COSMOS_EXIM

This repository is made as a back-up and version control for a research project at the Amsterdam UMC, location AMC Experimental Immunology-Hematology department. This project will utilize Saezlab's method COSMOS for regulatory network generation through the integration of multi-omic data. In this research, we applied the COSMOS method to gene expression and metabolomics datasets of CLL and healthy donor T cells.

Pipeline explanation

We created the following 7-step pipeline, each step corresponding to a script, which can be found under scripts. This pipeline shows each step of the network generation workflow, from raw data to network visualization in Cytoscape.

The input is from unpublished sources, which can be requested via f.s.peters@amsterdamumc.nl.

Output

The networks can be found under network_figures/individual and network_figures/merged.

The thesis resulting from this research project can be found in this repository as Thesis.pdf.

Requirements

IBM Cplex was used used as the ILP solver for COSMOS in this project. This was a requirement for this version of COSMOS. IBM provides CPLEX Optimization Studio freely under an academic license.

Sources

Method sources:

• COSMOS

Dugourd A, Kuppe C, Sciacovelli M, Gjerga E, Gabor A, Emdal KB, Vieira V, Bekker-Jensen DB, Kranz J, Bindels EMJ, Jesper V Olsen, Christian Frezza, Rafael Kramann, Julio Saez-Rodriguez et al (2021) Causal integration of multi-omics data with prior knowledge to generate mechanistic hypotheses. Mol Syst Biol 17: e9730

Dugourd A, Lafrenz P, Mañanes D, Fallegger R, Kroger AC, Turei D, Shtylla B, Saez-Rodriguez J; Modeling causal signal propagation in multi-omic factor space with COSMOS; BioRxiv. 2024 Jul 17 DOI: 10.1101/2024.07.15.603538

• Cytoscape:

Shannon P., Markiel A., Ozier O., Baliga N.S., Wang J.T., Ramage D., Amin N., Schwikowski B., Ideker T. Cytoscape: a software environment for integrated models of biomolecular interaction networks. Genome Research 2003 Nov; 13(11):2498-504.

• RCy3:

Otasek, et al., Cytoscape Automation: empowering workflow-based network analysis. Genome Biology, 20:185 (2019)