Robust and adaptive non-parametric tests for detecting general distributional shifts in gene expression.

ABSTRACT: Differential expression analysis is crucial in genomics, yet most methods focus only on mean shifts. Variance shifts in gene expression-especially in cellular signaling and aging-are increasingly recognized as being biologically important. We present QRscore (quantile rank score), a general non-parametric framework that extends the Mann-Whitney test to detect both mean and variance shifts through model-informed weights derived from negative binomial (NB) and zero-inflated NB (ZINB) distributions. QRscore offers high statistical power with false discovery rate (FDR) control, surpassing existing methods in detecting both types of distributional changes. When applied to bulk RNA sequencing (RNA-seq) data from Genotype-Tissue Expression (GTEx), QRscore uncovers numerous genes with dispersion s

SUBMITTER: Zhou F 

PROVIDER: S-EPMC12539254 | biostudies-literature | 2025 Sep

REPOSITORIES: biostudies-literature