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 gmail.com> |
| 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: brt_1.3.0.zip, r-release: brt_1.3.0.zip, r-oldrel: brt_1.3.0.zip |
| macOS binaries: | r-release: brt_1.3.0.tgz, r-oldrel: brt_1.3.0.tgz |
| Old sources: | brt archive |
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