Classes¶
Estimators¶
GPCCA¶
- class cellrank.tl.estimators.GPCCA(obj, inplace=True, read_from_adata=False, obsp_key=None, g2m_key='G2M_score', s_key='S_score', write_to_adata=True, key=None)[source]
Generalized Perron Cluster Cluster Analysis [Reuter et al., 2018] as implemented in pyGPCCA.
Coarse-grains a discrete Markov chain into a set of macrostates and computes coarse-grained transition probabilities among the macrostates. Each macrostate corresponds to an area of the state space, i.e. to a subset of cells. The assignment is soft, i.e. each cell is assigned to every macrostate with a certain weight, where weights sum to one per cell. Macrostates are computed by maximizing the ‘crispness’ which can be thought of as a measure for minimal overlap between macrostates in a certain inner-product sense. Once the macrostates have been computed, we project the large transition matrix onto a coarse-grained transition matrix among the macrostates via a Galerkin projection. This projection is based on invariant subspaces of the original transition matrix which are obtained using the real Schur decomposition [Reuter et al., 2018].
- Parameters
obj (
Union
[KernelExpression
, ~AnnData,spmatrix
,ndarray
]) – Either acellrank.tl.kernels.Kernel
object, ananndata.AnnData
object which stores the transition matrix in.obsp
attribute ornumpy
orscipy
array.inplace (
bool
) – Whether to modifyadata
object inplace or make a copy.read_from_adata (
bool
) – Whether to read available attributes inadata
, if present.obsp_key (
Optional
[str
]) – Key inobj.obsp
whenobj
is ananndata.AnnData
object.g2m_key (
Optional
[str
]) – Key inadata
.obs
. Can be used to detect cell-cycle driven start- or endpoints.s_key (
Optional
[str
]) – Key inadata
.obs
. Can be used to detect cell-cycle driven start- or endpoints.write_to_adata (
bool
) – Whether to write the transition matrix toadata
.obsp
and the parameters toadata
.uns
.key (
Optional
[str
]) – Key used when writing transition matrix toadata
. If None, thekey
is set to ‘T_bwd’ ifbackward
is True, else ‘T_fwd’. Only used whenwrite_to_adata=True
.
- compute_macrostates(n_states=None, n_cells=30, use_min_chi=False, cluster_key=None, en_cutoff=0.7, p_thresh=1e-15)[source]
Compute the macrostates.
- Parameters
n_states (
Union
[int
,Tuple
[int
,int
],List
[int
],Dict
[str
,int
],None
]) – Number of macrostates. If None, use the eigengap heuristic.n_cells (
Optional
[int
]) – Number of most likely cells from each macrostate to select.use_min_chi (
bool
) – Whether to usepygpcca.GPCCA.minChi()
to calculate the number of macrostates. If True,n_states
corresponds to a closed interval [min, max] inside of which the potentially optimal number of macrostates is searched.cluster_key (
Optional
[str
]) – If a key to cluster labels is given, names and colors of the states will be associated with the clusters.en_cutoff (
Optional
[float
]) – Ifcluster_key
is given, this parameter determines when an approximate recurrent class will be labeled as ‘Unknown’, based on the entropy of the distribution of cells over transcriptomic clusters.p_thresh (
float
) – If cell cycle scores were provided, a Wilcoxon rank-sum test is conducted to identify cell-cycle states. If the test returns a positive statistic and a p-value smaller thanp_thresh
, a warning will be issued.
- Returns
Nothing, but updates the following fields:
- Return type
- set_terminal_states_from_macrostates(names=None, n_cells=30)[source]
Manually select terminal states from macrostates.
- Parameters
names (
Union
[Sequence
[str
],Mapping
[str
,str
],str
,None
]) – Names of the macrostates to be marked as terminal. Multiple states can be combined using ‘,’, such as["Alpha, Beta", "Epsilon"]
. If adict
, keys correspond to the names of the macrostates and the values to the new names. If None, select all macrostates.n_cells (
int
) – Number of most likely cells from each macrostate to select.
- Returns
Nothing, just updates the following fields:
- Return type
- compute_terminal_states(method='stability', n_cells=30, alpha=1, stability_threshold=0.96, n_states=None)[source]
Automatically select terminal states from macrostates.
- Parameters
method (
str
) –One of following:
’eigengap’ - select the number of states based on the eigengap of the transition matrix.
’eigengap_coarse’ - select the number of states based on the eigengap of the diagonal of the coarse-grained transition matrix.
’top_n’ - select top
n_states
based on the probability of the diagonal of the coarse-grained transition matrix.’stability’ - select states which have a stability index >=
stability_threshold
. The stability index is given by the diagonal elements of the coarse-grained transition matrix.
n_cells (
int
) – Number of most likely cells from each macrostate to select.alpha (
Optional
[float
]) – Weight given to the deviation of an eigenvalue from one. Used whenmethod='eigengap'
ormethod='eigengap_coarse'
.stability_threshold (
float
) – Threshold used whenmethod='stability'
.n_states (
Optional
[int
]) – Numer of states used whenmethod='top_n'
.
- Returns
Nothing, just updates the following fields:
- Return type
- compute_gdpt(n_components=10, key_added='gdpt_pseudotime', **kwargs)[source]
Compute generalized Diffusion pseudotime from [Haghverdi et al., 2016] using the real Schur decomposition.
- Parameters
- Returns
Nothing, just updates
adata
.obs[key_added]
with the computed pseudotime.- Return type
- plot_coarse_T(show_stationary_dist=True, show_initial_dist=False, cmap='viridis', xtick_rotation=45, annotate=True, show_cbar=True, title=None, figsize=(8, 8), dpi=80, save=None, text_kwargs=mappingproxy({}), **kwargs)[source]
Plot the coarse-grained transition matrix between macrostates.
- Parameters
show_stationary_dist (
bool
) – Whether to show the stationary distribution, if present.show_initial_dist (
bool
) – Whether to show the initial distribution.cmap (
Union
[str
,ListedColormap
]) – Colormap to use.xtick_rotation (
float
) – Rotation of ticks on the x-axis.annotate (
bool
) – Whether to display the text on each cell.show_cbar (
bool
) – Whether to show colorbar.dpi (
int
) – Dots per inch.save (
Union
[str
,Path
,None
]) – Filename where to save the plot.text_kwargs (
Mapping
[str
,Any
]) – Keyword arguments formatplotlib.pyplot.text()
.kwargs – Keyword arguments for
matplotlib.pyplot.imshow()
.
- Returns
Nothing, just plots the figure. Optionally saves it based on
save
.- Return type
- plot_macrostate_composition(key, width=0.8, title=None, labelrot=45, legend_loc='upper right out', figsize=None, dpi=None, save=None, show=True)[source]
Plot stacked histogram of macrostates over categorical annotations.
- Parameters
adata (
anndata.AnnData
) – Annotated data object.key (
str
) – Key fromadata
.obs
containing categorical annotations.width (
float
) – Bar width in [0, 1].title (
Optional
[str
]) – Title of the figure. If None, create one automatically.labelrot (
float
) – Rotation of labels on x-axis.legend_loc (
Optional
[str
]) – Position of the legend. If None, don’t show legend.figsize (
Optional
[Tuple
[float
,float
]]) – Size of the figure.save (
Union
[str
,Path
,None
]) – Filename where to save the plot.show (
bool
) – If False, returnmatplotlib.pyplot.Axes
.
- Return type
Optional
[Axes
]- Returns
matplotlib.pyplot.Axes
– The axis object ifshow=False
.None – Nothing, just plots the figure. Optionally saves it based on
save
.
- fit(n_lineages=None, cluster_key=None, keys=None, method='krylov', compute_absorption_probabilities=True, **kwargs)[source]
Run the pipeline, computing the macrostates, initial or terminal states and optionally the absorption probabilities.
It is equivalent to running:
if n_lineages is None or n_lineages == 1: compute_eigendecomposition(...) # get the stationary distribution if n_lineages > 1: compute_schur(...) compute_macrostates(...) if n_lineages is None: compute_terminal_states(...) else: set_terminal_states_from_macrostates(...) if compute_absorption_probabilities: compute_absorption_probabilities(...)
- Parameters
n_lineages (
Optional
[int
]) – Number of lineages. If None, it will be determined automatically.cluster_key (
Optional
[str
]) – Match computed states against pre-computed clusters to annotate the states. For this, provide a key fromadata
.obs
where cluster labels have been computed.keys (
Optional
[Sequence
[str
]]) – Determines which initial or terminal states to use by passing their names. Further, initial or terminal states can be combined. If e.g. the terminal states are [‘Neuronal_1’, ‘Neuronal_1’, ‘Astrocytes’, ‘OPC’], then passingkeys=['Neuronal_1, Neuronal_2', 'OPC']
means that the two neuronal terminal states are treated as one and the ‘Astrocyte’ state is excluded.method (
str
) – Method to use when computing the Schur decomposition. Valid options are: ‘krylov’ or ‘brandts’.compute_absorption_probabilities (
bool
) – Whether to compute the absorption probabilities or only the initial or terminal states.kwargs – Keyword arguments for
cellrank.tl.estimators.GPCCA.compute_macrostates()
.
- Returns
Nothing, just makes available the following fields:
- Return type
- property absorption_probabilities: cellrank.tl._lineage.Lineage
Absorption probabilities.
- Return type
- property adata: anndata._core.anndata.AnnData
Annotated data object.
- Returns
Annotated data object.
- Return type
- property coarse_T: pandas.core.frame.DataFrame
Coarse-grained transition matrix.
- Return type
DataFrame
- property coarse_initial_distribution: pandas.core.series.Series
Coarse initial distribution.
- Return type
Series
- property coarse_stationary_distribution: pandas.core.series.Series
Coarse stationary distribution.
- Return type
Series
- compute_absorption_probabilities(keys=None, check_irreducibility=False, solver='gmres', use_petsc=True, time_to_absorption=None, n_jobs=None, backend='loky', show_progress_bar=True, tol=1e-06, preconditioner=None)
Compute absorption probabilities of a Markov chain.
