In POMA: User-friendly Workflow for Pre-processing and Statistical Analysis of Mass Spectrometry Data

POMA

POMA introduces a structured, reproducible and easy 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 pre-existing 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.

Installation

To install Bioconductor devel version:

# install.packages("BiocManager")
BiocManager::install(version = 'devel') # Install BiocManager devel version
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")

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.

POMA documentation built on Nov. 8, 2020, 6:26 p.m.