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POMA <img src='man/figures/logo.png' align="right" height="139" />

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Lifecycle: stable CodeFactor Last Commit License: GPL v3

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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.

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Installation

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")

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.

@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}
}

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