POMA introduces a
structured, reproducible and easy-to-use workflow for the visualization,
pre-processing, exploratory and statistical analysis of mass
spectrometry data. 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
MSnbase data structures,
developed by Laurent Gatto, 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 mass spectrometry spectral counts data based on Bioconductor packages msmsEDA and msmsTests. 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.