Biological entities such as proteins, mRNA, or metabolites act in concert in their network context. Network-based analyses thus provide means to incorporate these physical and regulatory interactions into decision making from molecular measurements. However, characterization beyond individual omic layers, especially combining metabolomics with gene-product-derived measurements, still remains a challenge.
We present a novel network-based analysis pipeline that enables integrative analysis of multi-omics data including metabolomics. It allows for comparative conclusions between two conditions, such as tumor subgroups, healthy vs. disease, or generally control vs. perturbed. The approach is based on representing the cellular complexity of the different conditions given by the data as heterogeneous, multi-omics molecular networks. This means nodes are representing different types of molecules such as mRNAs, proteins, metabolites, or phosphosites. We focus on interactions and their strength instead of on node properties. 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 study, we investigate differential drug response in breast cancer from datasets providing proteomics, transcriptomics, phospho-proteomics, and metabolomics measurements. We contrast patients with different estrogen receptor status. The impact of our approach is highlighted by critical evaluation on ground truth data from cancer cell lines.
Our proposed pipeline leverages multi-omics data to derive differential predictions, e.g. on drug response, and includes prior information on interactions. We make it available as R package, https://CRAN.R-project.org/package=molnet, that enables versatile integration and network-based analysis of multi-omics data.

Ein Vortrag in englischer Sprache von Dr. Katharina Baum (Network-based Data Analysis, Hasso Plattner Institut, Digital Engineering Fakultät, Universität Potsdam).

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