Characterizing and mitigating protocol-dependent gene expression bias in 3' and 5' single-cell RNA sequencing

Single-cell RNA sequencing (scRNA-seq) has enabled large-scale characterization of cellular heterogeneity; yet, integrating datasets generated through different library preparation protocols remains challenging. For instance, comparisons between 10X Genomics 3' and 5' chemistries are complicated by protocol-dependent technical biases imposed by differences in transcript end capture and amplification. While normalization, and often batch correction, is an integral step in preprocessing scRNA-seq datasets, it remains unclear which correction is most appropriate, or even necessary, for reliable cross-protocol comparisons. Here, we systematically characterize protocol-related expression differences using matched 3' and 5' scRNA-seq data from 36 subjects across six datasets spanning multiple tissues. We find that gene expression discrepancies are not pervasive across the whole transcriptome but are instead driven by a relatively small, reproducible subset of protocol-biased genes. These protocol-specific differences were not primarily explained by GC content or gene length. Excluding the affected genes improves cross-protocol concordance, indicating that most genes are directly comparable without aggressive correction. We then benchmark eleven commonly employed normalization approaches and show that while several methods, such as fastMNN, improve statistical alignment when cell populations are well matched, they can distort gene-level signals and inflate differential expression in biologically realistic settings with incomplete cell-type overlap. Taken together, our results demonstrate that protocol bias between 3' and 5' scRNA-seq is limited in scope and that targeted handling of biased genes presents an alternative approach to normalization or batch correction strategies when integrating and comparing data across protocols.