@@ -6,11 +6,11 @@ This repository contains the code to reproduce the analysis presented in section
## Procedure Details
We obtained the transcriptomics dataset from the GEO database with accession number [GSE147507](https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE147507)[Blanco-Melo et al., 2020](https://doi.org/10.1016/j.cell.2020.04.026). We extracted the series number 5 from the dataset, consisting of 2 conditions in triplicate, A549 cells treated with a mock and A549 infected with SARS-CoV-2, measured 24 hours after infection. Differential analysis of the transcript abundances was performed using DESeq2 [Love et al., 2014](https://doi.org/10.1186/s13059-014-0550-8). The resulting t-values of the differential analysis were used as input to estimate pathway activity deregulation using Progeny [Schubert et al., 2018](https://doi.org/10.1038/s41467-017-02391-6). The differential analysis t-values were also used to estimate the deregulation of TF activities using Dorothea [Garcia-Alonso et al., 2019](http://www.genome.org/cgi/doi/10.1101/gr.240663.118) as a source of TF-target regulon and the Viper algorithm [Alvarez et al., 2016](https://doi.org/10.1038/ng.3593) to estimate the TF activity score.
We obtained the transcriptomics dataset from the GEO database with accession number [GSE147507](https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE147507)([Blanco-Melo et al., 2020](https://doi.org/10.1016/j.cell.2020.04.026)). We extracted the series number 5 from the dataset, consisting of 2 conditions in triplicate, A549 cells treated with a mock and A549 infected with SARS-CoV-2, measured 24 hours after infection. Differential analysis of the transcript abundances was performed using DESeq2 ([Love et al., 2014](https://doi.org/10.1186/s13059-014-0550-8)). The resulting t-values of the differential analysis were used as input to estimate pathway activity deregulation using Progeny ([Schubert et al., 2018](https://doi.org/10.1038/s41467-017-02391-6)). The differential analysis t-values were also used to estimate the deregulation of TF activities using Dorothea ([Garcia-Alonso et al., 2019](http://www.genome.org/cgi/doi/10.1101/gr.240663.118)) as a source of TF-target regulon and the Viper algorithm ([Alvarez et al., 2016](https://doi.org/10.1038/ng.3593)) to estimate the TF activity score.
Phosphoproteomic data of mock-treated and SARS-CoV.2 infected cells were extracted from [Stukalov et al., 2021](https://doi.org/10.1038/s41586-021-03493-4). Phosphosite differential analysis log2FC was used to estimate the deregulation of kinase activities using (Bachman et al., 2019)[https://doi.org/10.1101/822668] as a source of kinase-substrate interactions and a z-test to estimate kinase activity score [Bouhaddou et al., 2020](https://doi.org/10.1016/j.cell.2020.06.034)[Hernandez-Armenta et al., 2017](https://doi.org/10.1093/bioinformatics/btx082)
Phosphoproteomic data of mock-treated and SARS-CoV.2 infected cells were extracted from ([Stukalov et al., 2021](https://doi.org/10.1038/s41586-021-03493-4)). Phosphosite differential analysis log2FC was used to estimate the deregulation of kinase activities using ((Bachman et al., 2019)[https://doi.org/10.1101/822668]) as a source of kinase-substrate interactions and a z-test to estimate kinase activity score ([Bouhaddou et al., 2020](https://doi.org/10.1016/j.cell.2020.06.034), [Hernandez-Armenta et al., 2017](https://doi.org/10.1093/bioinformatics/btx082))
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.
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.
