# cellrank.kernels.VelocityKernel#

class cellrank.kernels.VelocityKernel(adata, backward=False, xkey='Ms', vkey='velocity', **kwargs)[source]#

Kernel which computes a transition matrix based on RNA velocity.

This borrows ideas from both and . In short, for each cell i, we compute transition probabilities $$p_{i, j}$$ to each cell j in the neighborhood of i. The transition probabilities are computed as a multinomial logistic regression where the weights $$w_j$$ (for all j) are given by the vector that connects cell i with cell j in gene expression space, and the features $$x_i$$ are given by the velocity vector $$v_i$$ of cell i.

Parameters:

## Attributes table#

 adata Annotated data object. backward Direction of the process. connectivities Underlying connectivity matrix. kernels Underlying base kernels. logits Array of shape (n_cells, n_cells) containing not row-normalized transition matrix. params Parameters which are used to compute the transition matrix. shape (n_cells, n_cells). transition_matrix Row-normalized transition matrix.

## Methods table#

 compute_transition_matrix([model, ...]) Compute transition matrix based on velocity directions on the local manifold. copy(*[, deep]) Return a copy of self. from_adata(adata, key[, copy]) Read kernel object saved using write_to_adata(). plot_projection([basis, key_added, ...]) Plot transition_matrix as a stream or a grid plot. plot_random_walks([n_sims, max_iter, seed, ...]) Plot random walks in an embedding. plot_single_flow(cluster, cluster_key, time_key) Visualize outgoing flow from a cluster of cells . read(fname[, adata, copy]) De-serialize self from a file. write(fname[, write_adata, ext]) Serialize self to a file. write_to_adata([key, copy]) Write the transition matrix and parameters used for computation to the underlying adata object.

## Attributes#

Annotated data object.

Return type:

AnnData

### backward#

VelocityKernel.backward#

Direction of the process.

Return type:

bool

### connectivities#

VelocityKernel.connectivities#

Underlying connectivity matrix.

Return type:

csr_matrix

### kernels#

VelocityKernel.kernels#

Underlying base kernels.

Return type:

Tuple[KernelExpression, ...]

### logits#

VelocityKernel.logits#

Array of shape (n_cells, n_cells) containing not row-normalized transition matrix.

Return type:

### params#

VelocityKernel.params#

Parameters which are used to compute the transition matrix.

Return type:

Dict[str, Any]

### shape#

VelocityKernel.shape#

(n_cells, n_cells).

Return type:

Tuple[int, int]

### transition_matrix#

VelocityKernel.transition_matrix#

Row-normalized transition matrix.

Return type:

## Methods#

### compute_transition_matrix#

VelocityKernel.compute_transition_matrix(model=VelocityModel.DETERMINISTIC, backward_mode=BackwardMode.TRANSPOSE, similarity=Similarity.CORRELATION, softmax_scale=None, n_samples=1000, seed=None, **kwargs)[source]#

Compute transition matrix based on velocity directions on the local manifold.

For each cell, infer transition probabilities based on the cell’s velocity-extrapolated cell state and the cell states of its K nearest neighbors.

Parameters:
• mode

How to compute transition probabilities. Valid options are:

• ’deterministic’ - deterministic computation that doesn’t propagate uncertainty.

• ’monte_carlo’ - Monte Carlo average of randomly sampled velocity vectors.

• ’stochastic’ - second order approximation, only available when jax is installed.

• backward_mode (Literal[‘transpose’, ‘negate’]) –

Only matters if initialized as backward = True. Valid options are:

• ’transpose’ - compute transitions from neighboring cells $$j$$ to cell $$i$$.

• ’negate’ - negate the velocity vector.

• softmax_scale (Optional[float]) – Scaling parameter for the softmax. If None, it will be estimated using 1 / median(correlations). The idea behind this is to scale the softmax to counter everything tending to orthogonality in high dimensions.

• scheme

Similarity measure between cells as described in . Can be one of the following:

• ’correlation’ - cellrank.kernels.utils.Correlation.

• ’cosine’ - cellrank.kernels.utils.Cosine.

• ’dot_product’ - cellrank.kernels.utils.DotProduct.

Alternatively, any function can be passed as long as it follows the signature of cellrank.kernels.utils.SimilarityABC.__call__().

• n_samples (int) – Number of samples when mode = 'monte_carlo'.

• seed (Optional[int]) – Random seed when mode = 'monte_carlo'.

• show_progress_bar – Whether to show a progress bar. Disabling it may slightly improve performance.

• n_jobs – Number of parallel jobs. If -1, use all available cores. If None or 1, the execution is sequential.

• backend – Which backend to use for parallelization. See joblib.Parallel for valid options.

• kwargs (Any) – Keyword arguments for the underlying model.

Return type:

VelocityKernel

Returns:

Self and updates transition_matrix, logits and params.

### copy#

VelocityKernel.copy(*, deep=False)#

Return a copy of self.

Return type:

Kernel

Parameters:
Return type:

Kernel

Returns:

The kernel with explicitly initialized properties:

### plot_projection#

VelocityKernel.plot_projection(basis='umap', key_added=None, recompute=False, stream=True, connectivities=None, **kwargs)#

Plot transition_matrix as a stream or a grid plot.

Parameters:
Return type:

None

Returns:

Nothing, just plots and modifies anndata.AnnData.obsm with a key based on key_added.

