RESEARCH USE
Where scVelo fits
Start with quality-controlled spliced and unspliced counts from single-cell RNA sequencing, select a steady-state, stochastic, or dynamical model that fits the research question, and project the estimated velocity onto an existing neighborhood graph or embedding. Preserve the original counts, preprocessing, gene selection, model mode, software version, and random seeds. Compare directions, latent time, and candidate driver genes with independent time information or experimental evidence.
Research tasks
- Estimate local directions of cell-state change
- Infer latent time and transcriptional-dynamics parameters
- Explore candidate genes associated with state transitions
What to evaluate before use
- RNA velocity depends on splicing kinetics, data quality, and model assumptions. Arrows or streamlines are not direct observations of lineage and do not establish causal direction.
- Preprocessing, neighborhood graphs, embeddings, and gene selection can change the result. Consequential interpretations need sensitivity analysis and independent validation.
Verification note
This entry summarizes the resource's role without assessing scientific accuracy or endorsing its outputs. Features and terms can change; consult the official source before adopting it for consequential work.
Last verified: 2026-09-18
Source: official documentation ↗