For each cell, this computes the probability of it reaching any of the approximate recurrent classes defined by
terminal_states
.- Parameters
keys (
Optional
[Sequence
[str
]]) – Keys defining the recurrent classes.check_irreducibility (
bool
) – Check whether the transition matrix is irreducible.solver (
str
) –Solver to use for the linear problem. Options are ‘direct’, ‘gmres’, ‘lgmres’, ‘bicgstab’ or ‘gcrotmk’ when
use_petsc=False
or one ofpetsc4py.PETSc.KPS.Type
otherwise.Information on the
scipy
iterative solvers can be found inscipy.sparse.linalg()
or forpetsc4py
solver here.use_petsc (
bool
) – Whether to use solvers frompetsc4py
orscipy
. Recommended for large problems. If no installation is found, defaults toscipy.sparse.linalg.gmres()
.time_to_absorption (
Union
[str
,Sequence
[Union
[str
,Sequence
[str
]]],Dict
[Union
[str
,Sequence
[str
]],str
],None
]) –Whether to compute mean time to absorption and its variance to specific absorbing states.
If a
dict
, can be specified as{'Alpha': 'var', ...}
to also compute variance. In case when states are atuple
, time to absorption will be computed to the subset of these states, such as[('Alpha', 'Beta'), ...]
or{('Alpha', 'Beta'): 'mean', ...}
. Can be specified as'all'
to compute it to any absorbing state inkeys
, which is more efficient than listing all absorbing states.It might be beneficial to disable the progress bar as
show_progress_bar=False
, because many linear systems are being solved.n_jobs (
Optional
[int
]) – Number of parallel jobs to use when using an iterative solver. Whenuse_petsc=True
or for quickly-solvable problems, we recommend higher number (>=8) of jobs in order to fully saturate the cores.backend (
str
) – Which backend to use for multiprocessing. Seejoblib.Parallel
for valid options.show_progress_bar (
bool
) – Whether to show progress bar when the solver isn’t a direct one.tol (
float
) – Convergence tolerance for the iterative solver. The default is fine for most cases, only consider decreasing this for severely ill-conditioned matrices.preconditioner (
Optional
[str
]) – Preconditioner to use, only available whenuse_petsc=True
. For available values, see here or the values of petsc4py.PETSc.PC.Type. We recommended ‘ilu’ preconditioner for badly conditioned problems.
- Returns
Nothing, but updates the following fields:
absorption_probabilities
- probabilities of being absorbed into the terminal states.lineage_absorption_times
- mean times until absorption to subset absorbing states and optionally their variances saved as'{lineage} mean'
and'{lineage} var'
, respectively, for each subset of absorbing states specified intime_to_absorption
.
- Return type
- compute_eigendecomposition(k=20, which='LR', alpha=1, only_evals=False, ncv=None)
Compute eigendecomposition of transition matrix.
Uses a sparse implementation, if possible, and only computes the top \(k\) eigenvectors to speed up the computation. Computes both left and right eigenvectors.
- Parameters
k (
int
) – Number of eigenvalues/vectors to compute.which (
str
) – Eigenvalues are in general complex. ‘LR’ - largest real part, ‘LM’ - largest magnitude.alpha (
float
) – Used to compute the eigengap.alpha
is the weight given to the deviation of an eigenvalue from one.only_evals (
bool
) – Compute only eigenvalues.
- Returns
Nothing, but updates the following field:
- Return type
- compute_lineage_drivers(lineages=None, method='fischer', cluster_key=None, clusters=None, layer='X', use_raw=False, confidence_level=0.95, n_perms=1000, seed=None, return_drivers=True, **kwargs)
Compute driver genes per lineage.
Correlates gene expression with lineage probabilities, for a given lineage and set of clusters. Often, it makes sense to restrict this to a set of clusters which are relevant for the specified lineages.
- Parameters
lineages (
Union
[str
,Sequence
,None
]) – Either a set of lineage names fromabsorption_probabilities
.names or None, in which case all lineages are considered.method (
str
) –Mode to use when calculating p-values and confidence intervals. Valid options are:
’fischer’ - use Fischer transformation [Fisher, 1921].
’perm_test’ - use permutation test.
cluster_key (
Optional
[str
]) – Key fromadata
.obs
to obtain cluster annotations. These are considered forclusters
.clusters (
Union
[str
,Sequence
,None
]) – Restrict the correlations to these clusters.use_raw (
bool
) – Whether or not to useadata
.raw
to correlate gene expression. If using a layer other than.X
, this must be set to False.confidence_level (
float
) – Confidence level for the confidence interval calculation. Must be in [0, 1].n_perms (
int
) – Number of permutations to use whenmethod='perm_test'
.return_drivers (
bool
) – Whether to return the drivers. This also contains the lower and upperconfidence_level
confidence interval bounds.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.
- Return type
Optional
[DataFrame
]- Returns
Dataframe of shape
(n_genes, n_lineages * 5)
containing the following columns, 1 for each lineage –{lineage} corr
- correlation between the gene expression and absorption probabilities.{lineage} pval
- calculated p-values for double-sided test.{lineage} qval
- corrected p-values using Benjamini-Hochberg method at level 0.05.{lineage} ci low
- lower bound of theconfidence_level
correlation confidence interval.{lineage} ci high
- upper bound of theconfidence_level
correlation confidence interval.
Only if
return_drivers=True
.Otherwise, updates
adata
.var
oradata
.raw.var
, dependinguse_raw
with –'{direction} {lineage} corr'
- the potential lineage drivers.'{direction} {lineage} qval'
- the corrected p-values.
Also updates the following fields –
lineage_drivers
- same as the returned values.
- compute_lineage_priming(method='kl_divergence', early_cells=None)
Compute the degree of lineage priming.
This method computes how naive vs. committed each individual cell is. It returns a score where 0 stands for naive and 1 stands for committed.
- Parameters
method (
Literal
[‘kl_divergence’, ‘entropy’]) –The method used to compute the degree of lineage priming. Valid options are:
’kl_divergence’: as in [Velten et al., 2017], computes KL-divergence between the fate probabilities of a cell and the average fate probabilities. Computation of average fate probabilities can be restricted to a set of user-defined
early_cells
.’entropy’: as in [Setty et al., 2019], computes entropy over a cell’s fate probabilities.
early_cells (
Union
[Mapping
[str
,Sequence
[str
]],Sequence
[str
],None
]) – Cell ids or a mask marking early cells. If None, use all cells. Only used whenmethod='kl_divergence'
. Cell ids or a mask marking early cells. If None, use all cells. Only used whenmethod='kl_divergence'
. If adict
, the key species a cluster key inanndata.AnnData.obs
and the values specify cluster labels containing early cells.
- Returns
- Return type
The priming degree.
- compute_partition()
Compute communication classes for the Markov chain.
- Returns
Nothing, but updates the following fields:
- Return type
- compute_schur(n_components=10, initial_distribution=None, method='krylov', which='LR', alpha=1)
Compute the Schur decomposition.
- Parameters
n_components (
int
) – Number of vectors to compute.initial_distribution (
Optional
[ndarray
]) – Input probability distribution over all cells. If None, uniform is chosen.method (
str
) –Method for calculating the Schur vectors. Valid options are: ‘krylov’ or ‘brandts’. For benefits of each method, see
pygpcca.GPCCA
.The former is an iterative procedure that computes a partial, sorted Schur decomposition for large, sparse matrices whereas the latter computes a full sorted Schur decomposition of a dense matrix.
which (
str
) – Eigenvalues are in general complex. ‘LR’ - largest real part, ‘LM’ - largest magnitude.alpha (
float
) – Used to compute the eigengap.alpha
is the weight given to the deviation of an eigenvalue from one.
- Returns
Nothing, but updates the following fields:
- Return type
- copy()
Return a copy of self, including the underlying
adata
object.- Return type
- property is_irreducible
Whether the Markov chain is irreducible or not.
- property issparse: bool
Whether the transition matrix is sparse or not.
- Return type
- property kernel: cellrank.tl.kernels._base_kernel.KernelExpression
Underlying kernel.
- Return type
KernelExpression
- property lineage_absorption_times: pandas.core.frame.DataFrame
Lineage absorption times.
- Return type
DataFrame
- property lineage_drivers: pandas.core.frame.DataFrame
Lineage drivers.
- Return type
DataFrame
- property macrostates: pandas.core.series.Series
Macrostates.
- Return type
Series
- property macrostates_memberships: cellrank.tl._lineage.Lineage
Macrostates memberships.
- Return type
- plot_absorption_probabilities(data, prop, discrete=False, lineages=None, cluster_key=None, mode='embedding', time_key='latent_time', title=None, same_plot=False, cmap='viridis', **kwargs)
Plot discrete states or probabilities in an embedding.
- Parameters
discrete (
bool
) – Whether to plot in discrete or continuous mode.lineages (
Union
[str
,Sequence
[str
],None
]) – Plot only these lineages. If None, plot all lineages.cluster_key (
Optional
[str
]) – Key fromadata
.obs
for plotting categorical observations.mode (
str
) –Can be either ‘embedding’ or ‘time’:
’embedding’ - plot the embedding while coloring in the absorption probabilities.
’time’ - plot the pseudotime on x-axis and the absorption probabilities on y-axis.
time_key (
str
) – Key fromadata
.obs
to use as a pseudotime ordering of the cells.title (
Optional
[str
]) – Either None, in which case titles are'{to,from} {terminal,initial} {state}'
, or an array of titles, one per lineage.same_plot (
bool
) – Whether to plot the lineages on the same plot using color gradients whenmode='embedding'
.cmap (
Union
[str
,ListedColormap
]) – Colormap to use.basis – Basis to use when
mode='embedding'
. If None, use ‘umap’.kwargs – Keyword arguments for
scvelo.pl.scatter()
.
- Returns
Nothing, just plots the figure. Optionally saves it based on
save
.- Return type
- plot_eigendecomposition(left=False, *args, **kwargs)
Plot eigenvectors in an embedding.
- Parameters
left (
bool
) – Whether to plot left or right eigenvectors.use – Which or how many vectors are to be plotted.
abs_value – Whether to take the absolute value before plotting.
cluster_key – Key in
adata
.obs
for plotting categorical observations.basis – Basis to use when
mode='embedding'
. If None, use ‘umap’.kwargs – Keyword arguments for
scvelo.pl.scatter()
.
- Returns
Nothing, just plots the figure. Optionally saves it based on
save
.- Return type
- plot_lineage_drivers(lineage, n_genes=8, ncols=None, use_raw=False, title_fmt='{gene} qval={qval:.4e}', figsize=None, dpi=None, save=None, **kwargs)
Plot lineage drivers discovered by
compute_lineage_drivers()
.- Parameters
lineage (
str
) – Lineage for which to plot the driver genes.n_genes (
int
) – Top most correlated genes to plot.use_raw (
bool
) – Whether to look inadata
.raw.var
oradata
.var
.title_fmt (
str
) – Title format. Possible keywords include {gene}, {qval}, {corr} for gene name, q-value and correlation, respectively.figsize (
Optional
[Tuple
[float
,float
]]) – Size of the figure.save (
Union
[str
,Path
,None
]) – Filename where to save the plot.kwargs – Keyword arguments for
scvelo.pl.scatter()
.
