, vkey='velocity', mode='stochastic', fit_offset=False, fit_offset2=False, filter_genes=False, groups=None, groupby=None, groups_for_fit=None, constrain_ratio=None, use_raw=False, use_latent_time=None, perc=None, min_r2=0.01, min_likelihood=0.001, r2_adjusted=None, use_highly_variable=True, diff_kinetics=None, copy=False, **kwargs)

Estimates velocities in a gene-specific manner.

The steady-state model [Manno et al., 2018] determines velocities by quantifying how observations deviate from a presumed steady-state equilibrium ratio of unspliced to spliced mRNA levels. This steady-state ratio is obtained by performing a linear regression restricting the input data to the extreme quantiles. By including second-order moments, the stochastic model [Bergen et al., 2020] exploits not only the balance of unspliced to spliced mRNA levels but also their covariation. By contrast, the likelihood-based dynamical model [Bergen et al., 2020] solves the full splicing kinetics and generalizes RNA velocity estimation to transient systems. It is also capable of capturing non-observed steady states.
data: AnnData

Annotated data matrix.

vkey: str (default: ‘velocity’)

Name under which to refer to the computed velocities for velocity_graph and velocity_embedding.

mode: ‘deterministic’, ‘stochastic’ or ‘dynamical’ (default: ‘stochastic’)

Whether to run the estimation using the steady-state/deterministic, stochastic or dynamical model of transcriptional dynamics. The dynamical model requires to run tl.recover_dynamics first.

fit_offset: bool (default: False)

Whether to fit with offset for first order moment dynamics.

fit_offset2: bool, (default: False)

Whether to fit with offset for second order moment dynamics.

filter_genes: bool (default: True)

Whether to remove genes that are not used for further velocity analysis.

groups: str, list (default: None)

Subset of groups, e.g. [‘g1’, ‘g2’, ‘g3’], to which velocity analysis shall be restricted.

groupby: str, list or np.ndarray (default: None)

Key of observations grouping to consider.

groups_for_fit: str, list or np.ndarray (default: None)

Subset of groups, e.g. [‘g1’, ‘g2’, ‘g3’], to which steady-state fitting shall be restricted.

constrain_ratio: float or tuple of type float or None: (default: None)

Bounds for the steady-state ratio.

use_raw: bool (default: False)

Whether to use raw data for estimation.

use_latent_time: bool`or `None (default: None)

Whether to use latent time as a regularization for velocity estimation.

perc: float (default: [5, 95])

Percentile, e.g. 98, for extreme quantile fit.

min_r2: float (default: 0.01)

Minimum threshold for coefficient of determination

min_likelihood: float (default: None)

Minimal likelihood for velocity genes to fit the model on.

r2_adjusted: bool (default: None)

Whether to compute coefficient of determination on full data fit (adjusted) or extreme quantile fit (None)

use_highly_variable: bool (default: True)

Whether to use highly variable genes only, stored in .var[‘highly_variable’].

copy: bool (default: False)

Return a copy instead of writing to adata.


  • velocity (.layers) – velocity vectors for each individual cell

  • velocity_genes, velocity_beta, velocity_gamma, velocity_r2 (.var) – parameters