scVelo Velocity Models Reference

Mathematical Framework

RNA velocity is based on the kinetic model of transcription:

dx_s/dt = β·x_u - γ·x_s   (spliced dynamics)
dx_u/dt = α(t) - β·x_u    (unspliced dynamics)

Where:

x_s: spliced mRNA abundance
x_u: unspliced (pre-mRNA) abundance
α(t): transcription rate (varies over time)
β: splicing rate
γ: degradation rate

Velocity is defined as: v = dx_s/dt = β·x_u - γ·x_s

v > 0: Gene is being upregulated (more unspliced than expected at steady state)
v < 0: Gene is being downregulated (less unspliced than expected)

Model Comparison

Steady-State (Velocyto, original)

Assumes constant α (transcription rate)
Fits γ using linear regression on steady-state cells
Limitation: Requires identifiable steady states; assumes constant transcription

python

# Use with scVelo for backward compatibility
scv.tl.velocity(adata, mode='steady_state')

Stochastic Model (scVelo v1)

Extends steady-state with variance/covariance terms
Models cell-to-cell variability in mRNA counts
More robust to noise than steady-state

python

scv.tl.velocity(adata, mode='stochastic')

Dynamical Model (scVelo v2, recommended)

Jointly estimates all kinetic rates (α, β, γ) and cell-specific latent time
Does not assume steady state
Identifies induction vs. repression phases
Computes fit_likelihood per gene (quality measure)

python

scv.tl.recover_dynamics(adata, n_jobs=4)
scv.tl.velocity(adata, mode='dynamical')

Kinetic states identified by dynamical model:

State	Description
Induction	α > 0, x_u increasing
Steady-state on	α > 0, constant high expression
Repression	α = 0, x_u decreasing
Steady-state off	α = 0, constant low expression

Velocity Graph

The velocity graph connects cells based on their velocity similarity to neighboring cells' states:

python

scv.tl.velocity_graph(adata)
# Stored in adata.uns['velocity_graph']
# Entry [i,j] = probability that cell i transitions to cell j

Parameters:

n_neighbors: Number of neighbors considered
sqrt_transform: Apply sqrt transform to data (default: False for spliced)
approx: Use approximate nearest neighbor search (faster for large datasets)

Latent Time Interpretation

Latent time τ ∈ [0, 1] for each gene represents:

τ = 0: Gene is at onset of induction
τ = 0.5: Gene is at peak of induction (for a complete cycle)
τ = 1: Gene has returned to steady-state off

Shared latent time is computed by taking the average over all velocity genes, weighted by fit_likelihood.

Quality Metrics

Gene-level

fit_likelihood: Goodness-of-fit of dynamical model (0-1; higher = better)
- Use for filtering driver genes: adata.var[adata.var['fit_likelihood'] > 0.1]
fit_alpha: Transcription rate during induction
fit_gamma: mRNA degradation rate
fit_r2: R² of kinetic fit

Cell-level

velocity_length: Magnitude of velocity vector (cell speed)
velocity_confidence: Coherence of velocity with neighboring cells (0-1)

Dataset-level

python

# Check overall velocity quality
scv.pl.proportions(adata)  # Ratio of spliced/unspliced per cell
scv.pl.velocity_confidence(adata, groupby='leiden')

Parameter Tuning Guide

Parameter	Function	Default	When to Change
`min_shared_counts`	Filter genes	20	Increase for deep sequencing; decrease for shallow
`n_top_genes`	HVG selection	2000	Increase for complex datasets
`n_neighbors`	kNN graph	30	Decrease for small datasets; increase for noisy
`n_pcs`	PCA dimensions	30	Match to elbow in scree plot
`t_max_rank`	Latent time constraint	None	Set if known developmental direction

Integration with Other Tools

CellRank (Fate Prediction)

python

import cellrank as cr
from cellrank.kernels import VelocityKernel, ConnectivityKernel

# Combine velocity and connectivity kernels
vk = VelocityKernel(adata).compute_transition_matrix()
ck = ConnectivityKernel(adata).compute_transition_matrix()
combined = 0.8 * vk + 0.2 * ck

# Compute macrostates (terminal and initial states)
g = cr.estimators.GPCCA(combined)
g.compute_macrostates(n_states=4, cluster_key='leiden')
g.plot_macrostates(which="all")

# Compute fate probabilities
g.compute_fate_probabilities()
g.plot_fate_probabilities()

Scanpy Integration

scVelo works natively with Scanpy's AnnData:

python

import scanpy as sc
import scvelo as scv

# Run standard Scanpy pipeline first
sc.pp.normalize_total(adata)
sc.pp.log1p(adata)
sc.pp.highly_variable_genes(adata)
sc.pp.pca(adata)
sc.pp.neighbors(adata)
sc.tl.umap(adata)
sc.tl.leiden(adata)

# Then add velocity on top
scv.pp.moments(adata)
scv.tl.recover_dynamics(adata)
scv.tl.velocity(adata, mode='dynamical')
scv.tl.velocity_graph(adata)
scv.tl.latent_time(adata)