VD-analysis: a dynamic network framework for analyzing disease progressions

The progression of a disease associates with changes in genomic activity, but it remains a challenge to screen genetic biomarkers for clinical applications. The disease progression, in dynamic network methods (DNM), can be analogous to an animated film composed of discrete frames, where each frame represents a temporary state of the time-varying gene-gene interaction network. The major shortage therein is that the transition between two neighboring temporary states was beyond investigation. Here, we develop an updated computational methodology named after VD-analysis. Because single-gene biomarkers were not approved capable of representing a complex biological process, we firstly introduce V-structure - a gene module composed of three genes and two interactions among them - and define it as unit module. We then identify the perturbed pathways that mark the disease progression, followed with the V-structures identified which drive the pathway perturbations. Such driver V-structures can be taken as eligible biomarkers for clinical applications. To test the feasibility of this method, we apply it to a time course dataset of gene expression related to mouse type-II diabetes (T2D). Result indicates that the whole process of T2D is exactly divided into 3 stages and that the driver V-structures inferred for each stage are qualified biomarkers. In summary, our method contributes to the description of dynamic disease progression and the V-structure biomarkers facilitate the treatments of disease.