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)
Author: Pol Castellano-Escuder [aut, cre] 
Maintainer: Pol Castellano-Escuder <polcaes at gmail.com>
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 | ||
| 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 |