- Returns
Nothing, just plots the figure. Optionally saves it based on
save
.- Return type
- plot_lineage_drivers_correlation(lineage_x, lineage_y, color=None, gene_sets=None, gene_sets_colors=None, use_raw=False, cmap='RdYlBu_r', fontsize=12, adjust_text=False, legend_loc='best', figsize=(4, 4), dpi=None, save=None, show=True, **kwargs)
Show scatter plot of gene-correlations between two lineages.
Optionally, you can pass a
dict
of gene names that will be annotated in the plot.- Parameters
lineage_x (
str
) – Name of the lineage on the x-axis.lineage_y (
str
) – Name of the lineage on the y-axis.gene_sets (
Optional
[Dict
[str
,Iterable
]]) – Gene sets annotations of the form {‘gene_set_name’: [‘gene_1’, ‘gene_2’], …}.gene_sets_colors (
Optional
[Iterable
]) – List of colors where each entry corresponds to a gene set fromgenes_sets
. If None and keys ingene_sets
correspond to lineage names, use the lineage colors. Otherwise, use default colors.use_raw (
bool
) – Whether to accessadata
.raw.var
oradata
.var
.cmap (
str
) – Colormap to use.fontsize (
int
) – Size of the text when plottinggene_sets
.adjust_text (
bool
) – Whether to automatically adjust text in order to reduce overlap.legend_loc (
Optional
[str
]) – Position of the legend. If None, don’t show the legend. Only used whengene_sets!=None
.figsize (
Optional
[Tuple
[float
,float
]]) – Size of the figure.save (
Union
[str
,Path
,None
]) – Filename where to save the plot.show (
bool
) – If False, returnmatplotlib.pyplot.Axes
.kwargs (
Any
) – Keyword arguments forscanpy.pl.scatter()
.
- Return type
Optional
[Axes
]- Returns
matplotlib.pyplot.Axes
– The axis object ifshow=False
.None – Nothing, just plots the figure. Optionally saves it based on
save
.
Notes
This plot is based on the following notebook by Maren Büttner.
- plot_macrostates(data, prop, discrete=False, lineages=None, cluster_key=None, mode='embedding', time_key='latent_time', title=None, same_plot=False, cmap='viridis', **kwargs)
Plot discrete states or probabilities in an embedding.
- Parameters
discrete (
bool
) – Whether to plot in discrete or continuous mode.lineages (
Union
[str
,Sequence
[str
],None
]) – Plot only these lineages. If None, plot all lineages.cluster_key (
Optional
[str
]) – Key fromadata
.obs
for plotting categorical observations.mode (
str
) –Can be either ‘embedding’ or ‘time’:
’embedding’ - plot the embedding while coloring in the absorption probabilities.
’time’ - plot the pseudotime on x-axis and the absorption probabilities on y-axis.
time_key (
str
) – Key fromadata
.obs
to use as a pseudotime ordering of the cells.title (
Optional
[str
]) – Either None, in which case titles are'{to,from} {terminal,initial} {state}'
, or an array of titles, one per lineage.same_plot (
bool
) – Whether to plot the lineages on the same plot using color gradients whenmode='embedding'
.cmap (
Union
[str
,ListedColormap
]) – Colormap to use.basis – Basis to use when
mode='embedding'
. If None, use ‘umap’.kwargs – Keyword arguments for
scvelo.pl.scatter()
.
- Returns
Nothing, just plots the figure. Optionally saves it based on
save
.- Return type
- plot_schur(vectors, prop, use=None, abs_value=False, cluster_key=None, **kwargs)
Plot vectors in an embedding.
- Parameters
use (
Union
[int
,Tuple
[int
],List
[int
],None
]) – Which or how many vectors are to be plotted.abs_value (
bool
) – Whether to take the absolute value before plotting.cluster_key (
Optional
[str
]) – Key inadata
.obs
for plotting categorical observations.basis – Basis to use when
mode='embedding'
. If None, use ‘umap’.kwargs – Keyword arguments for
scvelo.pl.scatter()
.
- Returns
Nothing, just plots the figure. Optionally saves it based on
save
.- Return type
- plot_schur_matrix(title='schur matrix', cmap='viridis', figsize=None, dpi=80, save=None, **kwargs)
Plot the Schur matrix.
- Parameters
- Returns
Nothing, just plots the figure. Optionally saves it based on
save
.- Return type
- plot_spectrum(n=None, real_only=False, show_eigengap=True, show_all_xticks=True, legend_loc=None, title=None, figsize=(5, 5), dpi=100, save=None, marker='.', **kwargs)
Plot the top eigenvalues in real or complex plane.
- Parameters
n (
Optional
[int
]) – Number of eigenvalues to show. If None, show all that have been computed.real_only (
bool
) – Whether to plot only the real part of the spectrum.show_eigengap (
bool
) – When real_only=True, this determines whether to show the inferred eigengap as a dotted line.show_all_xticks (
bool
) – When real_only=True, this determines whether to show the indices of all eigenvalues on the x-axis.legend_loc (
Optional
[str
]) – Location parameter for the legend.figsize (
Optional
[Tuple
[float
,float
]]) – Size of the figure.dpi (
int
) – Dots per inch.save (
Union
[str
,Path
,None
]) – Filename where to save the plot.marker (
str
) – Marker symbol used, valid options can be found inmatplotlib.markers
.kwargs – Keyword arguments for
matplotlib.pyplot.scatter()
.
- Returns
Nothing, just plots the figure. Optionally saves it based on
save
.- Return type
- plot_terminal_states(data, prop, discrete=False, lineages=None, cluster_key=None, mode='embedding', time_key='latent_time', title=None, same_plot=False, cmap='viridis', **kwargs)
Plot discrete states or probabilities in an embedding.
- Parameters
discrete (
bool
) – Whether to plot in discrete or continuous mode.lineages (
Union
[str
,Sequence
[str
],None
]) – Plot only these lineages. If None, plot all lineages.cluster_key (
Optional
[str
]) – Key fromadata
.obs
for plotting categorical observations.mode (
str
) –Can be either ‘embedding’ or ‘time’:
’embedding’ - plot the embedding while coloring in the absorption probabilities.
’time’ - plot the pseudotime on x-axis and the absorption probabilities on y-axis.
time_key (
str
) – Key fromadata
.obs
to use as a pseudotime ordering of the cells.title (
Optional
[str
]) – Either None, in which case titles are'{to,from} {terminal,initial} {state}'
, or an array of titles, one per lineage.same_plot (
bool
) – Whether to plot the lineages on the same plot using color gradients whenmode='embedding'
.cmap (
Union
[str
,ListedColormap
]) – Colormap to use.basis – Basis to use when
mode='embedding'
. If None, use ‘umap’.kwargs – Keyword arguments for
scvelo.pl.scatter()
.
- Returns
Nothing, just plots the figure. Optionally saves it based on
save
.- Return type
- property priming_degree: pandas.core.series.Series
Priming degree.
- Return type
Series
- static read(fname)
Deserialize self from a file.
- property recurrent_classes
Recurrent classes of the Markov chain.
- rename_terminal_states(new_names, update_adata=True)
Rename the names of
terminal_states
.
- property schur: numpy.ndarray
Schur vectors.
- Return type
- property schur_matrix: numpy.ndarray
Schur matrix.
- Return type
- set_terminal_states(labels, cluster_key=None, en_cutoff=None, p_thresh=None, add_to_existing=False, **kwargs)
Manually define terminal states.
- Parameters
labels (
Union
[Series
,Dict
[str
,Sequence
[Any
]]]) –Defines the terminal states. Valid options are:
categorical
pandas.Series
where each category corresponds to one terminal state. NaN entries denote cells that do not belong to any terminal state, i.e. these are either initial or transient cells.dict
where keys are terminal states and values are lists of cell barcodes corresponding to annotations inadata
.obs_names
. If only 1 key is provided, values should correspond to terminal state clusters if a categoricalpandas.Series
can be found inadata
.obs
.
cluster_key (
Optional
[str
]) – Key fromadata.obs
where categorical cluster labels are stored. These are used to associate names and colors with each terminal state. Each terminal state will be given the name and color corresponding to the cluster it mostly overlaps with.en_cutoff (
Optional
[float
]) – Ifcluster_key
is given, this parameter determines when an approximate recurrent class will be labeled as ‘Unknown’, based on the entropy of the distribution of cells over transcriptomic clusters.p_thresh (
Optional
[float
]) – If cell cycle scores were provided, a Wilcoxon rank-sum test is conducted to identify cell-cycle states. If the test returns a positive statistic and a p-value smaller thanp_thresh
, a warning will be issued.add_to_existing (
bool
) – Whether the new terminal states should be added to pre-existing ones. Cells already assigned to a terminal state will be re-assigned to the new terminal state if there’s a conflict between old and new annotations. This throws an error if no previous annotations corresponding to terminal states have been found.
- Returns
Nothing, but updates the following fields:
- Return type
- property terminal_states: pandas.core.series.Series
Terminal states.
- Return type
Series
- property terminal_states_probabilities: pandas.core.series.Series
Terminal states probabilities.
- Return type
Series
- property transient_classes
Transient classes of the Markov chain.
- property transition_matrix: Union[numpy.ndarray, scipy.sparse.base.spmatrix]
Transition matrix.
CFLARE¶
- class cellrank.tl.estimators.CFLARE(obj, inplace=True, read_from_adata=False, obsp_key=None, g2m_key='G2M_score', s_key='S_score', write_to_adata=True, key=None)[source]
Compute the initial/terminal states of a Markov chain via spectral heuristics.
This estimator uses the left eigenvectors of the transition matrix to filter to a set of recurrent cells and the right eigenvectors to cluster this set of cells into discrete groups.
- Parameters
obj (
Union
[KernelExpression
, ~AnnData,spmatrix
,ndarray
]) – Either acellrank.tl.kernels.Kernel
object, ananndata.AnnData
object which stores the transition matrix in.obsp
attribute ornumpy
orscipy
array.inplace (
bool
) – Whether to modifyadata
object inplace or make a copy.read_from_adata (
bool
) – Whether to read available attributes inadata
, if present.obsp_key (
Optional
[str
]) – Key inobj.obsp
whenobj
is ananndata.AnnData
object.g2m_key (
Optional
[str
]) – Key inadata
.obs
. Can be used to detect cell-cycle driven start- or endpoints.s_key (
Optional
[str
]) – Key inadata
.obs
. Can be used to detect cell-cycle driven start- or endpoints.write_to_adata (
bool
) – Whether to write the transition matrix toadata
.obsp
and the parameters toadata
.uns
.key (
Optional
[str
]) – Key used when writing transition matrix toadata
. If None, thekey
is set to ‘T_bwd’ ifbackward
is True, else ‘T_fwd’. Only used whenwrite_to_adata=True
.
