Description Usage Arguments Details Value Note References See Also Examples

Given two vectors of p-values from the primary and follow-up studies, returns the adjusted p-values for false discovery rate control on replicability claims. The p-value vectors are only for features selected for follow-up.

1 2 3 | ```
radjust_pf(pv1, pv2, m, c2 = 0.5, l00 = 0, variant = c("none",
"general_dependency", "use_threshold"), threshold = NULL,
alpha = 0.05)
``` |

`pv1` |
numeric vector of p-values from the primary study which
corresponds to the p-values from the follow-up study ( |

`pv2` |
numeric vector of p-values from the follow-up study. |

`m` |
the number of features examined in the primary study (> |

`c2` |
the relative boost to the p-values from the |

`l00` |
a lower bound of the fraction of features (out of m) with true null hypotheses in both studies. For example, for GWAS on the whole genome, the choice of 0.8 is conservative in typical applications. |

`variant` |
- none
the default. - general_dependency
use *m*=m*sum(1/i)*instead of`m` .- use_threshold
c1 is computed given the `threshold` value.
Both variants guarantee that the procedure that declares as replicated all features with r-values below |

`threshold` |
the selection threshold for p-values from the primary study; must be supplied when variant 'use_threshold' is selected, otherwise ignored. |

`alpha` |
The FDR level to control. |

When many hypotheses are simultaneously examined in a primary study, and then a subset of hypotheses are forwarded for follow-up in an independent study, it is of interest to know which findings are replicated across studies. As a measure of replicability of significance, we compute the r-value, i.e. the FDR adjusted replicability p-value, for each hypothesis followed-up. This measure is different than the FDR adjusted p-value in a typical meta-analysis, where a p-value close to zero in one of the studies is enough to declare the finding as highly significant. The FDR r-value for a feature is the smallest FDR level at which we can say that the finding is among the replicated ones.

vector of length of `pv1`

and `pv2`

, containing the r-values.

The function is also available as a web applet: http://www.math.tau.ac.il/~ruheller/App.html

Bogomolov, M. and Heller, R. (2013). Discovering findings that replicate from a primary study of high dimension to a follow-up study. Journal of the American Statistical Association, Vol. 108, No. 504, Pp. 1480-1492.

Heller, R., Bogomolov, M., & Benjamini, Y. (2014). Deciding whether follow-up studies have replicated findings in a preliminary large-scale omics study. Proceedings of the National Academy of Sciences of the United States of America, Vol. 111, No. 46, Pp. 16262<e2><80><93>16267.

`radjust_sym`

for replicability analysis in two symmetric studies.

1 2 3 4 | ```
data(crohn)
rv <- radjust_pf(pv1 = crohn$pv1, pv2 = crohn$pv1, m = 635547, l00 = 0.8)
rv2 <- radjust_pf(pv1 = crohn$pv1, pv2 = crohn$pv1, m = 635547, l00 = 0.8,
variant="use_threshold",threshold = 1e-5)
``` |

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