Estimating the null distribution for conditional inference and genome-scale screening

TL;DR

This paper introduces a method for estimating the null distribution in large-scale genomic testing, improving conditional inference and decision-making by quantifying the benefits of conditioning on the estimated null.

Contribution

It proposes a novel approach to estimate the null distribution for genome-scale testing and introduces an information-theoretic score to guide inference decisions.

Findings

Conditioning on the estimated null improves inference accuracy.

The score quantifies the trade-off between ancillarity and relevance.

Applications demonstrate practical benefits in gene expression analysis.

Abstract

In a novel approach to the multiple testing problem, Efron (2004; 2007) formulated estimators of the distribution of test statistics or nominal p-values under a null distribution suitable for modeling the data of thousands of unaffected genes, non-associated single-nucleotide polymorphisms, or other biological features. Estimators of the null distribution can improve not only the empirical Bayes procedure for which it was originally intended, but also many other multiple comparison procedures. Such estimators serve as the groundwork for the proposed multiple comparison procedure based on a recent frequentist method of minimizing posterior expected loss, exemplified with a non-additive loss function designed for genomic screening rather than for validation. The merit of estimating the null distribution is examined from the vantage point of conditional inference in the remainder of the paper. In a simulation study of genome-scale multiple testing, conditioning the observed confidence level on the estimated null distribution as an approximate ancillary statistic markedly improved conditional inference. To enable researchers to determine whether to rely on a particular estimated null distribution for inference or decision making, an information-theoretic score is provided that quantifies the benefit of conditioning. As the sum of the degree of ancillarity and the degree of inferential relevance, the score reflects the balance conditioning would strike between the two conflicting terms. Applications to gene expression microarray data illustrate the methods introduced.