Identification of phenotypic networks based on whole transcriptome by comparative network decomposition

Complex diseases are usually caused by the dysfunctions of the molecular system or molecular network rather than individual molecules. Generally, the conventional methods first obtain a disease-associated network based on expression data and then study its biological functions. However, such a network may be only a part of the system facilitating a biological function or may involve in multiple functions. In this paper, we present a computational framework based on an integer programming model, named as comparative network decomposition (CND), to jointly identify optimal structures of significant and moderate phenotypic functions/networks and their optimal combination by integrating gene expression, gene network and gene ontology together. Particularly, CND makes full use of dysfunctional information, e.g. both strong and weak changes on gene expressions and correlations, to extract various phenotypic networks, where one phenotypic network just corresponds to a specific biological function. A synthetic example clearly suggests that CND can identify multiple types of the disease-related phenotypic networks, rather than conventional approaches only exact significant phenotypic networks. As a proof-of-concept study to real data, CND is further used to identify the significant and moderate phenotypic networks for discriminating two different but associated diseases, e.g. subtypes of diabetes. In the comparison of type 1 and type 2 diabetes, the moderate and significant phenotypic networks can capture the disease-related biological functions and their corresponding networks. Therefore, CND is actually a powerful bioinformatics tool, which can investigate phenotype-associated genes and networks in a whole transcriptome and function-centered manner, and the comparative study of complex diseases with other works also demonstrates its effectiveness.