Expression-Based Network Biology Identifies Alteration in Key Regulatory Pathways of Type 2 Diabetes and Associated Risk/Complications
Figure 1
Overview of integrated network of gene-gene co-expression and protein-protein interaction information of microarray data (1-6).
(1) All the datasets have been filtered through the defined cut-off of student's t-test and fold change value. The differentially expressed up and down-regulated genes were sorted to make separate lists. (2) The co-expression network was constructed by calculating Pearson's correlation coefficient (r-value) for each gene pair. And the top-ranking genes (r-value varies for the datasets) were used to construct the co-expression network. The corresponding gene sets were processed to generate the protein-protein interaction networks. (3) & (4) Both the networks were merged to get the final large network. The statistical validation of the large network was done using Wilcoxon test. (5) The potential sub-networks were identified using an in-house Java program. (6) Sub-networks were further validated through Wilcoxon test. The other validation parameters like average clustering coefficient, average degree and network density have also been calculated to assess the statistical significance of the network.