Independent Hypothesis Weighting
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. IHW is described in the following paper:
N. Ignatiadis, B. Klaus, J.B. Zaugg, W. Huber. Data-driven hypothesis weighting increases detection power in genome-scale multiple testing. Nature methods. 2016 Jul;13(7):577-80.
Also see the following paper for the theoretical underpinning of the method:
N. Ignatiadis and W. Huber. Covariate-powered cross weighted multiple testing. [arXiv]
The package is available on Bioconductor, and may be installed as follows:
if (!requireNamespace("BiocManager", quietly=TRUE))install.packages("BiocManager")BiocManager::install("IHW")
The package can be installed as follows with devtools from the Github repository:
devtools::install_github("nignatiadis/IHW")