- compute_terminal_states(use=None, percentile=98, method='kmeans', cluster_key=None, n_clusters_kmeans=None, n_neighbors=20, resolution=0.1, n_matches_min=0, n_neighbors_filtering=15, basis=None, n_comps=5, scale=False, en_cutoff=0.7, p_thresh=1e-15)[source]
Find approximate recurrent classes of the Markov chain.
Filter to obtain recurrent states in left eigenvectors. Cluster to obtain approximate recurrent classes in right eigenvectors.
- Parameters
use (
Union
[int
,Tuple
[int
],List
[int
],range
,None
]) – Which or how many first eigenvectors to use as features for clustering/filtering. If None, use the eigengap statistic.percentile (
Optional
[int
]) – Threshold used for filtering out cells which are most likely transient states. Cells which are in the lowerpercentile
percent of each eigenvector will be removed from the data matrix.method (
str
) – Method to be used for clustering. Must be one of ‘louvain’, ‘leiden’ or ‘kmeans’.cluster_key (
Optional
[str
]) – If a key to cluster labels is given,terminal_states
will get associated with these for naming and colors.n_clusters_kmeans (
Optional
[int
]) – If None, this is set touse + 1
.n_neighbors (
int
) – If we use ‘louvain’ or ‘leiden’ for clustering cells, we need to build a KNN graph. This is the \(K\) parameter for that, the number of neighbors for each cell.resolution (
float
) – Resolution parameter for ‘louvain’ or ‘leiden’ clustering. Should be chosen relatively small.n_matches_min (
Optional
[int
]) – Filters out cells which don’t have at least n_matches_min neighbors from the same class. This filters out some cells which are transient but have been misassigned.n_neighbors_filtering (
int
) – Parameter for filtering cells. Cells are filtered out if they don’t have at leastn_matches_min
neighbors among theirn_neighbors_filtering
nearest cells.basis (
Optional
[str
]) – Key from :paramref`adata`.obsm
to be used as additional features for the clustering.n_comps (
int
) – Number of embedding components to be use whenbasis
is not None.scale (
bool
) – Scale to z-scores. Consider using this if appending embedding to features.en_cutoff (
Optional
[float
]) – Ifcluster_key
is given, this parameter determines when an approximate recurrent class will be labeled as ‘Unknown’, based on the entropy of the distribution of cells over transcriptomic clusters.p_thresh (
float
) – If cell cycle scores were provided, a Wilcoxon rank-sum test is conducted to identify cell-cycle states. If the test returns a positive statistic and a p-value smaller thanp_thresh
, a warning will be issued.
- Returns
Nothing, but updates the following fields:
- Return type
- fit(n_lineages, keys=None, cluster_key=None, compute_absorption_probabilities=True, **kwargs)[source]
Run the pipeline, computing the initial or terminal states and optionally the absorption probabilities.
It is equivalent to running:
compute_eigendecomposition(...) compute_terminal_states(...) compute_absorption_probabilities(...)
- Parameters
n_lineages (
Optional
[int
]) – Number of lineages. If None, it will be determined automatically.cluster_key (
Optional
[str
]) – Match computed states against pre-computed clusters to annotate the states. For this, provide a key fromadata
.obs
where cluster labels have been computed.keys (
Optional
[Sequence
[str
]]) – Determines which initial or terminal states to use by passing their names. Further, initial or terminal states can be combined. If e.g. the terminal states are [‘Neuronal_1’, ‘Neuronal_1’, ‘Astrocytes’, ‘OPC’], then passingkeys=['Neuronal_1, Neuronal_2', 'OPC']
means that the two neuronal terminal states are treated as one and the ‘Astrocyte’ state is excluded.kwargs – Keyword arguments for
compute_terminal_states()
, such asn_cells
.
- Returns
Nothing, just makes available the following fields:
- Return type
- property absorption_probabilities: cellrank.tl._lineage.Lineage
Absorption probabilities.
- Return type
- property adata: anndata._core.anndata.AnnData
Annotated data object.
- Returns
Annotated data object.
- Return type
- compute_absorption_probabilities(keys=None, check_irreducibility=False, solver='gmres', use_petsc=True, time_to_absorption=None, n_jobs=None, backend='loky', show_progress_bar=True, tol=1e-06, preconditioner=None)
Compute absorption probabilities of a Markov chain.
For each cell, this computes the probability of it reaching any of the approximate recurrent classes defined by
terminal_states
.- Parameters
keys (
Optional
[Sequence
[str
]]) – Keys defining the recurrent classes.check_irreducibility (
bool
) – Check whether the transition matrix is irreducible.solver (
str
) –Solver to use for the linear problem. Options are ‘direct’, ‘gmres’, ‘lgmres’, ‘bicgstab’ or ‘gcrotmk’ when
use_petsc=False
or one ofpetsc4py.PETSc.KPS.Type
otherwise.Information on the
scipy
iterative solvers can be found inscipy.sparse.linalg()
or forpetsc4py
solver here.use_petsc (
bool
) – Whether to use solvers frompetsc4py
orscipy
. Recommended for large problems. If no installation is found, defaults toscipy.sparse.linalg.gmres()
.time_to_absorption (
Union
[str
,Sequence
[Union
[str
,Sequence
[str
]]],Dict
[Union
[str
,Sequence
[str
]],str
],None
]) –Whether to compute mean time to absorption and its variance to specific absorbing states.
If a
dict
, can be specified as{'Alpha': 'var', ...}
to also compute variance. In case when states are atuple
, time to absorption will be computed to the subset of these states, such as[('Alpha', 'Beta'), ...]
or{('Alpha', 'Beta'): 'mean', ...}
. Can be specified as'all'
to compute it to any absorbing state inkeys
, which is more efficient than listing all absorbing states.It might be beneficial to disable the progress bar as
show_progress_bar=False
, because many linear systems are being solved.n_jobs (
Optional
[int
]) – Number of parallel jobs to use when using an iterative solver. Whenuse_petsc=True
or for quickly-solvable problems, we recommend higher number (>=8) of jobs in order to fully saturate the cores.backend (
str
) – Which backend to use for multiprocessing. Seejoblib.Parallel
for valid options.show_progress_bar (
bool
) – Whether to show progress bar when the solver isn’t a direct one.tol (
float
) – Convergence tolerance for the iterative solver. The default is fine for most cases, only consider decreasing this for severely ill-conditioned matrices.preconditioner (
Optional
[str
]) – Preconditioner to use, only available whenuse_petsc=True
. For available values, see here or the values of petsc4py.PETSc.PC.Type. We recommended ‘ilu’ preconditioner for badly conditioned problems.
- Returns
Nothing, but updates the following fields:
absorption_probabilities
- probabilities of being absorbed into the terminal states.lineage_absorption_times
- mean times until absorption to subset absorbing states and optionally their variances saved as'{lineage} mean'
and'{lineage} var'
, respectively, for each subset of absorbing states specified intime_to_absorption
.
- Return type
- compute_eigendecomposition(k=20, which='LR', alpha=1, only_evals=False, ncv=None)
Compute eigendecomposition of transition matrix.
Uses a sparse implementation, if possible, and only computes the top \(k\) eigenvectors to speed up the computation. Computes both left and right eigenvectors.
- Parameters
k (
int
) – Number of eigenvalues/vectors to compute.which (
str
) – Eigenvalues are in general complex. ‘LR’ - largest real part, ‘LM’ - largest magnitude.alpha (
float
) – Used to compute the eigengap.alpha
is the weight given to the deviation of an eigenvalue from one.only_evals (
bool
) – Compute only eigenvalues.
- Returns
Nothing, but updates the following field:
- Return type
- compute_lineage_drivers(lineages=None, method='fischer', cluster_key=None, clusters=None, layer='X', use_raw=False, confidence_level=0.95, n_perms=1000, seed=None, return_drivers=True, **kwargs)
Compute driver genes per lineage.
Correlates gene expression with lineage probabilities, for a given lineage and set of clusters. Often, it makes sense to restrict this to a set of clusters which are relevant for the specified lineages.
- Parameters
lineages (
Union
[str
,Sequence
,None
]) – Either a set of lineage names fromabsorption_probabilities
.names or None, in which case all lineages are considered.method (
str
) –Mode to use when calculating p-values and confidence intervals. Valid options are:
’fischer’ - use Fischer transformation [Fisher, 1921].
’perm_test’ - use permutation test.
cluster_key (
Optional
[str
]) – Key fromadata
.obs
to obtain cluster annotations. These are considered forclusters
.clusters (
Union
[str
,Sequence
,None
]) – Restrict the correlations to these clusters.use_raw (
bool
) – Whether or not to useadata
.raw
to correlate gene expression. If using a layer other than.X
, this must be set to False.confidence_level (
float
) – Confidence level for the confidence interval calculation. Must be in [0, 1].n_perms (
int
) – Number of permutations to use whenmethod='perm_test'
.return_drivers (
bool
) – Whether to return the drivers. This also contains the lower and upperconfidence_level
confidence interval bounds.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.
- Return type
Optional
[DataFrame
]- Returns
Dataframe of shape
(n_genes, n_lineages * 5)
containing the following columns, 1 for each lineage –{lineage} corr
- correlation between the gene expression and absorption probabilities.{lineage} pval
- calculated p-values for double-sided test.{lineage} qval
- corrected p-values using Benjamini-Hochberg method at level 0.05.{lineage} ci low
- lower bound of theconfidence_level
correlation confidence interval.{lineage} ci high
- upper bound of theconfidence_level
correlation confidence interval.
Only if
return_drivers=True
.Otherwise, updates
adata
.var
oradata
.raw.var
, dependinguse_raw
with –'{direction} {lineage} corr'
- the potential lineage drivers.'{direction} {lineage} qval'
- the corrected p-values.
Also updates the following fields –
lineage_drivers
- same as the returned values.
- compute_lineage_priming(method='kl_divergence', early_cells=None)
Compute the degree of lineage priming.
This method computes how naive vs. committed each individual cell is. It returns a score where 0 stands for naive and 1 stands for committed.
