Fig. 1
From: Ant colony optimization for the identification of dysregulated gene subnetworks from expression data
An overview of the proposed workflow which takes as input a gene expression dataset and an adjacency matrix representing gene interconnections. A built-in preprocessing step generates distance and rank correlation matrices, in addition to t-statistic scores reflecting the magnitude of differential expressions of gene in each module. Then, for each gene, the proposed Ant Colony Optimization approach identifies the most significantly dysregulated module, based on \(p^{fisher}\) which is derived from \(p^S\), the p value for the sphere of influence of a center gene on its neighbors in a module, and \(p^D\), the p value for the decay of differential expression within that module