POMA

Tools for Omics Data Analysis


Bioconductor version: Release (3.20)

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. See https://github.com/pcastellanoescuder/POMAShiny. Paper: Castellano-Escuder et al. (2021) for more details.

Author: Pol Castellano-Escuder [aut, cre]

Maintainer: Pol Castellano-Escuder <polcaes at gmail.com>

Citation (from within R, enter citation("POMA")):

Installation

To install this package, start R (version "4.4") and enter:


if (!require("BiocManager", quietly = TRUE))
    install.packages("BiocManager")

BiocManager::install("POMA")

For older versions of R, please refer to the appropriate Bioconductor release.

Documentation

To view documentation for the version of this package installed in your system, start R and enter:

browseVignettes("POMA")
Get Started HTML R Script
Normalization Methods HTML R Script
Reference Manual PDF
NEWS Text

Details

biocViews BatchEffect, Classification, Clustering, DecisionTree, DimensionReduction, MultidimensionalScaling, Normalization, Preprocessing, PrincipalComponent, RNASeq, Regression, Software, StatisticalMethod, Visualization
Version 1.16.0
In Bioconductor since BioC 3.12 (R-4.0) (4 years)
License GPL-3
Depends R (>= 4.0)
Imports broom, caret, ComplexHeatmap, dbscan, dplyr, DESeq2, fgsea, FSA, ggcorrplot, ggplot2, ggrepel, glmnet, impute, janitor, limma, lme4, magrittr, MASS, mixOmics, multcomp, msigdbr, purrr, randomForest, RankProd(>= 3.14), rlang, SummarizedExperiment, sva, tibble, tidyr, utils, uwot, vegan
System Requirements
URL https://github.com/pcastellanoescuder/POMA
Bug Reports https://github.com/pcastellanoescuder/POMA/issues
See More
Suggests BiocStyle, covr, ggraph, ggtext, knitr, patchwork, plotly, tidyverse, testthat (>= 2.3.2)
Linking To
Enhances
Depends On Me
Imports Me PRONE
Suggests Me fobitools
Links To Me
Build Report Build Report

Package Archives

Follow Installation instructions to use this package in your R session.

Source Package POMA_1.16.0.tar.gz
Windows Binary (x86_64) POMA_1.16.0.zip
macOS Binary (x86_64) POMA_1.16.0.tgz
macOS Binary (arm64) POMA_1.15.17.tgz
Source Repository git clone https://git.bioconductor.org/packages/POMA
Source Repository (Developer Access) git clone git@git.bioconductor.org:packages/POMA
Bioc Package Browser https://code.bioconductor.org/browse/POMA/
Package Short Url https://bioconductor.org/packages/POMA/
Package Downloads Report Download Stats