- Parameters
method (
Literal
[‘kl_divergence’, ‘entropy’]) –The method used to compute the degree of lineage priming. Valid options are:
’kl_divergence’: as in [Velten et al., 2017], computes KL-divergence between the fate probabilities of a cell and the average fate probabilities. Computation of average fate probabilities can be restricted to a set of user-defined
early_cells
.’entropy’: as in [Setty et al., 2019], computes entropy over a cell’s fate probabilities.
early_cells (
Union
[Mapping
[str
,Sequence
[str
]],Sequence
[str
],None
]) – Cell ids or a mask marking early cells. If None, use all cells. Only used whenmethod='kl_divergence'
. Cell ids or a mask marking early cells. If None, use all cells. Only used whenmethod='kl_divergence'
. If adict
, the key species a cluster key inanndata.AnnData.obs
and the values specify cluster labels containing early cells.
- Returns
- Return type
The priming degree.
- compute_partition()
Compute communication classes for the Markov chain.
- Returns
Nothing, but updates the following fields:
- Return type
- copy()
Return a copy of self, including the underlying
adata
object.- Return type
- property is_irreducible
Whether the Markov chain is irreducible or not.
- property issparse: bool
Whether the transition matrix is sparse or not.
- Return type
- property kernel: cellrank.tl.kernels._base_kernel.KernelExpression
Underlying kernel.
- Return type
KernelExpression
- property lineage_absorption_times: pandas.core.frame.DataFrame
Lineage absorption times.
- Return type
DataFrame
- property lineage_drivers: pandas.core.frame.DataFrame
Lineage drivers.
- Return type
DataFrame
- plot_absorption_probabilities(data, prop, discrete=False, lineages=None, cluster_key=None, mode='embedding', time_key='latent_time', title=None, same_plot=False, cmap='viridis', **kwargs)
Plot discrete states or probabilities in an embedding.
- Parameters
discrete (
bool
) – Whether to plot in discrete or continuous mode.lineages (
Union
[str
,Sequence
[str
],None
]) – Plot only these lineages. If None, plot all lineages.cluster_key (
Optional
[str
]) – Key fromadata
.obs
for plotting categorical observations.mode (
str
) –Can be either ‘embedding’ or ‘time’:
’embedding’ - plot the embedding while coloring in the absorption probabilities.
’time’ - plot the pseudotime on x-axis and the absorption probabilities on y-axis.
time_key (
str
) – Key fromadata
.obs
to use as a pseudotime ordering of the cells.title (
Optional
[str
]) – Either None, in which case titles are'{to,from} {terminal,initial} {state}'
, or an array of titles, one per lineage.same_plot (
bool
) – Whether to plot the lineages on the same plot using color gradients whenmode='embedding'
.cmap (
Union
[str
,ListedColormap
]) – Colormap to use.basis – Basis to use when
mode='embedding'
. If None, use ‘umap’.kwargs – Keyword arguments for
scvelo.pl.scatter()
.
- Returns
Nothing, just plots the figure. Optionally saves it based on
save
.- Return type
- plot_eigendecomposition(left=False, *args, **kwargs)
Plot eigenvectors in an embedding.
- Parameters
left (
bool
) – Whether to plot left or right eigenvectors.use – Which or how many vectors are to be plotted.
abs_value – Whether to take the absolute value before plotting.
cluster_key – Key in
adata
.obs
for plotting categorical observations.basis – Basis to use when
mode='embedding'
. If None, use ‘umap’.kwargs – Keyword arguments for
scvelo.pl.scatter()
.
- Returns
Nothing, just plots the figure. Optionally saves it based on
save
.- Return type
- plot_lineage_drivers(lineage, n_genes=8, ncols=None, use_raw=False, title_fmt='{gene} qval={qval:.4e}', figsize=None, dpi=None, save=None, **kwargs)
Plot lineage drivers discovered by
compute_lineage_drivers()
.- Parameters
lineage (
str
) – Lineage for which to plot the driver genes.n_genes (
int
) – Top most correlated genes to plot.use_raw (
bool
) – Whether to look inadata
.raw.var
oradata
.var
.title_fmt (
str
) – Title format. Possible keywords include {gene}, {qval}, {corr} for gene name, q-value and correlation, respectively.figsize (
Optional
[Tuple
[float
,float
]]) – Size of the figure.save (
Union
[str
,Path
,None
]) – Filename where to save the plot.kwargs – Keyword arguments for
scvelo.pl.scatter()
.
- Returns
Nothing, just plots the figure. Optionally saves it based on
save
.- Return type
- plot_lineage_drivers_correlation(lineage_x, lineage_y, color=None, gene_sets=None, gene_sets_colors=None, use_raw=False, cmap='RdYlBu_r', fontsize=12, adjust_text=False, legend_loc='best', figsize=(4, 4), dpi=None, save=None, show=True, **kwargs)
Show scatter plot of gene-correlations between two lineages.
Optionally, you can pass a
dict
of gene names that will be annotated in the plot.- Parameters
lineage_x (
str
) – Name of the lineage on the x-axis.lineage_y (
str
) – Name of the lineage on the y-axis.gene_sets (
Optional
[Dict
[str
,Iterable
]]) – Gene sets annotations of the form {‘gene_set_name’: [‘gene_1’, ‘gene_2’], …}.gene_sets_colors (
Optional
[Iterable
]) – List of colors where each entry corresponds to a gene set fromgenes_sets
. If None and keys ingene_sets
correspond to lineage names, use the lineage colors. Otherwise, use default colors.use_raw (
bool
) – Whether to accessadata
.raw.var
oradata
.var
.cmap (
str
) – Colormap to use.fontsize (
int
) – Size of the text when plottinggene_sets
.adjust_text (
bool
) – Whether to automatically adjust text in order to reduce overlap.legend_loc (
Optional
[str
]) – Position of the legend. If None, don’t show the legend. Only used whengene_sets!=None
.figsize (
Optional
[Tuple
[float
,float
]]) – Size of the figure.save (
Union
[str
,Path
,None
]) – Filename where to save the plot.show (
bool
) – If False, returnmatplotlib.pyplot.Axes
.kwargs (
Any
) – Keyword arguments forscanpy.pl.scatter()
.
- Return type
Optional
[Axes
]- Returns
matplotlib.pyplot.Axes
– The axis object ifshow=False
.None – Nothing, just plots the figure. Optionally saves it based on
save
.
Notes
This plot is based on the following notebook by Maren Büttner.
- plot_spectrum(n=None, real_only=False, show_eigengap=True, show_all_xticks=True, legend_loc=None, title=None, figsize=(5, 5), dpi=100, save=None, marker='.', **kwargs)
Plot the top eigenvalues in real or complex plane.
- Parameters
n (
Optional
[int
]) – Number of eigenvalues to show. If None, show all that have been computed.real_only (
bool
) – Whether to plot only the real part of the spectrum.show_eigengap (
bool
) – When real_only=True, this determines whether to show the inferred eigengap as a dotted line.show_all_xticks (
bool
) – When real_only=True, this determines whether to show the indices of all eigenvalues on the x-axis.legend_loc (
Optional
[str
]) – Location parameter for the legend.figsize (
Optional
[Tuple
[float
,float
]]) – Size of the figure.dpi (
int
) – Dots per inch.save (
Union
[str
,Path
,None
]) – Filename where to save the plot.marker (
str
) – Marker symbol used, valid options can be found inmatplotlib.markers
.kwargs – Keyword arguments for
matplotlib.pyplot.scatter()
.
- Returns
Nothing, just plots the figure. Optionally saves it based on
save
.- Return type
- plot_terminal_states(data, prop, discrete=False, lineages=None, cluster_key=None, mode='embedding', time_key='latent_time', title=None, same_plot=False, cmap='viridis', **kwargs)
Plot discrete states or probabilities in an embedding.
- Parameters
discrete (
bool
) – Whether to plot in discrete or continuous mode.lineages (
Union
[str
,Sequence
[str
],None
]) – Plot only these lineages. If None, plot all lineages.cluster_key (
Optional
[str
]) – Key fromadata
.obs
for plotting categorical observations.mode (
str
) –Can be either ‘embedding’ or ‘time’:
’embedding’ - plot the embedding while coloring in the absorption probabilities.
’time’ - plot the pseudotime on x-axis and the absorption probabilities on y-axis.
time_key (
str
) – Key fromadata
.obs
to use as a pseudotime ordering of the cells.title (
Optional
[str
]) – Either None, in which case titles are'{to,from} {terminal,initial} {state}'
, or an array of titles, one per lineage.same_plot (
bool
) – Whether to plot the lineages on the same plot using color gradients whenmode='embedding'
.cmap (
Union
[str
,ListedColormap
]) – Colormap to use.basis – Basis to use when
mode='embedding'
. If None, use ‘umap’.kwargs – Keyword arguments for
scvelo.pl.scatter()
.
- Returns
Nothing, just plots the figure. Optionally saves it based on
save
.- Return type
- property priming_degree: pandas.core.series.Series
Priming degree.
- Return type
Series
- static read(fname)
Deserialize self from a file.
- property recurrent_classes
Recurrent classes of the Markov chain.
- rename_terminal_states(new_names, update_adata=True)
Rename the names of
terminal_states
.
- set_terminal_states(labels, cluster_key=None, en_cutoff=None, p_thresh=None, add_to_existing=False, **kwargs)
Manually define terminal states.
- Parameters
labels (
Union
[Series
,Dict
[str
,Sequence
[Any
]]]) –Defines the terminal states. Valid options are:
categorical
pandas.Series
where each category corresponds to one terminal state. NaN entries denote cells that do not belong to any terminal state, i.e. these are either initial or transient cells.dict
where keys are terminal states and values are lists of cell barcodes corresponding to annotations inadata
.obs_names
. If only 1 key is provided, values should correspond to terminal state clusters if a categoricalpandas.Series
can be found inadata
.obs
.
cluster_key (
Optional
[str
]) – Key fromadata.obs
where categorical cluster labels are stored. These are used to associate names and colors with each terminal state. Each terminal state will be given the name and color corresponding to the cluster it mostly overlaps with.en_cutoff (
Optional
[float
]) – Ifcluster_key
is given, this parameter determines when an approximate recurrent class will be labeled as ‘Unknown’, based on the entropy of the distribution of cells over transcriptomic clusters.p_thresh (
Optional
[float
]) – If cell cycle scores were provided, a Wilcoxon rank-sum test is conducted to identify cell-cycle states. If the test returns a positive statistic and a p-value smaller thanp_thresh
, a warning will be issued.add_to_existing (
bool
) – Whether the new terminal states should be added to pre-existing ones. Cells already assigned to a terminal state will be re-assigned to the new terminal state if there’s a conflict between old and new annotations. This throws an error if no previous annotations corresponding to terminal states have been found.
