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DAtest: a framework for choosing differential abundance or expression method

Jakob RusseliD, Jonathan ThorseniD, Asker BrejnrodiD, Hans Flinker BisgaardiD, Søren Johannes SørenseniD, Mette BurmølleiD

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Abstract

DAtest is an R package for directly comparing different statistical methods for differential abundance and expression analysis on a dataset of interest; be it data from RNA-seq, proteomics, metabolomics or a microbial marker-gene survey. A myriad of statistical methods exists for conducting these analyses, and with this tool we give the analyst an empirical foundation for choosing a method suitable for a specific dataset. The package supports categorical and quantitative variables, paired/block experimental designs, and the inclusion of covariates. It is freely available at GitHub: https://github.com/Russel88/DAtest along with detailed instructions.

Abstract summary

Claude Opus 5.5 · xhigh effort · 2 Nov 2026 · from the abstract · CC BY-NC-ND 4.0

DAtest is an R package that empirically compares statistical methods for differential expression or abundance directly on a user's own dataset across diverse omics types.

Numerous statistical tests exist for differential abundance and expression analysis, making it difficult to select an appropriate approach for a given dataset. DAtest provides an empirical framework to directly compare candidate methods on the specific dataset of interest.

Key results

  1. ClaimedDAtest enables direct empirical comparison of statistical methods for differential expression and abundance on user-supplied datasets across RNA-seq, proteomics, metabolomics, and microbial marker-gene surveys.

    Described as an R package supporting categorical and quantitative variables, paired or block designs, and covariates; no quantitative benchmark results are reported in the abstract. · Abstract

Methods, data and reproducibility

The authors developed an R package (DAtest) that evaluates and compares statistical tests for differential expression and abundance on a target dataset, accommodating categorical or quantitative variables, paired or block experimental designs, and covariates.

Methods & tools
DAtest, R
Software & environment
R package
Reproducibility gaps
No example datasets, R version requirements, or benchmark data links are provided in the abstract.
Limitations (2)
  • The abstract does not specify which differential abundance or expression methods are included in the comparison or how empirical performance is scored. · noted in this summary
  • No benchmark datasets or quantitative validation results are presented in the abstract. · noted in this summary

Read this if you want to empirically benchmark differential expression or abundance tests on your own RNA-seq, proteomics, metabolomics, or microbiome dataset in R.

Citations
59

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