Article

Journal of Agricultural, Biological, and Environmental Statistics

, Volume 11, Issue 3, pp 337-356

Estimating the number of true null hypotheses from a histogram of p values

  • Dan NettletonAffiliated withDepartment of Statistics, Iowa State University Email author 
  • , J. T. Gene HwangAffiliated withDepartments of Mathematics and Statistics, Cornell University
  • , Rico A. CaldoAffiliated withDepartment of Plant Pathology and Center for Plant Responses to Environmental Stresses, Iowa State University
  • , Roger P. WiseAffiliated withDepartment of Plant Pathology and Center for Plant Responses to Environmental Stresses, Iowa State UniversityUSDA-ARS-Corn Insects and Crop Genetics Research Unit, Iowa State University

Rent the article at a discount

Rent now

* Final gross prices may vary according to local VAT.

Get Access

Abstract

In an earlier article, an intuitively appealing method for estimating the number of true null hypotheses in a multiple test situation was proposed. That article presented an iterative algorithm that relies on a histogram of observed p values to obtain the estimator. We characterize the limit of that iterative algorithm and show that the estimator can be computed directly without iteration. We compare the performance of the histogram-based estimator with other procedures for estimating the number of true null hypotheses from a collection of observed p values and find that the histogram-based estimator performs well in settings similar to those encountered in microarray data analysis. We demonstrate the approach using p values from a large microarray experiment aimed at uncovering molecular mechanisms of barley resistance to a fungal pathogen.

Key Words

False discovery rate Microarray data Multiple testing