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

Need some help? Ask on the Bioconductor Support site!

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

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_1.16.0.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