Removal of unwanted variation in pseudobulk analysis of single-cell RNA sequencing data and the leveraging of pseudoreplicates

Abstract

Removing unwanted variation (RUV) is key for accurate biological interpretation in high-throughput sequencing studies. However, no standardized approach exists for pseudobulked single-cell RNA-sequencing (scRNA-seq) data. Improper implementation of RUV methods may remove biological information, jeopardizing power and false positive control in differential expression analysis. We evaluate the impact of three implementation strategies ('trails') in three RUV methods (RUV2, RUVIII, RUV4) using simulated and real biological signals in pseudobulked scRNA-seq data. Effects of technical noise under confounding and model misspecification conditions are also considered. Additionally, we introduce a novel strategy, RUVIII PBPS, to remove unwanted variation in pseudobulk differential expression analyses with insufficient technical replicates or negative control genes. Our analysis demonstrates that removing unwanted variation per cell type with RUV2 or RUVIII extracts factors associated with technical noise and controls the false discovery rate (FDR), even in the presence of confounding. RUVIII PBPS successfully controls the FDR when other standard RUV methods cannot be used due to missing technical replicates, dependence between the factor of interest and the sources of unwanted variation, and lack of plausible negative control genes.