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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.
Package details |
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Author | Le Zheng[aut], Peng Yu[aut, cre] |
Maintainer | Le Zheng <lzheng.chn@gmail.com> |
License | GPL (>= 2) |
Version | 1.3.0 |
Package repository | View on CRAN |
Installation |
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