tidybulk: an R tidy framework for modular transcriptomic data analysis
Open Access
- 22 January 2021
- journal article
- research article
- Published by Springer Science and Business Media LLC in Genome Biology
- Vol. 22 (1), 1-15
- https://doi.org/10.1186/s13059-020-02233-7
Abstract
Recently, efforts have been made toward the harmonization of transcriptomic data structures and workflows using the concept of data tidiness, to facilitate modularisation. We present tidybulk, a modular framework for bulk transcriptional analyses that introduces a tidy transcriptomic data structure paradigm and analysis grammar. Tidybulk covers a wide variety of analysis procedures and integrates a large ecosystem of publicly available analysis algorithms under a common framework. Tidybulk decreases coding burden, facilitates reproducibility, increases efficiency for expert users, lowers the learning curve for inexperienced users, and bridges transcriptional data analysis with the tidyverse. Tidybulk is available at R/Bioconductor bioconductor.org/packages/tidybulk.Keywords
This publication has 40 references indexed in Scilit:
- Software for Computing and Annotating Genomic RangesPLoS Computational Biology, 2013
- An integrated encyclopedia of DNA elements in the human genomeNature, 2012
- clusterProfiler: an R Package for Comparing Biological Themes Among Gene ClustersOMICS: A Journal of Integrative Biology, 2012
- The sva package for removing batch effects and other unwanted variation in high-throughput experimentsBioinformatics, 2012
- Molecular signatures database (MSigDB) 3.0Bioinformatics, 2011
- Conservation of an RNA regulatory map between Drosophila and mammalsGenome Research, 2010
- Permutation and Parametric Bootstrap Tests for Gene-Gene and Gene-Environment InteractionsAnnals of Human Genetics, 2010
- A scaling normalization method for differential expression analysis of RNA-seq dataGenome Biology, 2010
- edgeR: a Bioconductor package for differential expression analysis of digital gene expression dataBioinformatics, 2009
- Capturing Heterogeneity in Gene Expression Studies by Surrogate Variable AnalysisPLoS Genetics, 2007