The POMA package offers a comprehensive toolkit designed for omics
data analysis, streamlining the process from initial visualization to
final statistical analysis. Its primary goal is to simplify and unify
the various steps involved in omics data processing, making it more
accessible and manageable within a single, intuitive R package.
Emphasizing on reproducibility and user-friendliness, POMA leverages
the standardized SummarizedExperiment class from Bioconductor,
ensuring seamless integration and compatibility with a wide array of
Bioconductor tools. This approach guarantees maximum flexibility and
replicability, making POMA an essential asset for researchers handling
omics datasets.
Castellano-Escuder et al. POMAShiny: A user-friendly web-based workflow
for metabolomics and proteomics data analysis. PLoS Comput Biol. 2021
Jul 1;17(7):e1009148. doi: 10.1371/journal.pcbi.1009148. PMID: 34197462;
PMCID: PMC8279420.
@article{castellano2021pomashiny,
title={POMAShiny: A user-friendly web-based workflow for metabolomics and proteomics data analysis},
author={Castellano-Escuder, Pol and Gonz{```
## News
Click
[here](https://github.com/pcastellanoescuder/POMA/blob/master/NEWS.md)
for the latest package updates.
a}lez-Dom{```
## News
Click
[here](https://github.com/pcastellanoescuder/POMA/blob/master/NEWS.md)
for the latest package updates.
\i}nguez, Ra{```
## News
Click
[here](https://github.com/pcastellanoescuder/POMA/blob/master/NEWS.md)
for the latest package updates.
u}l and Carmona-Pontaque, Francesc and Andr{```
## News
Click
[here](https://github.com/pcastellanoescuder/POMA/blob/master/NEWS.md)
for the latest package updates.
e}s-Lacueva, Cristina and S{```
## News
Click
[here](https://github.com/pcastellanoescuder/POMA/blob/master/NEWS.md)
for the latest package updates.
a}nchez-Pla, Alex},
journal={PLOS Computational Biology},
volume={17},
number={7},
pages={e1009148},
year={2021},
publisher={Public Library of Science San Francisco, CA USA}
}
POMA
The
POMApackage offers a comprehensive toolkit designed for omics data analysis, streamlining the process from initial visualization to final statistical analysis. Its primary goal is to simplify and unify the various steps involved in omics data processing, making it more accessible and manageable within a single, intuitive R package. Emphasizing on reproducibility and user-friendliness,POMAleverages the standardizedSummarizedExperimentclass from Bioconductor, ensuring seamless integration and compatibility with a wide array of Bioconductor tools. This approach guarantees maximum flexibility and replicability, makingPOMAan essential asset for researchers handling omics datasets.Installation
To install the Bioconductor last release version:
To install the GitHub version:
To install the GitHub devel version:
Citation
Castellano-Escuder et al. POMAShiny: A user-friendly web-based workflow for metabolomics and proteomics data analysis. PLoS Comput Biol. 2021 Jul 1;17(7):e1009148. doi: 10.1371/journal.pcbi.1009148. PMID: 34197462; PMCID: PMC8279420.
News
Click here for the latest package updates.