Ratnasiri Kalani, Mach Sara N, Blish Catherine A, Khatri Purvesh
Stanford Immunology Program, Stanford University School of Medicine, Stanford, CA, 94305, USA.
Department of Epidemiology and Population Health, Stanford University, Stanford, CA, USA.
bioRxiv. 2025 Jun 8:2025.06.04.657898. doi: 10.1101/2025.06.04.657898.
Traditional differential gene expression methods are limited for analysis of single cell RNA-sequencing (scRNA-seq) studies that use paired repeated measures and matched cohort designs. Many existing approaches consider cells as independent samples, leading to high false positive rates while ignoring inherent sampling structures. Although pseudobulk methods address this, they ignore intra-sample expression variability and have higher false negatives rates. We propose a novel meta-analysis approach that accounts for biological replicates and cell variability in paired scRNA-seq data. Using both real and synthetic datasets, we show that our method, single-cell MetaIntegrator (https://github.com/Khatri-Lab/scMetaIntegrator), provides robust effect size estimates and reproducible p-values.
传统的差异基因表达方法在分析使用配对重复测量和匹配队列设计的单细胞RNA测序（scRNA-seq）研究时存在局限性。许多现有方法将细胞视为独立样本，导致假阳性率很高，同时忽略了固有的抽样结构。尽管伪批量方法解决了这个问题，但它们忽略了样本内的表达变异性，并且假阴性率更高。我们提出了一种新的荟萃分析方法，该方法考虑了配对scRNA-seq数据中的生物学重复和细胞变异性。使用真实和合成数据集，我们表明我们的方法单细胞MetaIntegrator（https://github.com/Khatri-Lab/scMetaIntegrator）提供了稳健的效应大小估计和可重复的p值。
