Preprocessing choices affect RNA velocity results for droplet scRNA-seq data.

Area of Science:

• Single-cell RNA sequencing (scRNA-seq)
• Computational biology
• Systems biology

Background:

• Single-cell experimental approaches are crucial for studying dynamic biological systems.
• RNA velocity analysis infers future cell states by modeling spliced and unspliced RNA.
• Accurate abundance quantification is essential for reliable RNA velocity estimation.

Purpose of the Study:

• To systematically compare quantification tools for spliced and unspliced RNA abundances in droplet scRNA-seq data.
• To assess the impact of quantification differences on downstream RNA velocity analysis.
• To identify genes exhibiting significant quantification discrepancies.

Main Methods:

• Comparative analysis of five widely used quantification tools.
• Evaluation of thirteen distinct quantification approaches.
• Assessment across five experimental droplet scRNA-seq datasets.

Main Results:

• Substantial differences in spliced and unspliced RNA abundance estimates were observed between tools.
• Specific genes were identified as displaying typical quantification discrepancies.
• These abundance differences significantly affected downstream RNA velocity calculations and biological interpretations.

Conclusions:

• Abundance quantification is a critical step in the RNA velocity analysis workflow.
• Careful consideration of genomic feature definition and quantification algorithms is necessary.
• Tool selection and parameter choices can profoundly influence scRNA-seq dynamic inference.