# POMA <img src='man/figures/logo.png' align="right" height="139" />
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| *BioC* branch | Status | Version | Dependencies | Rank |
|-------------------------------------------------------------------------|---------------------------------------------------------------------------------------------------------------------------------------------------------|----------------------------------------------------------------------------------------------------------------------------------------------------|------------------------------------------------------------------------------------------------------------------------------------------------------|------------------------------------------------------------------------------------------------------------------------------|
| [Release](http://bioconductor.org/packages/release/bioc/html/POMA.html) | [](https://bioconductor.org/checkResults/release/bioc-LATEST/POMA/) | [](https://www.bioconductor.org/packages/POMA) | [](http://bioconductor.org/packages/release/bioc/html/POMA.html#since) | [](https://bioconductor.org/packages/stats/bioc/POMA) |
| [Devel](http://bioconductor.org/packages/devel/bioc/html/POMA.html) | [](https://bioconductor.org/checkResults/devel/bioc-LATEST/POMA/) | [](https://bioconductor.org/packages/devel/bioc/html/POMA.html) | [](http://bioconductor.org/packages/devel/bioc/html/POMA.html#since) | [](https://bioconductor.org/packages/stats/bioc/POMA) |
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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.
<!-- For more information and to get started, visit the POMA website. -->
<!-- POMA provides two different Shiny apps both for exploratory data analysis and statistical analysis that implement all POMA functions in two user-friendly web interfaces. -->
<!-- - **POMAShiny**: Shiny version of this package. https://github.com/pcastellanoescuder/POMAShiny -->
<!-- - **POMAcounts**: Shiny version for exploratory and statistical analysis of mass spectrometry spectral counts data and RNAseq data. https://github.com/pcastellanoescuder/POMAcounts -->
<!-- The [GitHub page](https://github.com/pcastellanoescuder/POMA) is for active development, issue tracking and forking/pulling purposes. To get an overview of the package, see the [*POMA Workflow*](https://pcastellanoescuder.github.io/POMA/articles/POMA-demo.html) vignette. -->
## Installation
To install the Bioconductor version:
``` r
# install.packages("BiocManager")
BiocManager::install("POMA")
```
If you need the GitHub version (not recommended), use:
``` r
# install.packages("devtools")
devtools::install_github("pcastellanoescuder/POMA")
```
## 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.
<!-- ### Cited In -->
<!-- Bellio C, Emperador M, Castellano P, et al. GDF15 Is an Eribulin Response Biomarker also Required for Survival of DTP Breast Cancer Cells. Cancers (Basel). 2022 May 23;14(10):2562. doi: 10.3390/cancers14102562. PMID: 35626166; PMCID: PMC9139899. -->
<!-- González-Domínguez R, Castellano-Escuder P, Lefèvre-Arbogast S, et al. Apolipoprotein E and sex modulate fatty acid metabolism in a prospective observational study of cognitive decline. Alzheimers Res Ther. 2022 Jan 3;14(1):1. doi: 10.1186/s13195-021-00948-8. PMID: 34980257; PMCID: PMC8725342. -->
<!-- González-Domínguez R, Castellano-Escuder P, Carmona F, et al. Food and Microbiota Metabolites Associate with Cognitive Decline in Older Subjects: A 12-Year Prospective Study. Mol Nutr Food Res. 2021 Dec;65(23):e2100606. doi: 10.1002/mnfr.202100606. Epub 2021 Oct 28. PMID: 34661340. -->
<!-- Peron G, Gargari G, Meroño T, et al. Crosstalk among intestinal barrier, gut microbiota and serum metabolome after a polyphenol-rich diet in older subjects with "leaky gut": The MaPLE trial. Clin Nutr. 2021 Oct;40(10):5288-5297. doi: 10.1016/j.clnu.2021.08.027. Epub 2021 Sep 9. PMID: 34534897. -->
## News
Click
[here](https://github.com/pcastellanoescuder/POMA/blob/master/NEWS.md)
for the latest package updates.
