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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} }
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