PT  - JOURNAL ARTICLE
AU  - Albert Kuo
AU  - Kasper D. Hansen
AU  - Stephanie C. Hicks
TI  - Quantification and statistical modeling of Chromium-based single-nucleus RNA-sequencing data
AID  - 10.1101/2022.05.20.492835
DP  - 2022 Jan 01
TA  - bioRxiv
PG  - 2022.05.20.492835
4099  - http://biorxiv.org/content/early/2022/05/20/2022.05.20.492835.short
4100  - http://biorxiv.org/content/early/2022/05/20/2022.05.20.492835.full
AB  - 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 pseudo-negative control data from nuclei in mouse cortex sequenced with the 10x Genomics Chromium system, we found that 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.Competing Interest StatementThe authors have declared no competing interest.