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POMA

This is the development version of POMA; for the stable release version, see POMA.

Tools for Omics Data Analysis


Bioconductor version: Development (3.19)

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")

# The following initializes usage of Bioc devel
BiocManager::install(version='devel')

BiocManager::install("POMA")

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

Documentation

Reference Manual PDF

Details

biocViews BatchEffect, Classification, Clustering, DecisionTree, DimensionReduction, MultidimensionalScaling, Normalization, Preprocessing, PrincipalComponent, RNASeq, Regression, Software, StatisticalMethod, Visualization
Version 1.13.22
In Bioconductor since BioC 3.12 (R-4.0) (3.5 years)
License GPL-3
Depends R (>= 4.0)
Imports broom, caret, ComplexHeatmap, dbscan, dplyr, DESeq2, FSA, ggplot2, ggrepel, glmnet, impute, janitor, limma, lme4, magrittr, MASS, mixOmics, 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
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Package Archives

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

Source Package
Windows Binary
macOS Binary (x86_64)
macOS Binary (arm64)
Source Repository git clone https://git.bioconductor.org/packages/POMA
Source Repository (Developer Access) git clone git@git.bioconductor.org:packages/POMA
Package Short Url https://bioconductor.org/packages/POMA/
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