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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.
To install the Bioconductor last release version:
# install.packages("BiocManager")
BiocManager::install("POMA")To install the GitHub version:
# install.packages("devtools")
devtools::install_github("pcastellanoescuder/POMA")To install the GitHub devel version:
devtools::install_github("pcastellanoescuder/POMA", ref = "devel")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{\'a}lez-Dom{\'\i}nguez, Ra{\'u}l and Carmona-Pontaque, Francesc and Andr{\'e}s-Lacueva, Cristina and S{\'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}
}Click here for the latest package updates.