This is the released version of dar; for the devel version, see dar.
Differential Abundance Analysis by ConsensusBioconductor version: Release (3.21)
Differential abundance testing in microbiome data challenges both parametric and non-parametric statistical methods, due to its sparsity, high variability and compositional nature. Microbiome-specific statistical methods often assume classical distribution models or take into account compositional specifics. These produce results that range within the specificity vs sensitivity space in such a way that type I and type II error that are difficult to ascertain in real microbiome data when a single method is used. Recently, a consensus approach based on multiple differential abundance (DA) methods was recently suggested in order to increase robustness. With dar, you can use dplyr-like pipeable sequences of DA methods and then apply different consensus strategies. In this way we can obtain more reliable results in a fast, consistent and reproducible way.
Author: Francesc Catala-Moll [aut, cre] ORCID: 0000-0002-2354-8648
Maintainer: Francesc Catala-Moll <fcatala at irsicaixa.es>
Citation (from within R, entercitation("dar")): Installation
To install this package, start R (version "4.5") and enter:
if (!require("BiocManager", quietly = TRUE))
install.packages("BiocManager")
BiocManager::install("dar")
For older versions of R, please refer to the appropriate Bioconductor release.
DocumentationTo view documentation for the version of this package installed in your system, start R and enter:
browseVignettes("dar")Follow Installation instructions to use this package in your R session.
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