Zhou Fanding, Aw Alan J, Erdmann-Pham Dan D, Fischer Jonathan, Song Yun S
Biostatistics Division, University of California, Berkeley.
Department of Statistics, University of California, Berkeley.
bioRxiv. 2025 Mar 11:2025.03.06.641952. doi: 10.1101/2025.03.06.641952.
Differential expression analysis is crucial in genomics, yet existing methods primarily focus on detecting mean shifts. Variance shifts in gene expression are well-documented in studies of cellular signaling pathways, and more recently they have characterized aging, thus motivating the need for flexible detection approaches that include tests of expression variance changes. In this work, we present QRscore (Quantile Rank Score), a general method for detecting distributional shifts in gene expression by extending the Mann-Whitney test into a flexible family of rank-based tests. Here, we focus on implementing QRscore to detect shifts in mean and variance in gene expression, using weights designed from negative binomial (NB) and zero-inflated negative binomial (ZINB) models to combine the strengths of parametric and non-parametric approaches. We show through simulations that QRscore not only achieves high statistical power while controlling the false discovery rate (FDR), but also outperforms existing methods in detecting variance shifts and mean shifts. Applying QRscore to bulk RNA-seq data from the Genotype-Tissue Expression (GTEx) project, we identified numerous differentially dispersed genes and differentially expressed genes across 33 tissues. Notably, many genes have significant variance shifts but non-significant mean shifts. QRscore augments the genome bioinformatics toolkit by offering a powerful and flexible approach for differential expression analysis. QRscore is available in R, at https://github.com/songlab-cal/QRscore.
差异表达分析在基因组学中至关重要，但现有方法主要侧重于检测均值变化。基因表达中的方差变化在细胞信号通路研究中已有充分记录，最近它们还被用于表征衰老，因此需要灵活的检测方法，包括对表达方差变化的检验。在这项工作中，我们提出了QRscore（分位数秩分数），这是一种通过将曼-惠特尼检验扩展为灵活的基于秩的检验族来检测基因表达分布变化的通用方法。在这里，我们专注于实现QRscore以检测基因表达均值和方差的变化，使用从负二项分布（NB）和零膨胀负二项分布（ZINB）模型设计的权重来结合参数化和非参数化方法的优势。我们通过模拟表明，QRscore不仅在控制错误发现率（FDR）的同时实现了高统计功效，而且在检测方差变化和均值变化方面优于现有方法。将QRscore应用于基因型-组织表达（GTEx）项目的批量RNA测序数据，我们在33个组织中鉴定出了大量差异分散基因和差异表达基因。值得注意的是，许多基因有显著的方差变化但均值变化不显著。QRscore通过提供一种强大而灵活的差异表达分析方法，增强了基因组生物信息学工具包。QRscore可在R中获取，网址为https://github.com/songlab-cal/QRscore。
