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.
|Author||Le Zheng[aut], Peng Yu[aut, cre]|
|Maintainer||Le Zheng <[email protected]>|
|License||GPL (>= 2)|
|Package repository||View on CRAN|
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