ArticlePLoS computational biology2021
Preprocessing choices affect RNA velocity results for droplet scRNA-seq data.
Article in PLoS computational biology, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 41 papers.
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Who cites it
41 citing papers in PubMed.
- Article
- CellRank: consistent and data view agnostic fate mapping for single-cell genomics.Nature protocols · 2026Review
- Comprehensive benchmarking of RNA velocity methods across single-cell datasets.Genome biology · 2026Article
- Single-cell multi-omics in cancer immunotherapy: from tumor heterogeneity to personalized precision treatment.Molecular cancer · 2025Review
- Paradigms, innovations, and biological applications of RNA velocity: a comprehensive review.Briefings in bioinformatics · 2025Review
- Article
- Accurate quantification of nascent and mature RNAs from single-cell and single-nucleus RNA-seq.Nucleic acids research · 2025Article
- nf-core/marsseq: systematic preprocessing pipeline for MARS-seq experiments.Bioinformatics advances · 2025Article
- Accurate RNA velocity estimation based on multibatch network reveals complex lineage in batch scRNA-seq data.BMC biology · 2024Article
- Single-Cell RNA Sequencing of PBMCs Identified Junction Plakoglobin (JUP) as Stratification Biomarker for Endometriosis.International journal of molecular sciences · 2024Article
- A global transcriptional atlas of the effect of acute sleep deprivation in the mouse frontal cortex.iScience · 2024Article
- Quantification and statistical modeling of droplet-based single-nucleus RNA-sequencing data.Biostatistics (Oxford, England) · 2024Article
- Dissection and integration of bursty transcriptional dynamics for complex systems.Proceedings of the National Academy of Sciences of the United States of America · 2024Article
- BEERS2: RNA-Seq simulation through high fidelity in silico modeling.Briefings in bioinformatics · 2024Article
- Differences in molecular sampling and data processing explain variation among single-cell and single-nucleus RNA-seq experiments.Genome research · 2024Article
- TFvelo: gene regulation inspired RNA velocity estimation.Nature communications · 2024Article
- Molecularly stratified hypothalamic astrocytes are cellular foci for obesity.Research square · 2024Article
- Forseti: A mechanistic and predictive model of the splicing status of scRNA-seq reads.bioRxiv : the preprint server for biology · 2024Article
- Deep generative modeling of transcriptional dynamics for RNA velocity analysis in single cells.Nature methods · 2024Article
- A Global Transcriptional Atlas of the Effect of Sleep Deprivation in the Mouse Frontal Cortex.bioRxiv : the preprint server for biology · 2023Article
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4 authors.
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Abstract
Experimental single-cell approaches are becoming widely used for many purposes, including investigation of the dynamic behaviour of developing biological systems. Consequently, a large number of computational methods for extracting dynamic information from such data have been developed. One example is RNA velocity analysis, in which spliced and unspliced RNA abundances are jointly modeled in order to infer a 'direction of change' and thereby a future state for each cell in the gene expression space. Naturally, the accuracy and interpretability of the inferred RNA velocities depend crucially on the correctness of the estimated abundances. Here, we systematically compare five widely used quantification tools, in total yielding thirteen different quantification approaches, in terms of their estimates of spliced and unspliced RNA abundances in five experimental droplet scRNA-seq data sets. We show that there are substantial differences between the quantifications obtained from different tools, and identify typical genes for which such discrepancies are observed. We further show that these abundance differences propagate to the downstream analysis, and can have a large effect on estimated velocities as well as the biological interpretation. Our results highlight that abundance quantification is a crucial aspect of the RNA velocity analysis workflow, and that both the definition of the genomic features of interest and the quantification algorithm itself require careful consideration.
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