https://github.com/saezlab/ocean
R package for metabolic enzyme enrichment anaylsis
https://github.com/saezlab/ocean
Last synced: over 1 year ago
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R package for metabolic enzyme enrichment anaylsis
- Host: GitHub
- URL: https://github.com/saezlab/ocean
- Owner: saezlab
- License: gpl-3.0
- Created: 2020-12-10T14:34:04.000Z (over 5 years ago)
- Default Branch: master
- Last Pushed: 2024-11-08T13:00:25.000Z (over 1 year ago)
- Last Synced: 2025-03-28T05:51:03.314Z (over 1 year ago)
- Language: R
- Size: 26.8 MB
- Stars: 12
- Watchers: 4
- Forks: 4
- Open Issues: 0
-
Metadata Files:
- Readme: README.md
- License: LICENSE
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README
# ocean: an R package for metabolic enzyme enrichment anaylsis
## Overview
The functional insights that metabolomic data sets contain currently lies
under-exploited. This is in part due to the complexity of metabolic reaction
networks and the indirect relationship between reaction fluxes and metabolite
abundance. Yet, footprint-based methods have been available for decades in the
context of other omic data sets such as transcriptomic and phosphoproteomic.
Here, we present ocEAn, a method that defines metabolic enzyme footprint from a
curated reduced version of the recon2 reaction network and use them to explore
coordinated deregulations of metabolite abundances with respect to their
position relative to metabolic enzymes in the same manner as Kinase-substrate
and TF-targets Enrichment analysis. This picture diplays the TCA cycle with
deregulated metabolites and estimated metabolic enzyme deregulations in kidney
cancer. You can generate a dynamic visualisation of this network and other
pathways by following
[our tutorial](https://saezlab.github.io/ocean/articles/ocean_intro.html).
## Tutorial
Instal the package (from github with remotes) :
```r
## If needed install the remotes and BiocManager packages
install.packages("remotes")
install.packages("BiocManager")
## instal ocean
remotes::install_github("saezlab/ocean", repos = BiocManager::repositories())
```
You can then run the
[tutorial scripts](https://saezlab.github.io/ocean/articles/ocean_intro.html)
with a kidney cancer toy metabolomic dataset.
you can find a lightweight tutorial R script [here](https://github.com/saezlab/ocean/blob/master/vignettes/tutorial_ocEAn.R)
PLEASE READ THE TUTORIAL CAREFULLY :) and do not hesitate to have an extensive
look at all the variable in it and what information they contain.
A new updated tutorial that showcase how ocean results can be used in parallel
with proteomic data will be comming soon!
## Citations
ocEAn manuscript: Sciacovelli, Dugourd et al. Nitrogen partitioning between
branched-chain amino acids and urea cycle enzymes sustains renal cancer
progression; 2022 https://pubmed.ncbi.nlm.nih.gov/36539415/
To reproduce the result of this manuscript, install ocEAn from the branch
"marco_paper".
The underlying metabolic model is based on a curated reduced model of human
metabolism, see: Masid M, Ataman M & Hatzimanikatis V (2020) Analysis of human
metabolism by reducing the complexity of the genome-scale models using
redHUMAN.
Nat Commun 11: 2821 https://www.nature.com/articles/s41467-020-16549-2#Tab1
The scripts to process the redHuman model into the oCEan format can be found
here: https://github.com/saezlab/redHuman_models