scholarly journals SUBSPACE DIFFERENTIAL COEXPRESSION ANALYSIS:

2009 ◽  
pp. 145-156 ◽  
Author(s):  
GANG FANG ◽  
RUI KUANG ◽  
GAURAV PANDEY ◽  
MICHAEL STEINBACH ◽  
CHAD L. MYERS ◽  
...  
Keyword(s):  
Coexpression Analysis ◽  
Differential Coexpression ◽  
Differential Coexpression Analysis

scholarly journals Module Based Differential Coexpression Analysis Method for Type 2 Diabetes

2015 ◽  
Vol 2015 ◽  
pp. 1-8 ◽  
Author(s):  
Lin Yuan ◽  
Chun-Hou Zheng ◽  
Jun-Feng Xia ◽  
De-Shuang Huang
Keyword(s):  
Type 2 Diabetes ◽  
Differential Expression Analysis ◽  
Maximum Clique ◽  
Analysis Method ◽  
Threshold Strategy ◽  
Coexpression Analysis ◽  
Differential Coexpression ◽  
Differential Coexpression Analysis ◽  
Gene Modules

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 andk-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.

scholarly journals Identification of Transcriptional Modules and Key Genes in Chickens Infected with Salmonella enterica Serovar Pullorum Using Integrated Coexpression Analyses

2017 ◽  
Vol 2017 ◽  
pp. 1-12 ◽  
Author(s):  
Bao-Hong Liu ◽  
Jian-Ping Cai
Keyword(s):  
Salmonella Enterica ◽  
Differential Expression Analysis ◽  
Molecular Response ◽  
Cytoskeleton Organization ◽  
Differential Coexpression ◽  
Differential Coexpression Analysis ◽  
Salmonella Enterica Serovar Pullorum ◽  
Causes Of Mortality ◽  
Coexpressed Genes ◽  
G Protein Coupled

Salmonella enterica Pullorum is one of the leading causes of mortality in poultry. Understanding the molecular response in chickens in response to the infection by S. enterica is important in revealing the mechanisms of pathogenesis and disease progress. There have been studies on identifying genes associated with Salmonella infection by differential expression analysis, but the relationships among regulated genes have not been investigated. In this study, we employed weighted gene coexpression network analysis (WGCNA) and differential coexpression analysis (DCEA) to identify coexpression modules by exploring microarray data derived from chicken splenic tissues in response to the S. enterica infection. A total of 19 modules from 13,538 genes were associated with the Jak-STAT signaling pathway, the extracellular matrix, cytoskeleton organization, the regulation of the actin cytoskeleton, G-protein coupled receptor activity, Toll-like receptor signaling pathways, and immune system processes; among them, 14 differentially coexpressed modules (DCMs) and 2,856 differentially coexpressed genes (DCGs) were identified. The global expression of module genes between infected and uninfected chickens showed slight differences but considerable changes for global coexpression. Furthermore, DCGs were consistently linked to the hubs of the modules. These results will help prioritize candidate genes for future studies of Salmonella infection.

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