Discovery of two-level modular organization from matched genomic data via joint matrix tri-factorization

With the rapid development of biotechnology, multidimensional genomic data are available for us to study the regulatory associations among multiple levels. Thus, it is essential to develop a tool to identify not only the modular patterns from multiple levels, but also the relationships among these modules. In this study, we adopt a novel non-negative matrix factorization framework (NetNMF) to integrate pairwise genomic data in a network manner. NetNMF could reveal the modules of each dimension and the connections within and between both types of modules. We first demonstrated the effectiveness of NetNMF using a set of simulated data and compared it with two typical NMF methods. Further, we applied it to two different types of pairwise genomic datasets including microRNA (miRNA) and gene expression data from The Cancer Genome Atlas and gene expression and pharmacological data from the Cancer Genome Project. We respectively identified a two-level miRNA-gene module network and a two-level gene-drug module network. Not only have the majority of identified modules significantly functional implications, but also the three types of module pairs have closely biological associations. Thismodule discovery tool provides us comprehensive insights into the mechanisms of how the two levels of molecules cooperate with each other.