The permuted score test for robust differential expression analysis

TL;DR

This paper introduces the permuted score test, a robust method for differential expression analysis in RNA sequencing data that better controls false positives without sacrificing power, outperforming existing NB regression methods.

Contribution

The paper proposes the permuted score test, a new robust regression approach that improves error control in differential expression analysis while maintaining efficiency and power.

Findings

Significantly reduces false positives compared to DESeq2.

Maintains comparable power to standard NB regression under assumptions.

Effective in both real and simulated RNA sequencing datasets.

Abstract

Negative binomial (NB) regression is a popular method for identifying differentially expressed genes in genomics data, such as bulk and single-cell RNA sequencing data. However, NB regression makes stringent parametric and asymptotic assumptions, which can fail to hold in practice, leading to excess false positive and false negative results. We propose the permuted score test, a new strategy for robust regression based on permuting score test statistics. The permuted score test provably controls type-I error across a much broader range of settings than standard NB regression while nevertheless approximately matching standard NB regression with respect to power (when the assumptions of standard NB regression obtain) and computational efficiency. We accelerate the permuted score test by leveraging emerging techniques for sequential Monte-Carlo testing and novel algorithms for efficiently computing GLM score tests. We apply the permuted score test to real and simulated RNA sequencing data, finding that it substantially improves upon the error control of existing NB regression implementations, including DESeq2. The permuted score test could enhance the reliability of differential expression analysis across diverse biological contexts.