IHW
IHW – Independent Hypothesis Weighting for Multiple Testing
IHW (Independent Hypothesis Weighting) is an R/Bioconductor package for multiple testing with false discovery rate control. It increases statistical power compared to commonly used FDR procedures by assigning data-driven weights to individual hypotheses. The package provides a flexible implementation that can be applied to different data types and analysis settings where large numbers of hypotheses are tested.
Key benefits
Supports false discovery rate control in multiple testing
Increases detection power through data-driven hypothesis weighting
Flexible implementation applicable to different data types
Integrates into R and Bioconductor-based analysis workflows
Useful for high-throughput biological data analysis
Applications
Multiple testing correction in high-throughput experiments
Differential expression and differential abundance analyses
Genome-wide, transcriptome-wide, or proteome-wide statistical testing
Increasing power in large-scale hypothesis testing workflows
Integration into reproducible statistical analysis pipelines
Intended use
IHW is intended for bioinformaticians, biostatisticians, statistical genomics researchers, and life science researchers performing large-scale hypothesis testing. It is particularly suited for analyses where false discovery rate control is required and additional covariate information can be used to improve statistical power.
Contact:
Website