brt: Biological Relevance Testing

Analyses of large-scale -omics datasets commonly use p-values as the indicators of statistical significance. However, considering p-value alone neglects the importance of effect size (i.e., the mean difference between groups) in determining the biological relevance of a significant difference. Here, we present a novel algorithm for computing a new statistic, the biological relevance testing (BRT) index, in the frequentist hypothesis testing framework to address this problem.

Version: 1.3.0
Depends: R (≥ 3.2.0)
Imports: stats, ggplot2
Suggests: knitr, rmarkdown, reshape2, vsn, DESeq2, pasilla
Published: 2018-05-01
Author: Le Zheng[aut], Peng Yu[aut, cre]
Maintainer: Le Zheng <lzheng.chn at>
License: GPL-2 | GPL-3 [expanded from: GPL (≥ 2)]
NeedsCompilation: no
CRAN checks: brt results


Reference manual: brt.pdf
Vignettes: brt workflow using simulated data and count data
Package source: brt_1.3.0.tar.gz
Windows binaries: r-devel:, r-release:, r-oldrel:
OS X binaries: r-release: brt_1.3.0.tgz, r-oldrel: brt_1.2.0.tgz
Old sources: brt archive


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