tidybulk: an R tidy framework for modular transcriptomic data analysis.
Stefano Mangiola, Ramyar Molania, Ruining Dong, Maria A Doyle, Anthony T Papenfuss
Author Information
Stefano Mangiola: Bioinformatics Division, Walter and Eliza Hall Institute of Medical Research, Parkville, VIC, Australia.
Ramyar Molania: Bioinformatics Division, Walter and Eliza Hall Institute of Medical Research, Parkville, VIC, Australia.
Ruining Dong: Bioinformatics Division, Walter and Eliza Hall Institute of Medical Research, Parkville, VIC, Australia.
Maria A Doyle: Peter MacCallum Cancer Centre, Melbourne, VIC, 3000, Australia.
Anthony T Papenfuss: Bioinformatics Division, Walter and Eliza Hall Institute of Medical Research, Parkville, VIC, Australia. papenfuss@wehi.edu.au. ORCID
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 .
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