, Anhui University, Hefei 230601, China.
Jun-Feng
JF
Xia
School of Electronics and Information Engineering, Tongji University, Shanghai 201804, China.
De-Shuang
DS
Huang
D000465:Algorithms; D003924:Diabetes Mellitus, Type 2 / Q000235:genetics* / Q000473:pathology; D020869:Gene Expression Profiling / Q000379:methods*; D005786:Gene Expression Regulation / Q000235:genetics; D053263:Gene Regulatory Networks; D006801:Humans; D053858:Metabolic Networks and Pathways / Q000235:genetics; D020411:Oligonucleotide Array Sequence Analysis