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https://github.com/saezlab/covid19

We use our tools to analysis Covid19 RNAseq datasets
https://github.com/saezlab/covid19

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We use our tools to analysis Covid19 RNAseq datasets

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# Applying Saezlab Tools to Covid-19 related Datasets

This repository contains the scripts used to apply some of our tools to Covid-19
related datasets. In particular, we take the RNAseq data from the study:

*"SARS-CoV-2 launches a unique transcriptional signature from in vitro, ex vivo, and in vivo systems"*

We first focused in analysing the effect of SARS-CoV-2 infection in different
human lung-related cells.

+ Human lung epithelial cells (**NHBE**): mock treated vs infected with
SARS-CoV-2.

+ **A549** alveolar cancer cell line: mock treated vs infected with SARS-CoV-2.

+ **A549** cell line does not express ACE2, the receptor used by SARS-CoV-2 to
penetrate into human cells. Therefore A549 were also transduced with **ACE2**
and then mock treated or infected with SARS-CoV-2

+ **Calu-3** human lung epithelial cancer cell line: mock treated vs
infected with SARS-CoV-2.

Then, we analysed the effects at the transcriptomic level of different viral
infections:

+ A549 alveolar cancer cell line: mock treated vs infected with SARS-CoV-2.

+ A549 alveolar cancer cell line: mock treated vs infected with RSV.

+ A549 alveolar cancer cell line: mock treated vs infected with HPIV3.

## Effect of SARS-CoV-2 infection in different cell lines

We detail below the different scripts and analysis performed in this section:

+ We first applied a traditional differential expression analysis using the
**DESeq2** R package. This is aligned with the script of Agatha Treveil from
Tamas' group.

+ Then, we used the normalised counts and the stastics generated in the previous
script to run **Dorothea** and **Progeny**. Doing so, we estimated Transcription
factors activity and pathway activity in the SARS-CoV-2 infected lines in
comparison with the mock treated.

+ We run **CARNIVAL** using different perturbations conditions related
to the virus action. In order to do so, we used TF and Pathway activity scores
generated in the previous script. CARNIVAL also requires a prior knowledge
network that was extracted from **Omnipath** using **OmnipathR** in line with
the network used by Dezso from Tamas' group.

+ We perform a pathway enrichment analysis taking into account the nodes
from **CARNIVAL** output.

+ We are also exploring the cell cycle phase of the different samples.

## Effect of different viral infections on A549 cell line

We detail below the different scripts and analysis performed in this section:

+ We first applied a traditional differential expression analysis using the
**DESeq2** R package:

+ Then, we used the normalised counts and the stastics generated in the previous
script to run **Dorothea** and **Progeny**. Doing so, we estimated Transcription
factors activity and pathway activity in the SARS-CoV-2 infected lines in
comparison with the mock treated.

+ We run **CARNIVAL** using different perturbations conditions related
to the virus action. In order to do so, we used TF and Pathway activity scores
generated in the previous script. CARNIVAL also requires a prior knowledge
network that was extracted from **Omnipath** using **OmnipathR** in line with
the network used by Dezso from Tamas' group.

+ We perform a pathway enrichment analysis taking into account the nodes
from **CARNIVAL** output.

### License Info

This program is free software: you can redistribute it and/or modify it under
the terms of the GNU General Public License as published by the Free Software
Foundation, either version 3 of the License, or (at your option) any later
version.

This program is distributed in the hope that it will be useful, but WITHOUT ANY
WARRANTY; without even the implied warranty of MERCHANTABILITY or FITNESS FOR
A PARTICULAR PURPOSE. See the GNU General Public License for more details.

Please check http://www.gnu.org/licenses/.