Multi-omic analysis and integration with prior knowledge to study signalling and metabolism deregulations in cancer cell lines

Overview:

Multi-omics datasets can provide molecular insights beyond the sum of individual omics. Various tools have been recently developed to integrate such datasets, but there are limited strategies to systematically extract mechanistic hypotheses from them. Here, we present COSMOS (Causal Oriented Search of Multi-Omics Space, https://github.com/saezlab/cosmosR), a method that integrates phosphoproteomics, transcriptomics, and metabolomics datasets. COSMOS combines extensive prior knowledge of signalling, metabolic, and gene regulatory networks with computational methods to estimate activities of transcription factors and kinases as well as network-level causal reasoning. COSMOS provides mechanistic hypotheses for experimental observations across multi-omics datasets, leveraging prior knowledge such as signalling events, ligand-receptor interactions and metabolic networks. The concepts behind COSMOS are presented, including its latest advances, notably its integration with factor based analysis tools such as MOFA.

GitHub page for the project: https://github.com/saezlab/Factor_COSMOS/tree/no_MOFA

Project aims:

Explore NCI60 cell line omic datasets

Interpreting TF activities estimated from RNA seq data

Learning to use cosmos to integrate signalling and metabolic data with prior knowledge

Generate and interpret testable hypotheses

Introduction to multi-omics data integration and visualisation

Multi-omic analysis and integration with prior knowledge to study signalling and metabolism deregulations in cancer cell lines

Overview:

Overview:

GitHub page for the project: https://github.com/saezlab/Factor_COSMOS/tree/no_MOFA

Project aims:

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