Hardcastle, J T (2026). baySeq: empirical Bayesian methods for identifying differential expression in sequence count data.. doi:10.18129/B9.bioc.baySeq. https://github.com/SamGG/baySeq/baySeq - R package version 2.47.0, http://www.bioconductor.org/packages/baySeq.

Hardcastle, J T (2016 Jan 15). “Generalized empirical Bayesian methods for discovery of differential data in high-throughput biology.” Bioinformatics. doi:10.1093/bioinformatics/btv569. https://academic.oup.com/bioinformatics/article/32/2/195/1744387.

Hardcastle, J T, Kelly, A K (2010 Aug 10). “baySeq: empirical Bayesian methods for identifying differential expression in sequence count data.” BMC Bioinformatics. doi:10.1186/1471-2105-11-422. https://bmcbioinformatics.biomedcentral.com/articles/10.1186/1471-2105-11-422.

Corresponding BibTeX entries:

  @Manual{,
    title = {baySeq: empirical Bayesian methods for identifying
      differential expression in sequence count data.},
    author = {{Hardcastle} and Thomas J},
    year = {2026},
    url = {http://www.bioconductor.org/packages/baySeq},
    note = {https://github.com/SamGG/baySeq/baySeq - R package version
      2.47.0},
    doi = {10.18129/B9.bioc.baySeq},
  }

  @Article{,
    title = {Generalized empirical Bayesian methods for discovery of
      differential data in high-throughput biology.},
    author = {{Hardcastle} and Thomas J},
    year = {2016 Jan 15},
    journal = {Bioinformatics},
    doi = {10.1093/bioinformatics/btv569},
    url =
      {https://academic.oup.com/bioinformatics/article/32/2/195/1744387},
  }

  @Article{,
    title = {baySeq: empirical Bayesian methods for identifying
      differential expression in sequence count data.},
    author = {{Hardcastle} and Thomas J and {Kelly} and Krystyna A},
    year = {2010 Aug 10},
    journal = {BMC Bioinformatics},
    doi = {10.1186/1471-2105-11-422},
    url =
      {https://bmcbioinformatics.biomedcentral.com/articles/10.1186/1471-2105-11-422},
  }