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COSMOS uses [CARNIVAL](https://saezlab.github.io/CARNIVAL/)’s Integer Linear Programming (ILP) optimization strategy to find the smallest coherent subnetwork causally connecting as many deregulated TFs, kinases/phosphatases and metabolites as possible. The subnetwork is extracted from a novel integrated PKN (available [here](http://metapkn.omnipathdb.org/)) spanning signaling, transcriptional regulation and metabolism. Transcription factors activities are inferred from gene expression with [DoRothEA](https://saezlab.github.io/dorothea/), a meta resource of TF/target links. Kinase activities are inferred from phosphoproteomic with a kinase/substrate network of [Omnipath](http://omnipathdb.org/), a meta resource of protein-protein. [CARNIVAL](https://saezlab.github.io/CARNIVAL/) was adapted to find mechanistic hypotheses connecting the TF and kinase activities with metabolites from a signaling/metabolic prior knowledge network combining [Omnipath](http://omnipathdb.org/), [STITCHdb](http://stitch.embl.de/) and [Recon3D](https://www.vmh.life/). The scripts to generate the current network can be found here: https://github.com/saezlab/meta_PKN_BIGG.
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COSMOS finds coherent subnetwork causally connecting as many deregulated TFs, kinases/phosphatases and metabolites as possible. The subnetwork is extracted from a novel integrated PKN (available [here](http://metapkn.omnipathdb.org/)) spanning signaling, transcriptional regulation and metabolism. Transcription factors activities are inferred from gene expression with [decoupleR](https://saezlab.github.io/decoupleR/). Kinase activities are inferred from phosphoproteomic with a kinase/substrate network of [Omnipath](http://omnipathdb.org/), a meta resource of protein-protein. The scripts to generate the current network can be found here: https://github.com/saezlab/meta_PKN_BIGG.
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You can also use COSMOS if you don't have metabolomic data, to connect TF activities (from transcriptomic) with kinase activities (from phosphoproteomic) for exmaple !
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## tutorial to use MOFA nad COSMOS
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[Here](https://github.com/saezlab/Factor_COSMOS) you can find an extensive tutorial showing how to use MOFA and COSMOS with the NCI60 dataset. This is an extensive tutorial, if you wish to get a quicker plug and play introduction to COSMOS, see below.
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[Here]([https://github.com/saezlab/Factor_COSMOS](https://saezlab.github.io/cosmosR/articles/MOFA_to_COSMOS.html) you can find an extensive tutorial showing how to use MOFA and COSMOS with the NCI60 dataset. This is an extensive tutorial, if you wish to get a quicker plug and play introduction to COSMOS, see below.
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## Tutorial (NCI60 playground)
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We made a repository that contains pre-processed inputs and an example script to use cosmos with the NCI60 RNA+metabolomic datasets.
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You can find the repository [here](https://github.com/saezlab/COSMOS_basic).
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## Tutorial (video)
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We recorded a video guide for cosmosR tutorial in the context of a course organised by EBI-EMBL.
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You can access the recording at this link for a step by step introduction to cosmosR :
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