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

2025-07-28

AI Summary

In RNA-seq differential expression analysis, parametric methods (e.g., DESeq2, edgeR) suffer from false discovery rate (FDR) inflation under mild model misspecification, whereas robust nonparametric alternatives often sacrifice statistical power. To address this trade-off, we propose Nullstrap-DE—a framework that constructs gene-specific null distributions to generate synthetic null data, without altering existing algorithms. Integrating resampling with empirical Bayes shrinkage, Nullstrap-DE achieves asymptotic FDR control while preserving high statistical power. It supports covariate adjustment and is compatible with both negative binomial modeling and Wilcoxon rank-sum tests. Extensive simulations and real-data analyses demonstrate that Nullstrap-DE strictly controls FDR at or below the nominal level while substantially improving detection sensitivity—outperforming DESeq2, edgeR, and Wilcoxon in both FDR calibration and biological interpretability, yielding more biologically plausible differentially expressed genes.

Technology Category

• Machine Learning: Ensemble Methods
• Natural Language Processing: Safety and Robustness
• Search and Optimization: Sampling/Simulation-based Search

Application Category

• Search and Retrieval-Augmented AI: Web learning to rank, online learning, and counterfactual learning for ranking
• Graph Algorithms and Modeling for the Web: Graph neural networks and deep learning approaches for Web-related graphs
• Web Mining and Content Analysis: Robustness and generalizability of Web mining methods

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.

Problem

• Calibrates FDR in DE methods while preserving statistical power
• Addresses FDR inflation in parametric methods like DESeq2 and edgeR
• Enhances robustness and power in differential expression analysis

Innovation

• Combines parametric and non-parametric DE approaches
• Generates synthetic null data for FDR calibration
• Preserves power while controlling false discoveries