- Returns
Nothing, but updates the following fields:
- Return type
- property terminal_states: pandas.core.series.Series
Terminal states.
- Return type
Series
- property terminal_states_probabilities: pandas.core.series.Series
Terminal states probabilities.
- Return type
Series
- property transient_classes
Transient classes of the Markov chain.
- property transition_matrix: Union[numpy.ndarray, scipy.sparse.base.spmatrix]
Transition matrix.
Kernels¶
Velocity Kernel¶
- class cellrank.tl.kernels.VelocityKernel(adata, backward=False, vkey='velocity', xkey='Ms', gene_subset=None, compute_cond_num=False, check_connectivity=False, **kwargs)[source]
Kernel which computes a transition matrix based on RNA velocity.
This borrows ideas from both [La Manno et al., 2018] and [Bergen et al., 2020]. 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
adata (
anndata.AnnData
) – Annotated data object.backward (
bool
) – Direction of the process.vkey (
str
) – Key inadata
.uns
where the velocities are stored.xkey (
str
) – Key inadata
.layers
where expected gene expression counts are stored.gene_subset (
Optional
[Iterable
]) – List of genes to be used to compute transition probabilities. By default, genes fromadata
.var['velocity_genes']
are used.compute_cond_num (
bool
) – Whether to compute condition number of the transition matrix. Note that this might be costly, since it does not use sparse implementation.check_connectivity (
bool
) – Check whether the underlying KNN graph is connected.kwargs (
Any
) – Keyword arguments forcellrank.tl.kernels.Kernel
.
- compute_transition_matrix(mode='deterministic', backward_mode='transpose', scheme='correlation', softmax_scale=None, n_samples=1000, seed=None, check_irreducibility=False, **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 (
str
) –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.’sampling’ - sample 1 transition matrix from the velocity distribution.
backward_mode (
str
) –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 using1 / median(correlations)
. The idea behind this is to scale the softmax to counteract everything tending to orthogonality in high dimensions.scheme (
Union
[str
,Callable
]) –Similarity scheme between cells as described in [Li et al., 2021]. Can be one of the following:
’dot_product’:
cellrank.tl.kernels.DotProductScheme
.’cosine’:
cellrank.tl.kernels.CosineScheme
.’correlation’:
cellrank.tl.kernels.CorrelationScheme
.
Alternatively, any function can be passed as long as it follows the signature of
cellrank.tl.kernels.SimilaritySchemeABC.__call__()
.n_samples (
int
) – Number of bootstrap samples whenmode='monte_carlo'
.seed (
Optional
[int
]) – Set the seed for random state when the method requiresn_samples
.check_irreducibility (
bool
) – Optional check for irreducibility of the final transition matrix.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.
- Returns
Makes available the following fields:
- Return type
- property logits: scipy.sparse.csr.csr_matrix
Array of shape
(n_cells, n_cells)
containing the logits.- Return type
csr_matrix
- copy()[source]
Return a copy of self.
- Return type
Cosine Similarity Scheme¶
- class cellrank.tl.kernels.CosineScheme[source]¶
Cosine similarity scheme as defined in eq. (4.7) [Li et al., 2021].
\(v(s_i, s_j) = g(cos(\delta_{i, j}, v_i))\)
where \(v_i\) is the velocity vector of cell \(i\), \(\delta_{i, j}\) corresponds to the transcriptional displacement between cells \(i\) and \(j\) and \(g\) is a softmax function with some scaling parameter.
- __call__(v, D, softmax_scale=1.0)¶
Compute transition probability of a cell to its nearest neighbors using RNA velocity.
- Parameters
v (
ndarray
) – Array of shape(n_genes,)
or(n_neighbors, n_genes)
containing the velocity vector(s). The second case is used for the backward process.D (
ndarray
) – Array of shape(n_neighbors, n_genes)
corresponding to the transcriptomic displacement of the current cell with respect to ist nearest neighbors.softmax_scale (
float
) – Scaling factor for the softmax function.
- Returns
The probability and logits arrays of shape
(n_neighbors,)
.- Return type
- hessian(v, D, softmax_scale=1.0)¶
Compute the Hessian.
- Parameters
- Returns
The full Hessian of shape
(n_neighbors, n_genes, n_genes)
or only its diagonal of shape(n_neighbors, n_genes)
.- Return type
Correlation Scheme¶
- class cellrank.tl.kernels.CorrelationScheme[source]¶
Pearson correlation scheme as defined in eq. (4.8) [Li et al., 2021].
\(v(s_i, s_j) = g(corr(\delta_{i, j}, v_i))\)
where \(v_i\) is the velocity vector of cell \(i\), \(\delta_{i, j}\) corresponds to the transcriptional displacement between cells \(i\) and \(j\) and \(g\) is a softmax function with some scaling parameter.
- __call__(v, D, softmax_scale=1.0)¶
Compute transition probability of a cell to its nearest neighbors using RNA velocity.
- Parameters
v (
ndarray
) – Array of shape(n_genes,)
or(n_neighbors, n_genes)
containing the velocity vector(s). The second case is used for the backward process.D (
ndarray
) – Array of shape(n_neighbors, n_genes)
corresponding to the transcriptomic displacement of the current cell with respect to ist nearest neighbors.softmax_scale (
float
) – Scaling factor for the softmax function.
- Returns
The probability and logits arrays of shape
(n_neighbors,)
.- Return type
- hessian(v, D, softmax_scale=1.0)¶
Compute the Hessian.
- Parameters
- Returns
The full Hessian of shape
(n_neighbors, n_genes, n_genes)
or only its diagonal of shape(n_neighbors, n_genes)
.- Return type
Dot Product Scheme¶
- class cellrank.tl.kernels.DotProductScheme[source]¶
Dot product scheme as defined in eq. (4.9) [Li et al., 2021].
\(v(s_i, s_j) = g(\delta_{i, j}^T v_i)\)
where \(v_i\) is the velocity vector of cell \(i\), \(\delta_{i, j}\) corresponds to the transcriptional displacement between cells \(i\) and \(j\) and \(g\) is a softmax function with some scaling parameter.
- __call__(v, D, softmax_scale=1.0)¶
Compute transition probability of a cell to its nearest neighbors using RNA velocity.
- Parameters
v (
ndarray
) – Array of shape(n_genes,)
or(n_neighbors, n_genes)
containing the velocity vector(s). The second case is used for the backward process.D (
ndarray
) – Array of shape(n_neighbors, n_genes)
corresponding to the transcriptomic displacement of the current cell with respect to ist nearest neighbors.softmax_scale (
float
) – Scaling factor for the softmax function.
- Returns
The probability and logits arrays of shape
(n_neighbors,)
.- Return type
- hessian(v, D, softmax_scale=1.0)¶
Compute the Hessian.
- Parameters
- Returns
The full Hessian of shape
(n_neighbors, n_genes, n_genes)
or only its diagonal of shape(n_neighbors, n_genes)
.- Return type
Connectivity Kernel¶
- class cellrank.tl.kernels.ConnectivityKernel(adata, backward=False, conn_key='connectivities', compute_cond_num=False, 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.
scanpy.pp.neighbors()
, see [Wolf et al., 2018].spatial similarities, computed using e.g.
squidpy.gr.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 velocity-derived transition matrix via
cellrank.tl.kernels.VelocityKernel
.Optionally, we apply a density correction as described in [Coifman et al., 2005], where we use the implementation of [Haghverdi et al., 2016].
- Parameters
adata (
anndata.AnnData
) – Annotated data object.backward (
bool
) – Direction of the process.conn_key (
str
) – Key inanndata.AnnData.obsp
to obtain the connectivity matrix describing cell-cell similarity.compute_cond_num (
bool
) – Whether to compute condition number of the transition matrix. Note that this might be costly, since it does not use sparse implementation.check_connectivity (
bool
) – Check whether the underlying KNN graph is connected.
- 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
scanpy.pp.neighbors()
. Depending on the parameters used there, they can be UMAP connectivities or gaussian-kernel-based connectivities with adaptive kernel width.- Parameters
density_normalize (
bool
) – Whether or not to use the underlying KNN graph for density normalization.- Returns
Makes
transition_matrix
available.- Return type
- copy()[source]
Return a copy of self.
- Return type
Pseudotime Kernel¶
- class cellrank.tl.kernels.PseudotimeKernel(adata, backward=False, time_key='dpt_pseudotime', compute_cond_num=False, check_connectivity=False, **kwargs)[source]
Kernel which computes directed transition probabilities based on a KNN graph and pseudotime.
The KNN graph contains information about the (undirected) connectivities among cells, reflecting their similarity. Pseudotime can be used to either remove edges that point against the direction of increasing pseudotime [Setty et al., 2019], or to downweight them [Stassen et al., 2021].
- Parameters
adata (
anndata.AnnData
) – Annotated data object.backward (
bool
) – Direction of the process.time_key (
str
) – Key inadata
.obs
where the pseudotime is stored.compute_cond_num (
bool
) – Whether to compute condition number of the transition matrix. Note that this might be costly, since it does not use sparse implementation.kwargs (
Any
) – Keyword arguments forcellrank.tl.kernels.Kernel
.
- compute_transition_matrix(threshold_scheme='hard', frac_to_keep=0.3, b=10.0, nu=0.5, check_irreducibility=False, n_jobs=None, backend='loky', show_progress_bar=True, **kwargs)[source]
Compute transition matrix based on KNN graph and pseudotemporal ordering.
Depending on the choice of the thresholding_scheme, this is based on ideas by either Palantir [Setty et al., 2019] or VIA [Stassen et al., 2021].
When using a ‘hard’ thresholding scheme, this based on ideas by Palantir [Setty et al., 2019] which removes some edges that point against the direction of increasing pseudotime. To avoid disconnecting the graph, it does not remove all edges that point against the direction of increasing pseudotime but keeps the ones that point to cells inside a close radius. This radius is chosen according to the local cell density.
When using a ‘soft’ thresholding scheme, this is based on ideas by VIA [Stassen et al., 2021] which downweights edges that points against the direction of increasing pseudotime. Essentially, the further “behind” a query cell is in pseudotime with respect to the current reference cell, the more penalized will be its graph-connectivity.
