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POMA introduces a
structured, reproducible and easy-to-use workflow for the visualization,
pre-processing, exploration, and statistical analysis of omics datasets.
The main aim of POMA is to enable a flexible data cleaning and
statistical analysis processes in one comprehensible and user-friendly R
package. This package uses the standardized
SummarizedExperiment
class to achieve the maximum flexibility and reproducibility and makes
POMA compatible with other Bioconductor
packages.
POMA also has two different Shiny app modules 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 is for active development, issue tracking and forking/pulling purposes. To get an overview of the package, see the POMA Workflow vignette.
To install Bioconductor version:
# install.packages("BiocManager")
BiocManager::install("POMA")If you need the GitHub version (not recommended unless you know what you are doing), use:
# install.packages("devtools")
devtools::install_github("pcastellanoescuder/POMA")Please note that the ‘POMA’ project is released with a Contributor Code of Conduct. By contributing to this project, you agree to abide by its terms.