dar: A Consensus-Based Framework for Differential Abundance Testing in Microbiome Data
Farre-Badia, J.; Noguera-Julian, M.; Paredes, R.; Catala-Moll, F.
Show abstract
SummaryThe dar R package streamlines differential abundance (DA) testing in microbiome research by integrating state-of-the-art DA methods--such as DESeq2, ALDEx2, ANCOM-BC, and MetagenomeSeq--within a customizable consensus-based framework, thereby enhancing the robustness and reproducibility of DA results. Leveraging microbiome data in phyloseq or TreeSummarizedExperiment formats, dar organizes analysis steps in a modular recipe object, enabling users to easily incorporate preprocessing tasks such as taxonomic filtering, rarefaction, and subsetting, alongside multiple DA analysis methods. Dedicated visualization tools facilitate the definition of a consensus by illustrating the overlap among methods, empowering users to refine analysis strategies and display final results. Reproducibility is supported through functions that export and import entire analysis workflows, making dar a comprehensive solution for addressing the complex, high-dimensional nature of microbiome data. Availability and implementationdar is an R package available from Bioconductor [≥] 3.19 (https://www.bioconductor.org/packages/dar) for R [≥] 4.4. The software is distributed under the MIT License and includes example datasets. Contactfcatala@irsicaixa.es Supplementary informationAdditional documentation is available at https://microbialgenomics-irsicaixaorg.github.io/dar
Matching journals
The top 4 journals account for 50% of the predicted probability mass.
Similar papers in this journal
- zAMP and zAMPExplorer: Reproducible Scalable Amplicon-based Metagenomics Analysis and Visualization 95%
- MerCat2: a versatile k-mer counter and diversity estimator for database-independent property analysis obtained from omics data 95%
- TAGINE: Fast Taxonomy-based Feature Engineering for Microbiome Analysis 94%
Similar papers in this journal
"Similar papers" are the closest papers from that journal in the model's embedding space. They show what the match is built on, but the ranking comes mostly from a classifier over the whole training set, not from these examples alone.