Independent hypothesis weighting (IHW) is a multiple testing procedure that increases power compared to the method of Benjamini and Hochberg by assigning data-driven weights to each hypothesis. The input to IHW is a two-column table of p-values and covariates. The covariate can be any continuous-valued or categorical variable that is thought to be informative on the statistical properties of each hypothesis test, while it is independent of the p-value under the null hypothesis.
|Author||Nikos Ignatiadis [aut, cre], Wolfgang Huber [aut]|
|Date of publication||None|
|Maintainer||Nikos Ignatiadis <firstname.lastname@example.org>|
get_bh_threshold: Data-driven threshold of Benjamini Hochberg Procedure
groups_by_filter: Stratify hypotheses based on increasing value of the...
ihw.default: ihw: Main function for Independent Hypothesis Weighting
ihw.DESeqResults: ihw.DESeqResults: IHW method dispatching on DESeqResults...
ihwResult-class: An S4 class to represent the ihw output.
plot-ihwResult-method: Plot functions for IHW