TY  - JOUR
A1  - Chamberlin, John T.
A1  - Lee, Younghee
A1  - Marth, Gabor T.
A1  - Quinlan, Aaron R.
T1  - Differences in molecular sampling and data processing explain variation among single-cell and single-nucleus RNA-seq experiments
Y1  - 2024/02/01 
JF  - Genome Research 
JO  - Genome Research 
SP  - 179 
EP  - 188 
DO  - 10.1101/gr.278253.123 
VL  - 34 
IS  - 2 
UR  - http://genome.cshlp.org/content/34/2/179.abstract 
N2  - A mechanistic understanding of the biological and technical factors that impact transcript measurements is essential to designing and analyzing single-cell and single-nucleus RNA sequencing experiments. Nuclei contain the same pre-mRNA population as cells, but they contain a small subset of the mRNAs. Nonetheless, early studies argued that single-nucleus analysis yielded results comparable to cellular samples if pre-mRNA measurements were included. However, typical workflows do not distinguish between pre-mRNA and mRNA when estimating gene expression, and variation in their relative abundances across cell types has received limited attention. These gaps are especially important given that incorporating pre-mRNA has become commonplace for both assays, despite known gene length bias in pre-mRNA capture. Here, we reanalyze public data sets from mouse and human to describe the mechanisms and contrasting effects of mRNA and pre-mRNA sampling on gene expression and marker gene selection in single-cell and single-nucleus RNA-seq. We show that pre-mRNA levels vary considerably among cell types, which mediates the degree of gene length bias and limits the generalizability of a recently published normalization method intended to correct for this bias. As an alternative, we repurpose an existing post hoc gene length–based correction method from conventional RNA-seq gene set enrichment analysis. Finally, we show that inclusion of pre-mRNA in bioinformatic processing can impart a larger effect than assay choice itself, which is pivotal to the effective reuse of existing data. These analyses advance our understanding of the sources of variation in single-cell and single-nucleus RNA-seq experiments and provide useful guidance for future studies. 
ER  -