Robust hyperparameter estimation protects against hypervariable genes and improves power to detect differential expression

TL;DR

This paper introduces a robust hyperparameter estimation method for empirical Bayes differential expression analysis, reducing false positives from outliers and increasing detection power, with implementation in the limma package.

Contribution

It presents a novel robust hyperparameter estimation procedure that improves differential expression testing by handling outliers more effectively.

Findings

Robust method reduces false positives from hypervariable genes.

Increases power to detect true differentially expressed genes.

Performs well in simulations and real case studies.

Abstract

One of the most common analysis tasks in genomic research is to identify genes that are differentially expressed (DE) between experimental conditions. Empirical Bayes (EB) statistical tests using moderated genewise variances have been very effective for this purpose, especially when the number of biological replicate samples is small. The EB procedures can however be heavily influenced by a small number of genes with very large or very small variances. This article improves the differential expression tests by robustifying the hyperparameter estimation procedure. The robust procedure has the effect of decreasing the informativeness of the prior distribution for outlier genes while increasing its informativeness for other genes. This effect has the double benefit of reducing the chance that hypervariable genes will be spuriously identified as DE while increasing statistical power for the main body of genes. The robust EB algorithm is fast and numerically stable. The procedure allows exact small-sample null distributions for the test statistics and reduces exactly to the original EB procedure when no outlier genes are present. Simulations show that the robustified tests have similar performance to the original tests in the absence of outlier genes but have greater power and robustness when outliers are present. The article includes case studies for which the robust method correctly identifies and downweights genes associated with hidden covariates and detects more genes likely to be scientifically relevant to the experimental conditions. The new procedure is implemented in the limma software package freely available from the Bioconductor repository.