https://github.com/saezlab/carnival-bioconductor-dev
Provisional repository for the development of CARNIVAL package for Bioconductor
https://github.com/saezlab/carnival-bioconductor-dev
Last synced: 10 months ago
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Provisional repository for the development of CARNIVAL package for Bioconductor
- Host: GitHub
- URL: https://github.com/saezlab/carnival-bioconductor-dev
- Owner: saezlab
- Created: 2019-11-13T14:56:07.000Z (over 6 years ago)
- Default Branch: master
- Last Pushed: 2020-08-25T20:31:17.000Z (almost 6 years ago)
- Last Synced: 2025-03-15T17:44:36.899Z (over 1 year ago)
- Language: R
- Size: 9.25 MB
- Stars: 1
- Watchers: 3
- Forks: 2
- Open Issues: 4
-
Metadata Files:
- Readme: README.md
Awesome Lists containing this project
README
This repository is outdated and kept mostly for dt and mult_cond functionalities. Merging them to the main CARNIVAL repository might not be as straightforward since code organization in CARNIVAL-Bioconductor-Dev differs from the main CARNIVAL repository.
# CARNIVAL
CARNIVAL is an R-package providing a framework to perform causal reasoning to infer a subset of signalling network from transcriptomics data. This work was originally based on [Melas et al.](https://pubs.rsc.org/en/content/articlehtml/2015/ib/c4ib00294f) with a number improved functionalities comparing to the original version.
Transcription factors’ (TFs) activities and pathway scores from gene expressions can be inferred with our in-house tools [DoRothEA](https://github.com/saezlab/DoRothEA) & [PROGENy](https://github.com/saezlab/progeny), respectively.
TFs’ activities and signed directed protein-protein interaction networks +/- drug targets and pathway scores are then used to derive a series of linear constraints to generate integer linear programming (ILP) problems.
An ILP solver (CPLEX) is subsequently applied to identify the sub-network topology with minimised discrepancies on fitting error and model size.
More detailed descriptions of CARNIVAL, benchmarking and applicational studies can be found on it's dedicated [web-page](https://saezlab.github.io/CARNIVAL/) and in [Liu, Trairatphisan, Gjerga et al.](https://www.nature.com/articles/s41540-019-0118-z):
> Liu A*, Trairatphisan P*, Gjerga E*, Didangelos A, Barratt J, Saez-Rodriguez J. (2019). From expression footprints to causal pathways: contextualizing large signaling networks with CARNIVAL. *npj Systems Biology and Applications*, https://doi.org/10.1038/s41540-019-0118-z (*equal contributions).
## Getting Started
A tutorial for preparing CARNIVAL input files starting from differentially gene expression (DEG) and for running the CARNIVAL pipeline are provided as vignettes in R-Markdown, R-script and HTML formats. The wrapper script "runCARNIVAL" was introduced to take input arguments, pre-process input descriptions, run optimisation and export results as network files and figures. Three built-in CARNIVAL examples are also supplied as case studies for users.
### Prerequisites
CARNIVAL requires the interactive version of IBM Cplex or CBC-COIN solver as the network optimiser. The IBM ILOG Cplex is freely available through Academic Initiative [here](https://www.ibm.com/products/ilog-cplex-optimization-studio?S_PKG=CoG&cm_mmc=Search_Google-_-Data+Science_Data+Science-_-WW_IDA-_-+IBM++CPLEX_Broad_CoG&cm_mmca1=000000RE&cm_mmca2=10000668&cm_mmca7=9041989&cm_mmca8=kwd-412296208719&cm_mmca9=_k_Cj0KCQiAr93gBRDSARIsADvHiOpDUEHgUuzu8fJvf3vmO5rI0axgtaleqdmwk6JRPIDeNcIjgIHMhZIaAiwWEALw_wcB_k_&cm_mmca10=267798126431&cm_mmca11=b&mkwid=_k_Cj0KCQiAr93gBRDSARIsADvHiOpDUEHgUuzu8fJvf3vmO5rI0axgtaleqdmwk6JRPIDeNcIjgIHMhZIaAiwWEALw_wcB_k_|470|135655&cvosrc=ppc.google.%2Bibm%20%2Bcplex&cvo_campaign=000000RE&cvo_crid=267798126431&Matchtype=b&gclid=Cj0KCQiAr93gBRDSARIsADvHiOpDUEHgUuzu8fJvf3vmO5rI0axgtaleqdmwk6JRPIDeNcIjgIHMhZIaAiwWEALw_wcB). The [CBC](https://projects.coin-or.org/Cbc) solver is open source and freely available for any user.
### Installing
CARNIVAL is currently available for the installation as an R-package from our GitHub page
```R
# Install CARNIVAL from Github using devtools
# install.packages('devtools') # in case devtools hasn't been installed
library(devtools)
install_github('saezlab/CARNIVAL-Bioconductor-Dev', build_vignettes = TRUE)
# or download the source file from GitHub and install from source
install.packages('path_to_extracted_CARNIVAL_directory', repos = NULL, type="source")
```
## Running CARNIVAL
To obtain the list of tutorials/vignettes of the CARNIVAL package, user can start with typing the following commmand on R-console:
```R
vignette("CARNIVAL-vignette")
```
## License
Distributed under the GNU GPLv3 License. See accompanying file [LICENSE.txt](https://github.com/saezlab/CARNIVAL/blob/master/LICENSE.txt) or copy at [http://www.gnu.org/licenses/gpl-3.0.html](http://www.gnu.org/licenses/gpl-3.0.html).
## References
[Melas et al.](https://pubs.rsc.org/en/content/articlehtml/2015/ib/c4ib00294f):
> Melas IN, Sakellaropoulos T, Iorio F, Alexopoulos L, Loh WY, Lauffenburger DA, Saez-Rodriguez J, Bai JPF. (2015). Identification of drug-specific pathways based on gene expression data: application to drug induced lung injury. *Integrative Biology*, Issue 7, Pages 904-920, https://doi.org/10.1039/C4IB00294F.
[DoRothEA v2 - Garcia-Alonso et al.](https://www.biorxiv.org/content/early/2018/06/03/337915):
> Garcia-Alonso L, Ibrahim MM, Turei D, Saez-Rodriguez J. (2018). Benchmark and integration of resources for the estimation of human transcription factor activities. *bioRXiv*, https://doi.org/10.1101/337915.
[PROGENy - Schubert et al.](https://www.nature.com/articles/s41467-017-02391-6):
> Schubert M, Klinger B, Klünemann M, Sieber A, Uhlitz F, Sauer S, Garnett MJ, Blüthgen N, Saez-Rodriguez J. (2018). Perturbation-response genes reveal signaling footprints in cancer gene expression. *Nature Communication*, Issue 9, Nr. 20. https://doi.org/10.1038/s41467-017-02391-6.
## Acknowledgement
CARNIVAL has been developed as a computational tool to analyse -omics data within the [TransQST Consortium](https://transqst.org) and [H2020 Symbiosys ITN Training Network](https://www.h2020symbiosys.eu/).
"This project has received funding by the European Union’s H2020 program (675585 Marie-Curie ITN ‘‘SymBioSys’’) and the Innovative Medicines Initiative 2 Joint Undertaking under grant agreement No 116030. The Joint Undertaking receives support from the European Union's Horizon 2020 research and innovation programme and EFPIA."