molnet

Networks provide a means to incorporate molecular interactions into reasoning, but on the omics-level, they are currently mainly used to combine genomic and proteomic information. We here present a novel network analysis pipeline that enables integrative analysis of multi-omics data including metabolomics. It allows for comparative conclusions between two different conditions, such as tumor subgroups, healthy vs. disease, or generally control vs. perturbed. Our approach focuses on interactions and their strength instead of on node properties and includes molecules with low abundance and unknown function. We use correlation-induced networks that are reduced and combined to form heterogeneous, multi-omics molecular networks. Prior information such as metabolite-protein interactions are incorporated. A semi-local, path-based integration step denoises the network and ensures integrative conclusions. As case studies, we investigate differential drug response in breast cancer tumor datasets providing proteomics, transcriptomics, phospho-proteomics and metabolomics data and contrasting patients with different estrogen receptor status. Our proposed pipeline leverages multi-omics data for differential predictions, e.g. on drug response, and includes prior information on interactions. The case study presented in the vignette uses data published by Krug (2020) doi:10.1016/j.cell.2020.10.036. The package license applies only to the software and explicitly not to the included data.