Nullstrap-DE: A General Framework for Calibrating FDR and Preserving Power in DE Methods, with Applications to DESeq2 and edgeR

TL;DR

Nullstrap-DE is a versatile framework that calibrates FDR control in differential expression analysis, improving reliability and power in RNA-seq studies without altering existing methods.

Contribution

It introduces a model-agnostic add-on that ensures FDR calibration and power preservation for popular DE tools like DESeq2 and edgeR.

Findings

Nullstrap-DE achieves reliable FDR control in simulations.

It maintains high power comparable to original methods.

Applications identify more biologically meaningful genes.

Abstract

Differential expression (DE) analysis is a key task in RNA-seq studies, aiming to identify genes with expression differences across conditions. A central challenge is balancing false discovery rate (FDR) control with statistical power. Parametric methods such as DESeq2 and edgeR achieve high power by modeling gene-level counts using negative binomial distributions and applying empirical Bayes shrinkage. However, these methods may suffer from FDR inflation when model assumptions are mildly violated, especially in large-sample settings. In contrast, non-parametric tests like Wilcoxon offer more robust FDR control but often lack power and do not support covariate adjustment. We propose Nullstrap-DE, a general add-on framework that combines the strengths of both approaches. Designed to augment tools like DESeq2 and edgeR, Nullstrap-DE calibrates FDR while preserving power, without modifying the original method's implementation. It generates synthetic null data from a model fitted under the gene-specific null (no DE), applies the same test statistic to both observed and synthetic data, and derives a threshold that satisfies the target FDR level. We show theoretically that Nullstrap-DE asymptotically controls FDR while maintaining power consistency. Simulations confirm that it achieves reliable FDR control and high power across diverse settings, where DESeq2, edgeR, or Wilcoxon often show inflated FDR or low power. Applications to real datasets show that Nullstrap-DE enhances statistical rigor and identifies biologically meaningful genes.