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POMA introduces a structured, reproducible and easy-to-use workflow for the visualization, pre-processing, exploration, and statistical analysis of metabolomics and proteomics 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, 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.

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")

If you need the GitHub version (not recommended unless you know what you are doing), use:

# install.packages("devtools")

Code of Conduct

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.

pcastellanoescuder/POMA documentation built on May 14, 2021, 11:07 p.m.