https://github.com/saezlab/nichenet_omnipath
Building and Training of the NicheNet Method exclusively using OmniPath resources. SARS-CoV-2 case study
https://github.com/saezlab/nichenet_omnipath
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Building and Training of the NicheNet Method exclusively using OmniPath resources. SARS-CoV-2 case study
- Host: GitHub
- URL: https://github.com/saezlab/nichenet_omnipath
- Owner: saezlab
- License: gpl-3.0
- Created: 2020-07-07T15:30:06.000Z (about 6 years ago)
- Default Branch: master
- Last Pushed: 2020-07-10T07:24:04.000Z (about 6 years ago)
- Last Synced: 2025-01-22T07:37:40.063Z (over 1 year ago)
- Homepage:
- Size: 2.64 MB
- Stars: 2
- Watchers: 4
- Forks: 2
- Open Issues: 0
-
Metadata Files:
- Readme: README.md
- License: LICENSE
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README
# OmniPath-based generation of the NicheNet Method prior model: A SARS-CoV-2 case study
## Overview
NicheNet is a recently developed method to prioritize ligand–target
relationships between interacting cells by combining their expression data
with prior knowledge on interaction networks. For this purpose, it explores the
most consistent inter- and intra-cellular protein interactions in accordance
with a given gene expression dataset. The authors collected different types of
interactions from more than 20 databases to build a ligand-receptor network, a
signaling network and a gene regulatory network. OmniPath provides a
single-access point covering all the different types of interactions employed in
the NicheNet method. **Therefore, we here highlight the value of OmniPath by
exclusively using its resources to create a ligand-target regulatory potential
model as described in the NicheNet article** (Browaeys, Saelens and Saeys, 2019).
Nowadays, COVID-19, caused by SARS-CoV-2, is spreading globally throughout the
planet. WHO has reported approximately 10 million confirmed cases and 500 000
deaths to date (June 29, 2020). Against this background, we aim at using our
Omnipath-based version of NicheNet to explore the autocrine signaling after
SARS-CoV-2 infection. In particular, **we explore the potential regulatory
effect of over-expressed ligands after infection on the expression of
inflammatory response related genes in the Calu3 cell line**. The RNAseq
expression data was taken from a recent study.
## Content
This repository contains the vignettes to reproduce the SARS-CoV-2 case study
presented in the publication:
> Türei, D., Valdeolivas, A., Gul, L., Palacio-Escat, N., Ivanova, O., Modos, D., Korcsmáros T. & Saez-Rodriguez, J.
Integration of intra- and intercellular signaling resources with OmniPath
## Important Links
### Omnipath:
### NicheNet:
### RNAseq Expression data
## References
> Türei, D., Korcsmáros, T., & Saez-Rodriguez, J. (2016). OmniPath: guidelines and gateway for literature-curated signaling pathway resources. _Nature methods_, 13(12), 966–967. [10.1038/nmeth.4077](https://doi.org/10.1038/nmeth.4077)
> Browaeys, R., Saelens, W. & Saeys, Y. NicheNet: modeling intercellular communication by linking ligands to target genes. _Nature Methods_ 17, 159–162 (2020). [10.1038/s41592-019-0667-5](https://doi.org/10.1038/s41592-019-0667-5)
> Blanco-Melo, D., Nilsson-Payant, B.E., Liu, W.-C., Uhl, S., Hoagland, D., Møller, R., Jordan, T.X., Oishi, K., Panis, M., Sachs, D., Wang, T.T., Schwartz, R.E., Lim, J.K., Albrecht, R.A., tenOever, B.R., 2020. Imbalanced Host Response to SARS-CoV-2 Drives Development of COVID-19. _Cell_ 181, 1036–1045.e9. [10.1016/j.cell.2020.04.026](https://doi.org/10.1016/j.cell.2020.04.026)