### plot_random_walks#

VelocityKernel.plot_random_walks(n_sims=100, max_iter=0.25, seed=None, successive_hits=0, start_ixs=None, stop_ixs=None, basis='umap', cmap='gnuplot', linewidth=1.0, linealpha=0.3, ixs_legend_loc=None, n_jobs=None, backend='loky', show_progress_bar=True, figsize=None, dpi=None, save=None, **kwargs)#

Plot random walks in an embedding.

This method simulates random walks on the Markov chain defined though the corresponding transition matrix. The method is intended to give qualitative rather than quantitative insights into the transition matrix. Random walks are simulated by iteratively choosing the next cell based on the current cell’s transition probabilities.

Parameters:
• n_sims (int) – Number of random walks to simulate.

• max_iter (Union[int, float]) – Maximum number of steps of a random walk. If a float, it can be specified as a fraction of the number of cells.

• seed (Optional[int]) – Random seed.

• successive_hits (int) – Number of successive hits in the stop_ixs required to stop prematurely.

• start_ixs (Union[Sequence[str], Mapping[str, Union[str, Sequence[str], Tuple[float, float]]], None]) –

Cells from which to sample the starting points. If None, use all cells. Can be specified as:

For example {'dpt_pseudotime': [0, 0.1]} means that starting points for random walks will be sampled uniformly from cells whose pseudotime is in [0, 0.1].

• stop_ixs (Union[Sequence[str], Mapping[str, Union[str, Sequence[str], Tuple[float, float]]], None]) –

Cells which when hit, the random walk is terminated. If None, terminate after max_iters. Can be specified as:

For example {'clusters': ['Alpha', 'Beta']} and successive_hits = 3 means that the random walk will stop prematurely after cells in the above specified clusters have been visited successively 3 times in a row.

• basis (str) – Basis in anndata.AnnData.obsm to use as an embedding.

• cmap (Union[str, LinearSegmentedColormap]) – Colormap for the random walk lines.

• linewidth (float) – Width of the random walk lines.

• linealpha (float) – Alpha value of the random walk lines.

• ixs_legend_loc (Optional[str]) – Legend location for the start/top indices.

• show_progress_bar (bool) – Whether to show a progress bar. Disabling it may slightly improve performance.

• n_jobs (Optional[int]) – Number of parallel jobs. If -1, use all available cores. If None or 1, the execution is sequential.

• backend (str) – Which backend to use for parallelization. See joblib.Parallel for valid options.

• figsize (Optional[Tuple[float, float]]) – Size of the figure.

• dpi (Optional[int]) – Dots per inch.

• save (Union[str, Path, None]) – Filename where to save the plot.

• kwargs (Any) – Keyword arguments for scvelo.pl.scatter().

Return type:

None

Returns:

Nothing, just plots the figure. Optionally saves it based on save. For each random walk, the first/last cell is marked by the start/end colors of cmap.

### plot_single_flow#

VelocityKernel.plot_single_flow(cluster, cluster_key, time_key, clusters=None, time_points=None, min_flow=0, remove_empty_clusters=True, ascending=False, legend_loc='upper right out', alpha=0.8, xticks_step_size=1, figsize=None, dpi=None, save=None, show=True)#

Visualize outgoing flow from a cluster of cells .

Parameters:
• cluster (str) – Cluster for which to visualize outgoing flow.

• cluster_key (str) – Key in anndata.AnnData.obs where clustering is stored.

• time_key (str) – Key in anndata.AnnData.obs where experimental time is stored.

• clusters (Optional[Sequence[Any]]) – Visualize flow only for these clusters. If None, use all clusters.

• time_points (Optional[Sequence[Union[float, int]]]) – Visualize flow only for these time points. If None, use all time points.

• min_flow (float) – Only show flow edges with flow greater than this value. Flow values are always in [0, 1].

• remove_empty_clusters (bool) – Whether to remove clusters with no incoming flow edges.

• ascending (Optional[bool]) – Whether to sort the cluster by ascending or descending incoming flow. If None, use the order as in defined by clusters.

• alpha (Optional[float]) – Alpha value for cell proportions.

• xticks_step_size (Optional[int]) – Show only every other n-th tick on the x-axis. If None, don’t show any ticks.

• legend_loc (Optional[str]) – Position of the legend. If None, do not show the legend.

• figsize (Optional[Tuple[float, float]]) – Size of the figure.

• dpi (Optional[int]) – Dots per inch.

• save (Union[str, Path, None]) – Filename where to save the plot.

• show (bool) – If False, return matplotlib.pyplot.Axes.

Return type:
Returns:

The axes object, if show = False. Nothing, just plots the figure. Optionally saves it based on save.

Notes

This function is a Python re-implementation of the following original R function with some minor stylistic differences. This function will not recreate the results from , because there, the Metacell model was used to compute the flow, whereas here the transition matrix is used.

De-serialize self from a file.

Parameters:
• fname (Union[str, Path]) – Filename from which to read the object.

• adata (Optional[AnnData]) – anndata.AnnData object to assign to the saved object. Only used when the saved object has adata and it was saved without it.

• copy (bool) – Whether to copy adata before assigning it or not. If adata is a view, it is always copied.

Return type:

IOMixin

Returns:

The de-serialized object.

### write#

Serialize self to a file.

Parameters:
• fname (Union[str, Path]) – Filename where to save the object.

• write_adata (bool) – Whether to save adata object or not, if present.

• ext (Optional[str]) – Filename extension to use. If None, don’t append any extension.

Return type:

None

Returns:

Nothing, just writes itself to a file using pickle.

Write the transition matrix and parameters used for computation to the underlying adata object.

Parameters:

key (Optional[str]) – Key used when writing transition matrix to adata. If None, the key will be determined automatically.

Return type:

None

Returns: