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.

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Description file content

Package
brt
Type
Package
Title
Biological Relevance Testing
Version
1.3.0
Date
2018-05-01
Author
Le Zheng[aut], Peng Yu[aut, cre]
Maintainer
Le Zheng
License
GPL (>= 2)
VignetteBuilder
knitr
Suggests
knitr, rmarkdown, reshape2, vsn, DESeq2, pasilla
Depends
R (>= 3.2.0)
Imports
stats, ggplot2
RoxygenNote
6.0.1
Description
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.
NeedsCompilation
no
Packaged
2018-05-01 04:16:49 UTC; Le
Repository
CRAN
Date/Publication
2018-05-01 04:32:09 UTC

install.packages('brt')

1.3.0

7 months ago

Le Zheng

GPL (>= 2)

Depends on

R (>= 3.2.0)

Imports

stats, ggplot2

Suggests

knitr, rmarkdown, reshape2, vsn, DESeq2, pasilla

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