A single-shot determination of differential gene network on multiple disease subtypes

Differential gene expressional network determines the prominent genes under altered phenotypes. Traditional approach requires n(n-2)/2 comparisons for n phenotypes. We present a direct method for determining the differential network under multiple phenotypes. We explore the non-discrete nature of gene expression as a pattern in fuzzy rough set. An edge between a pair of genes represents positive region of fuzzy similarity relation upon a phenotypic change. We apply a weight ranking formula and obtain a directed ranked network; we term it as Phenotype Interweaved Network. Nodes with large in-degree connectivity bubble up as significant genes under respective phenotypic changes. We test the method on datasets of six diseases and achieve good corroboration with results of previous studies in two-step approach. The subgraphs of isolated genes achieve good significance upon validation through information theoretic approach. Top ranking genes determined in all our case studies have parity with genes reported by wet-lab tests.