- Parameters
frac_to_keep (
float
) – The frac_to_keep * n_neighbors closest neighbors (according to graph connectivities) are kept, no matter whether they lie in the pseudotemporal past or future. This is done to ensure that the graph remains connected. Only used when threshold_scheme=’hard’. frac_to_keep needs to fall within the interval [0, 1].b (
float
) – The growth rate of generalized logistic function. Only used when threshold_scheme=’soft’.nu (
float
) – Affects near which asymptote maximum growth occurs. Only used when threshold_scheme=’soft’.check_irreducibility (
bool
) – Optional check for irreducibility of the final transition matrix.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. Seejoblib.Parallel
for valid options.kwargs (
Any
) – Keyword arguments forthreshold_scheme
.
- Returns
Makes
transition_matrix
available.- Return type
- property pseudotime: numpy.array
Pseudotemporal ordering of cells.
- Return type
array
- copy()[source]
Return a copy of self.
- Return type
Hard Threshold Scheme¶
- class cellrank.tl.kernels.HardThresholdScheme[source]¶
Thresholding scheme inspired by Palantir [Setty et al., 2019].
Note that this won’t exactly reproduce the original Palantir results, for three reasons:
Palantir computes the KNN graph in a scaled space of diffusion components.
Palantir uses its own pseudotime to bias the KNN graph which is not implemented here.
Palantir uses a slightly different mechanism to ensure the graph remains connected when removing edges that point into the “pseudotime past”.
- __call__(cell_pseudotime, neigh_pseudotime, neigh_conn, n_neighs, frac_to_keep=0.3)[source]¶
Convert the undirected graph of cell-cell similarities into a directed one by removing “past” edges.
This uses a pseudotemporal measure to remove graph-edges that point into the pseudotime-past. For each cell, it keeps the closest neighbors, even if they are in the pseudotime past, to make sure the graph remains connected.
- Parameters
cell_pseudotime (
float
) – Pseudotime of the current cell.neigh_pseudotime (
ndarray
) – Array of shape(n_neighbors,)
containing pseudotimes of neighbors.neigh_conn (
ndarray
) – Array of shape(n_neighbors,)
containing connectivities of the current cell and its neighbors.n_neighs (
int
) – Number of neighbors to keep.frac_to_keep (
float
) – The frac_to_keep * n_neighbors closest neighbors (according to graph connectivities) are kept, no matter whether they lie in the pseudotemporal past or future. frac_to_keep needs to fall within the interval [0, 1].
- Returns
- Return type
Array of shape
(n_neighbors,)
containing the biased connectivities.
Soft Threshold Scheme¶
- class cellrank.tl.kernels.SoftThresholdScheme[source]¶
Thresholding scheme inspired by [Stassen et al., 2021].
The idea is to downweight edges that points against the direction of increasing pseudotime. Essentially, the further “behind” a query cell is in pseudotime with respect to the current reference cell, the more penalized will be its graph-connectivity.
- __call__(cell_pseudotime, neigh_pseudotime, neigh_conn, b=10.0, nu=0.5)[source]¶
Bias the connectivities by downweighting ones to past cells.
This function uses generalized logistic regression to weight the past connectivities.
- Parameters
cell_pseudotime (
float
) – Pseudotime of the current cell.neigh_pseudotime (
ndarray
) – Array of shape(n_neighbors,)
containing pseudotimes of neighbors.neigh_conn (
ndarray
) – Array of shape(n_neighbors,)
containing connectivities of the current cell and its neighbors.b (
float
) – The growth rate of generalized logistic function.nu (
float
) – Affects near which asymptote maximum growth occurs.
- Returns
- Return type
Array of shape
(n_neighbors,)
containing the biased connectivities.
CytoTRACE Kernel¶
- class cellrank.tl.kernels.CytoTRACEKernel(adata, backward=False, layer='Ms', aggregation='mean', use_raw=False, compute_cond_num=False, check_connectivity=False, **kwargs)[source]
Kernel which computes directed transition probabilities based on a KNN graph and the CytoTRACE score [Gulati et al., 2020].
The KNN graph contains information about the (undirected) connectivities among cells, reflecting their similarity. CytoTRACE can be used to estimate cellular plasticity and in turn, a pseudotemporal ordering of cells from more plastic to less plastic states. This kernel internally uses the
cellrank.tl.kernels.PseudotimeKernel
to direct the KNN graph on the basis of the CytoTRACE-derived pseudotime.Optionally, we apply a density correction as described in [Coifman et al., 2005], where we use the implementation of [Haghverdi et al., 2016].
- Parameters
adata (
anndata.AnnData
) – Annotated data object.backward (
bool
) – Direction of the process.layer (
str
) – Key inanndata.AnnData.layers
or ‘X’ foranndata.AnnData.X
from where to get the expression.aggregation (
Literal
[‘mean’, ‘median’, ‘hmean’, ‘gmean’]) –How to aggregate expression of the top-correlating genes. Valid options are:
’mean’: arithmetic mean.
’median’: median.
’gmean’: geometric mean.
’hmean’: harmonic mean.
compute_cond_num (
bool
) – Whether to compute condition number of the transition matrix. Note that this might be costly, since it does not use sparse implementation.check_connectivity (
bool
) – Check whether the underlying KNN graph is connected.kwargs (
Any
) – Keyword arguments forcellrank.tl.kernels.PseudotimeKernel
.
Example
Workflow:
# import packages and load data import scvelo as scv import cellrank as cr adata = cr.datasets.pancreas() # standard pre-processing sc.pp.filter_genes(adata, min_cells=10) sc.pp.normalize_total(adata) sc.pp.log1p(adata) sc.pp.highly_variable_genes(adata) # CytoTRACE by default uses imputed data - a simple way to compute KNN-imputed data is to use scVelo's moments # function. However, note that this function expects `spliced` counts because it's designed for RNA velocity, # so we're using a simple hack here: if 'spliced' not in adata.layers or 'unspliced' not in adata.layers: adata.layers['spliced'] = adata.X adata.layers['unspliced'] = adata.X # compute KNN-imputation using scVelo's moments function scv.pp.moments(adata) # import and initialize the CytoTRACE kernel, compute transition matrix - done! from cellrank.tl.kernels import CytoTRACEKernel ctk = CytoTRACEKernel(adata).compute_transition_matrix()
- compute_cytotrace(layer='Ms', aggregation='mean', use_raw=False)[source]
Re-implementation of the CytoTRACE algorithm [Gulati et al., 2020] to estimate cellular plasticity.
Computes the number of genes expressed per cell and ranks genes according to their correlation with this measure. Next, it selects to top-correlating genes and aggregates their (imputed) expression to obtain the CytoTRACE score. A high score stands for high differentiation potential (naive, plastic cells) and a low score stands for low differentiation potential (mature, differentiation cells).
- Parameters
layer (
str
) – Key inanndata.AnnData.layers
or ‘X’ foranndata.AnnData.X
from where to get the expression.aggregation (
Literal
[‘mean’, ‘median’, ‘hmean’, ‘gmean’]) –How to aggregate expression of the top-correlating genes. Valid options are:
’mean’: arithmetic mean.
’median’: median.
’gmean’: geometric mean.
’hmean’: harmonic mean.
use_raw (
bool
) – Whether to use theanndata.AnnData.raw
to compute the number of genes expressed per cell (#genes/cell) and the correlation of gene expression across cells with #genes/cell.
- Return type
- Returns
Nothing, just modifies
anndata.AnnData.obs
with the following keys –‘ct_score’: the normalized CytoTRACE score.
’ct_pseudotime’: associated pseudotime, essentially 1 - CytoTRACE score.
’ct_num_exp_genes’: the number of genes expressed per cell, basis of the CytoTRACE score.
It also modifies
anndata.AnnData.var
with the following keys –‘ct_gene_corr’: the correlation as specified above.
’ct_correlates’: indication of the genes used to compute the CytoTRACE score, i.e. the ones that correlated best with ‘num_exp_genes’.
Notes
This will not exactly reproduce the results of the original CytoTRACE algorithm [Gulati et al., 2020] because we allow for any normalization and imputation techniques whereas CytoTRACE has built-in specific methods for that.
- compute_transition_matrix(threshold_scheme='hard', frac_to_keep=0.3, b=10.0, nu=0.5, check_irreducibility=False, n_jobs=None, backend='loky', show_progress_bar=True, **kwargs)
Compute transition matrix based on KNN graph and pseudotemporal ordering.
Depending on the choice of the thresholding_scheme, this is based on ideas by either Palantir [Setty et al., 2019] or VIA [Stassen et al., 2021].
When using a ‘hard’ thresholding scheme, this based on ideas by Palantir [Setty et al., 2019] which removes some edges that point against the direction of increasing pseudotime. To avoid disconnecting the graph, it does not remove all edges that point against the direction of increasing pseudotime but keeps the ones that point to cells inside a close radius. This radius is chosen according to the local cell density.
When using a ‘soft’ thresholding scheme, this is based on ideas by VIA [Stassen et al., 2021] which downweights edges that points against the direction of increasing pseudotime. Essentially, the further “behind” a query cell is in pseudotime with respect to the current reference cell, the more penalized will be its graph-connectivity.
- Parameters
frac_to_keep (
float
) – The frac_to_keep * n_neighbors closest neighbors (according to graph connectivities) are kept, no matter whether they lie in the pseudotemporal past or future. This is done to ensure that the graph remains connected. Only used when threshold_scheme=’hard’. frac_to_keep needs to fall within the interval [0, 1].b (
float
) – The growth rate of generalized logistic function. Only used when threshold_scheme=’soft’.nu (
float
) – Affects near which asymptote maximum growth occurs. Only used when threshold_scheme=’soft’.check_irreducibility (
bool
) – Optional check for irreducibility of the final transition matrix.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. Seejoblib.Parallel
for valid options.kwargs (
Any
) – Keyword arguments forthreshold_scheme
.
- Returns
Makes
transition_matrix
available.- Return type
Precomputed Kernel¶
- class cellrank.tl.kernels.PrecomputedKernel(transition_matrix=None, adata=None, backward=False, compute_cond_num=False, **kwargs)[source]
Kernel which contains a precomputed transition matrix.
- Parameters
transition_matrix (
Union
[ndarray
,spmatrix
,KernelExpression
,str
,None
]) – Row-normalized transition matrix or a key inadata
.obsp
or acellrank.tl.kernels.KernelExpression
with a precomputed transition matrix. If None, try to determine the key based onbackward
.adata (
anndata.AnnData
) – Annotated data object. If None, a temporary placeholder object is created.backward (
bool
) – Direction of the process.compute_cond_num (
bool
) – Whether to compute condition number of the transition matrix. Note that this might be costly, since it does not use sparse implementation.kwargs (
Any
) – Keyword arguments forcellrank.tl.kernels.Kernel
.
