@@ -12,6 +12,9 @@ Phosphoproteomic data of mock-treated and SARS-CoV.2 infected cells were extract
Finally, we used Carnival ([Liu et al., 2019](https://doi.org/10.1038/s41540-019-0118-z)) with the COSMOS approach ([Dugourd et al., 2021](https://doi.org/10.15252/msb.20209730)) to connect the top 10 deregulated kinases with the top 30 deregulated TFs with a Prior Knowledge Network assembled from OmniPath resources ([Türei et al., 2021](https://doi.org/10.1101/2020.08.03.221242)). Progeny pathway activity scores were used to weigh the PKN and facilitate the optimal network search to connect kinases and TFs.
## Markdown
To place our results in the context of the whole study, we matched the genes obtained in carnival results with those included in the curated pathways by the [Covid-19 Disease map community] (https://covid.pages.uni.lu/map_contents).
The notebook containing the analysis can be found [here](https://git-r3lab.uni.lu/computational-modelling-and-simulation/footprint-based-analysis-and-causal-network-contextualisation-in-sars-cov-2-infected-a549-cell-line/-/blob/master/02_Carnival_Transcriptomics_phosphoprotemicsMann.md).
## Markdowns
1. The notebook containing the footprint-based analysis can be found [here](https://git-r3lab.uni.lu/computational-modelling-and-simulation/footprint-based-analysis-and-causal-network-contextualisation-in-sars-cov-2-infected-a549-cell-line/-/blob/master/02_Carnival_Transcriptomics_phosphoprotemicsMann.md).
2. The notebook containing the analysis to match the footprint-based results with the content of the disease map communicaty can be found [here](https://git-r3lab.uni.lu/computational-modelling-and-simulation/footprint-based-analysis-and-causal-network-contextualisation-in-sars-cov-2-infected-a549-cell-line/-/blob/master/Matching_with_Covid19DM_Diagrams/grep_disease_maps.md)
