DCNA: A tool for differential co-expression network analysis of gene expression data

A method was developed based on comparing gene expressions of tissues in two context-specific conditions (i.e. disease and normal samples or chemical applied and not applied samples), and differential-co-expression analysis was performed based on comparing the correlation relationships between the expression levels of genes in both states. Gene clusters (modules) with high interaction with each other in the created network structures were obtained. In this thesis, the web-based tool named DCNA was developed by using the new algorithm belonging to our research group. Codes and supplementary data are available in http://www.github.com/tyasird/differential-coexpression-network-analysis. Besides, the most important point of the study, a user-friendly web-based tool was designed, where researchers can easily analyze gene expression data (microarray & RNA-seq) without wasting time with coding. DCNA is accessible at http://dcna.computationalbiology.org/. Herewith method, gastric cancer microarray datasets were used as a case study and a cancer-specific correlation network was constructed. Significant gene modules were determined with the help of the DCNA web-based tool. We assert that disease-specific candidate gene modules found by the DCNA web-based tool will help to develop strategies for the prognosis and treatment of the disease.