scvelo.tl.velocity_graph

scvelo.tl.velocity_graph(data, vkey='velocity', xkey='Ms', tkey=None, basis=None, n_neighbors=None, n_recurse_neighbors=None, random_neighbors_at_max=None, sqrt_transform=None, variance_stabilization=None, gene_subset=None, compute_uncertainties=None, approx=None, mode_neighbors='distances', copy=False, n_jobs=None, backend='loky', show_progress_bar=True)

Computes velocity graph based on cosine similarities.

The cosine similarities are computed between velocities and potential cell state transitions, i.e. it measures how well a corresponding change in gene expression \(\delta_{ij} = x_j - x_i\) matches the predicted change according to the velocity vector \(\nu_i\),

\[\pi_{ij} = \cos\angle(\delta_{ij}, \nu_i) = \frac{\delta_{ij}^T \nu_i}{\left\lVert\delta_{ij}\right\rVert \left\lVert \nu_i \right\rVert}.\]
Parameters:

  • data (AnnData) – Annotated data matrix.

  • vkey (str (default: ‘velocity’)) – Name of velocity estimates to be used.

  • xkey (str (default: ‘Ms’)) – Layer key to extract count data from.

  • tkey (str (default: None)) – Observation key to extract time data from.

  • basis (str (default: None)) – Basis / Embedding to use.

  • n_neighbors (int or None (default: None)) – Use fixed number of neighbors or do recursive neighbor search (if None).

  • n_recurse_neighbors (int (default: None)) – Number of recursions for neighbors search. Defaults to 2 if mode_neighbors is ‘distances’, and 1 if mode_neighbors is ‘connectivities’.

  • random_neighbors_at_max (int or None (default: None)) – If number of iterative neighbors for an individual cell is higher than this threshold, a random selection of such are chosen as reference neighbors.

  • sqrt_transform (bool (default: False)) – Whether to variance-transform the cell states changes and velocities before computing cosine similarities.

  • gene_subset (list of str, subset of adata.var_names or None`(default: `None)) – Subset of genes to compute velocity graph on exclusively.

  • compute_uncertainties (bool (default: None)) – Whether to compute uncertainties along with cosine correlation.

  • approx (bool or None (default: None)) – If True, first 30 pc’s are used instead of the full count matrix

  • mode_neighbors (‘str’ (default: ‘distances’)) – Determines the type of KNN graph used. Options are ‘distances’ or ‘connectivities’. The latter yields a symmetric graph.

  • copy (bool (default: False)) – Return a copy instead of writing to adata.

  • n_jobs (int or None (default: None)) – Number of parallel jobs.

  • backend (str (default: “loky”)) – Backend used for multiprocessing. See joblib.Parallel for valid options.

  • show_progress_bar (bool) – Whether to show a progress bar.

Returns:

velocity_graph – sparse matrix with correlations of cell state transitions with velocities

Return type:

.uns