Quantification and statistical modeling of droplet-based single-nucleus RNA-sequencing data

In complex tissues containing cells that are difficult to dissociate, single-nucleus RNA-sequencing (snRNA-seq) has become the preferred experimental technology over single-cell RNA-sequencing (scRNA-seq) to measure gene expression. To accurately model these data in downstream analyses, previous work has shown that droplet-based scRNA-seq data are not zero-inflated, but whether droplet-based snRNA-seq data follow the same probability distributions has not been systematically evaluated. Using pseudonegative control data from nuclei in mouse cortex sequenced with the 10x Genomics Chromium system and mouse kidney sequenced with the DropSeq system, we found that droplet-based snRNA-seq data follow a negative binomial distribution, suggesting that parametric statistical models applied to scRNA-seq are transferable to snRNA-seq. Furthermore, we found that the quantification choices in adapting quantification mapping strategies from scRNA-seq to snRNA-seq can play a significant role in downstream analyses and biological interpretation. In particular, reference transcriptomes that do not include intronic regions result in significantly smaller library sizes and incongruous cell type classifications. We also confirmed the presence of a gene length bias in snRNA-seq data, which we show is present in both exonic and intronic reads, and investigate potential causes for the bias.

Automated summary

In complex tissues, single-nucleus RNA-sequencing (snRNA-seq) is preferred over single-cell RNA-sequencing (scRNA-seq) to measure gene expression. We show that droplet-based snRNA-seq data follow a negative binomial distribution and that quantification choices in adapting scRNA-seq to snRNA-seq can impact downstream analyses. Additionally, we confirm the presence of a gene length bias in snRNA-seq data and explore potential causes for the bias.