cellrank.kernels.ConnectivityKernel#
 class cellrank.kernels.ConnectivityKernel(adata, conn_key='connectivities', check_connectivity=False)[source]#
Kernel which computes transition probabilities based on similarities among cells.
As a measure of similarity, we currently support:
transcriptomic similarities, computed using, e.g.,
neighbors()
, see [Wolf et al., 2018].spatial similarities, computed using, e.g.,
spatial_neighbors()
, see [Palla et al., 2021].
The resulting transition matrix is symmetric and thus cannot be used to learn about the direction of the biological process. To include this direction, consider combining with a velocityderived transition matrix via
VelocityKernel
.Optionally, we apply a density correction as described in [Coifman et al., 2005], where we use the implementation of [Haghverdi et al., 2016].
Attributes table#
Annotated data object. 

None. 

Underlying connectivity matrix. 

Underlying base kernels. 

Parameters which are used to compute the transition matrix. 



Rownormalized transition matrix. 
Methods table#

Compute transition matrix based on transcriptomic similarity. 

Return a copy of self. 

Read the kernel saved using 

Plot 

Plot random walks in an embedding. 

Visualize outgoing flow from a cluster of cells [Mittnenzweig et al., 2021]. 

Deserialize self from a file. 

Serialize self to a file using 

Write the transition matrix and parameters used for computation to the underlying 
Attributes#
adata#
 ConnectivityKernel.adata#
Annotated data object.
backward#
 ConnectivityKernel.backward#
None.
connectivities#
 ConnectivityKernel.connectivities#
Underlying connectivity matrix.
kernels#
 ConnectivityKernel.kernels#
Underlying base kernels.
params#
 ConnectivityKernel.params#
Parameters which are used to compute the transition matrix.
shape#
 ConnectivityKernel.shape#
(n_cells, n_cells)
.
transition_matrix#
 ConnectivityKernel.transition_matrix#
Rownormalized transition matrix.
Methods#
compute_transition_matrix#
 ConnectivityKernel.compute_transition_matrix(density_normalize=True)[source]#
Compute transition matrix based on transcriptomic similarity.
Uses symmetric, weighted kNN graph to compute symmetric transition matrix. The connectivities are computed using
neighbors()
. Depending on the parameters used there, they can be UMAP connectivities or Gaussiankernelbased connectivities with adaptive kernel width. Parameters:
density_normalize (
bool
) – Whether to use the underlying kNN graph for density normalization. Return type:
 Returns:
: Returns self and updates
transition_matrix
andparams
.
copy#
 ConnectivityKernel.copy(*, deep=False)#
Return a copy of self.
 Parameters:
deep (
bool
) – Whether to usedeepcopy()
. Return type:
 Returns:
: Copy of self.
from_adata#
 classmethod ConnectivityKernel.from_adata(adata, key, copy=False)#
Read the kernel saved using
write_to_adata()
. Parameters:
adata (
AnnData
) – Annotated data object.key (
str
) – Key inobsp
where the transition matrix is stored. The parameters should be stored inadata.uns['{key}_params']
.copy (
bool
) – Whether to copy the transition matrix.
 Return type:
 Returns:
: The kernel with explicitly initialized properties:
transition_matrix
 the transition matrix.params
 parameters used for computation.
plot_projection#
 ConnectivityKernel.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:
key_added (
Optional
[str
]) – If notNone
, save the result toadata.obsm['{key_added}']
. Otherwise, save the result to'T_fwd_{basis}'
or'T_bwd_{basis}'
, depending on the direction.recompute (
bool
) – Whether to recompute the projection if it already exists.stream (
bool
) – IfTrue
, usevelocity_embedding_stream()
. Otherwise, usevelocity_embedding_grid()
.connectivities (
Optional
[spmatrix
]) – Connectivity matrix to use for projection. IfNone
, use ones from the underlying kernel, is possible.kwargs (
Any
) – Keyword argument for the abovementioned plotting function.
 Return type:
 Returns:
: Nothing, just plots and modifies
obsm
with a key based on thekey_added
.
plot_random_walks#
 ConnectivityKernel.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 afloat
, it can be specified as a fraction of the number of cells.successive_hits (
int
) – Number of successive hits in thestop_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:dict
 dictionary with 1 key inobs
with values corresponding to either 1 or more clusters (if the column is categorical) or atuple
specifying \([min, max]\) interval from which to select the indices.
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 aftermax_iters
. Can be specified as:dict
 dictionary with 1 key inobs
with values corresponding to either 1 or more clusters (if the column is categorical) or atuple
specifying \([min, max]\) interval from which to select the indices.
For example
{'clusters': ['Alpha', 'Beta']}
andsuccessive_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.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. IfNone
or 1, the execution is sequential.backend (
str
) – Which backend to use for parallelization. SeeParallel
for valid options.figsize (
Optional
[Tuple
[float
,float
]]) – Size of the figure.save (
Union
[str
,Path
,None
]) – Filename where to save the plot.
 Return type:
 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 ofcmap
.
plot_single_flow#
 ConnectivityKernel.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 [Mittnenzweig et al., 2021].
 Parameters:
cluster (
str
) – Cluster for which to visualize outgoing flow.time_key (
str
) – Key inobs
where experimental time is stored.clusters (
Optional
[Sequence
[Any
]]) – Visualize flow only for these clusters. IfNone
, use all clusters.time_points (
Optional
[Sequence
[Union
[float
,int
]]]) – Visualize flow only for these time points. IfNone
, 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 byclusters
.xticks_step_size (
Optional
[int
]) – Show only every other nth tick on the xaxis. IfNone
, don’t show any ticks.legend_loc (
Optional
[str
]) – Position of the legend. IfNone
, do not show the legend.figsize (
Optional
[Tuple
[float
,float
]]) – Size of the figure.figsize – Size of the figure.
dpi – Dots per inch.
save (
Union
[str
,Path
,None
]) – Filename where to save the plot.
 Return type:
 Returns:
: The axes object, if
show = False
. Nothing, just plots the figure. Optionally saves it based onsave
.
Notes
This function is a Python reimplementation of the following original R function with some minor stylistic differences. This function will not recreate the results from [Mittnenzweig et al., 2021], because there, the Metacell model [Baran et al., 2019] was used to compute the flow, whereas here the transition matrix is used.
read#
 static ConnectivityKernel.read(fname, adata=None, copy=False)#
Deserialize self from a file.
 Parameters:
fname (
Union
[str
,Path
]) – Path from which to read the object.adata (
Optional
[AnnData
]) –AnnData
object to assign to the saved object. Only used when the saved object hasadata
and it was saved without it.copy (
bool
) – Whether to copyadata
before assigning it. Ifadata
is a view, it is always copied.
 Return type:
IOMixin
 Returns:
: The deserialized object.
write#
write_to_adata#
 ConnectivityKernel.write_to_adata(key=None, copy=False)#
Write the transition matrix and parameters used for computation to the underlying
adata
object. Parameters:
 Return type:
 Returns:
: Updates the
adata
with the following fields:obsp['{key}']
 the transition matrix.uns['{key}_params']
 parameters used for the calculation.