README.md

In nignatiadis/IHW: Independent Hypothesis Weighting

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]

Software availability

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")

nignatiadis/IHW documentation built on Aug. 22, 2023, 2:11 p.m.