- copy()[source]
Return a copy of self.
- Return type
- compute_transition_matrix(*args, **kwargs)[source]
Return self.
- Return type
Models¶
GAM¶
- class cellrank.ul.models.GAM(adata, n_knots=6, spline_order=3, distribution='gamma', link='log', max_iter=2000, expectile=None, grid=None, spline_kwargs=mappingproxy({}), **kwargs)[source]
Fit Generalized Additive Models (GAMs) using
pygam
.- Parameters
adata (
anndata.AnnData
) – Annotated data object.spline_order (
int
) – Order of the splines, i.e. 3 for cubic splines.distribution (
str
) – Name of the distribution. Available distributions can be found here.link (
str
) – Name of the link function. Available link functions can be found here.max_iter (
int
) – Maximum number of iterations for optimization.expectile (
Optional
[float
]) – Expectile forpygam.pygam.ExpectileGAM
. This forces the distribution to be ‘normal’ and link function to ‘identity’. Must be in interval (0, 1).grid (
Union
[str
,Mapping
,None
]) – Whether to perform a grid search. Keys correspond to a parameter names and values to range to be searched. If ‘default’, use the default grid. If None, don’t perform a grid search.spline_kwargs (
Mapping
) – Keyword arguments forpygam.s
.kwargs – Keyword arguments for
pygam.pygam.GAM
.
- fit(x=None, y=None, w=None, **kwargs)[source]
Fit the model.
- Parameters
x (
Optional
[ndarray
]) – Independent variables, array of shape (n_samples, 1). If None, usex
.y (
Optional
[ndarray
]) – Dependent variables, array of shape (n_samples, 1). If None, usey
.w (
Optional
[ndarray
]) – Optional weights ofx
, array of shape (n_samples,). If None, usew
.kwargs – Keyword arguments for underlying
model
’s fitting function.
- Returns
Fits the model and returns self.
- Return type
- predict(x_test=None, key_added='_x_test', **kwargs)[source]
Run the prediction.
- Parameters
- Returns
Updates and returns the following:
- Return type
- property adata: cellrank.ul.models._base_model.AnnData
Annotated data object.
- Returns
adata – Annotated data object.
- Return type
- property conf_int: numpy.ndarray
Array of shape (n_samples, 2) containing the lower and upper bounds of the confidence interval.
- Return type
- confidence_interval(x_test=None, **kwargs)[source]
Calculate the confidence interval.
- Parameters
x_test (
Optional
[ndarray
]) – Array of shape (n_samples,) used for confidence interval calculation. If None, usex_test
.kwargs – Keyword arguments for underlying
model
’s confidence method or fordefault_confidence_interval()
.
- Returns
Updates the following fields:
conf_int
- Array of shape (n_samples, 2) containing the lower and upper bounds of the confidence interval.
- Return type
- default_confidence_interval(x_test=None, **kwargs)
Calculate the confidence interval, if the underlying
model
has no method for it.This formula is taken from [DeSalvo, 1970], eq. 5.
- Parameters
x_test (
Optional
[ndarray
]) – Array of shape (n_samples,) used for confidence interval calculation. If None, usex_test
.kwargs – Keyword arguments for underlying
model
’s confidence method or fordefault_confidence_interval()
.
- Returns
Updates the following fields:
conf_int
- Array of shape (n_samples, 2) containing the lower and upper bounds of the confidence interval.x_hat
- Filtered independent variables used when calculating default confidence interval, usually same asx
.y_hat
- Filtered dependent variables used when calculating default confidence interval, usually same asy
.
- Return type
- property model: Any
The underlying model.
- Return type
- plot(figsize=(8, 5), same_plot=False, hide_cells=False, perc=None, abs_prob_cmap=<matplotlib.colors.ListedColormap object>, cell_color=None, lineage_color='black', alpha=0.8, lineage_alpha=0.2, title=None, size=15, lw=2, cbar=True, margins=0.015, xlabel='pseudotime', ylabel='expression', conf_int=True, lineage_probability=False, lineage_probability_conf_int=False, lineage_probability_color=None, obs_legend_loc='best', dpi=None, fig=None, ax=None, return_fig=False, save=None, **kwargs)
Plot the smoothed gene expression.
- Parameters
same_plot (
bool
) – Whether to plot all trends in the same plot.hide_cells (
bool
) – Whether to hide the cells.perc (
Optional
[Tuple
[float
,float
]]) – Percentile by which to clip the absorption probabilities.abs_prob_cmap (
ListedColormap
) – Colormap to use when coloring in the absorption probabilities.cell_color (
Optional
[str
]) – Key inanndata.AnnData.obs
oranndata.AnnData.var_names
used for coloring the cells.lineage_color (
str
) – Color for the lineage.alpha (
float
) – Alpha channel for cells.lineage_alpha (
float
) – Alpha channel for lineage confidence intervals.size (
int
) – Size of the points.lw (
float
) – Line width for the smoothed values.cbar (
bool
) – Whether to show colorbar.margins (
float
) – Margins around the plot.xlabel (
str
) – Label on the x-axis.ylabel (
str
) – Label on the y-axis.conf_int (
bool
) – Whether to show the confidence interval.lineage_probability (
bool
) – Whether to show smoothed lineage probability as a dashed line. Note that this will require 1 additional model fit.lineage_probability_conf_int (
Union
[bool
,float
]) – Whether to compute and show smoothed lineage probability confidence interval. Ifself
iscellrank.ul.models.GAMR
, it can also specify the confidence level, the default is 0.95. Only used whenshow_lineage_probability=True
.lineage_probability_color (
Optional
[str
]) – Color to use when plotting the smoothedlineage_probability
. If None, it’s the same aslineage_color
. Only used whenshow_lineage_probability=True
.obs_legend_loc (
Optional
[str
]) – Location of the legend whencell_color
corresponds to a categorical variable.fig (
Optional
[Figure
]) – Figure to use, if None, create a new one.ax (
matplotlib.axes.Axes
) – Ax to use, if None, create a new one.return_fig (
bool
) – If True, return the figure object.save (
Optional
[str
]) – Filename where to save the plot. If None, just shows the plots.kwargs – Keyword arguments for
matplotlib.axes.Axes.legend()
, e.g. to disable the legend, specifyloc=None
. Only available whenshow_lineage_probability=True
.
- Returns
Nothing, just plots the figure. Optionally saves it based on
save
.- Return type
- prepare(gene, lineage, backward=False, time_range=None, data_key='X', time_key='latent_time', use_raw=False, threshold=None, weight_threshold=(0.01, 0.01), filter_cells=None, n_test_points=200)
Prepare the model to be ready for fitting.
- Parameters
gene (
str
) – Gene inadata
.var_names
or inadata
.raw.var_names
.lineage (
Optional
[str
]) – Name of a lineage inadata
.obsm['{lineage_key}']
. If None, all weights will be set to 1.backward (
bool
) – Direction of the process.time_range (
Union
[float
,Tuple
[float
,float
],None
]) –Specify start and end times:
data_key (
str
) – Key inadata
.layers
or ‘X’ foradata
.X
. Ifuse_raw=True
, it’s always set to ‘X’.time_key (
str
) – Key inadata
.obs
where the pseudotime is stored.threshold (
Optional
[float
]) – Consider only cells with weights >threshold
when estimating the test endpoint. If None, use the median of the weights.weight_threshold (
Union
[float
,Tuple
[float
,float
]]) – Set all weights belowweight_threshold
toweight_threshold
if afloat
, or to the second value, if atuple
.filter_cells (
Optional
[float
]) – Filter out all cells with expression values lower than this threshold.n_test_points (
int
) – Number of test points. If None, use the original points based onthreshold
.
- Returns
Nothing, but updates the following fields:
x
- Filtered independent variables of shape (n_filtered_cells, 1) used for fitting.y
- Filtered dependent variables of shape (n_filtered_cells, 1) used for fitting.w
- Filtered weights of shape (n_filtered_cells,) used for fitting.x_all
- Unfiltered independent variables of shape (n_cells, 1).y_all
- Unfiltered dependent variables of shape (n_cells, 1).w_all
- Unfiltered weights of shape (n_cells,).x_test
- Independent variables of shape (n_samples, 1) used for prediction.prepared
- Whether the model is prepared for fitting.
- Return type
- property prepared
Whether the model is prepared for fitting.
- static read(fname)
Deserialize self from a file.
- property w: numpy.ndarray
Filtered weights of shape (n_filtered_cells,) used for fitting.
- Return type
- property w_all: numpy.ndarray
Unfiltered weights of shape (n_cells,).
- Return type
- write(fname, ext='pickle')
Serialize self to a file.
- property x: numpy.ndarray
Filtered independent variables of shape (n_filtered_cells, 1) used for fitting.
- Return type
- property x_all: numpy.ndarray
Unfiltered independent variables of shape (n_cells, 1).
- Return type
- property x_hat: numpy.ndarray
Filtered independent variables used when calculating default confidence interval, usually same as
x
.- Return type
- property x_test: numpy.ndarray
Independent variables of shape (n_samples, 1) used for prediction.
- Return type
- property y: numpy.ndarray
Filtered dependent variables of shape (n_filtered_cells, 1) used for fitting.
- Return type
- property y_all: numpy.ndarray
Unfiltered dependent variables of shape (n_cells, 1).
- Return type
- property y_hat: numpy.ndarray
Filtered dependent variables used when calculating default confidence interval, usually same as
y
.- Return type
- property y_test: numpy.ndarray
Prediction values of shape (n_samples,) for
x_test
.- Return type
SKLearnModel¶
- class cellrank.ul.models.SKLearnModel(adata, model, weight_name=None, ignore_raise=False)[source]
Wrapper around
sklearn.base.BaseEstimator
.- Parameters
adata (
anndata.AnnData
) – Annotated data object.model (
BaseEstimator
) – Instance of the underlyingsklearn
estimator, such assklearn.svm.SVR
.weight_name (
Optional
[str
]) – Name of the weight argument formodel
.fit
. If None, to determine it automatically. If and empty string, no weights will be used.ignore_raise (
bool
) – Do not raise an exception if weight argument is not found in the fitting function ofmodel
. This is useful in case when weight is passed in**kwargs
and cannot be determined from signature.
- fit(x=None, y=None, w=None, **kwargs)[source]
Fit the model.
- Parameters
x (
Optional
[ndarray
]) – Independent variables, array of shape (n_samples, 1). If None, usex
.y (
Optional
[ndarray
]) – Dependent variables, array of shape (n_samples, 1). If None, usey
.w (
Optional
[ndarray
]) – Optional weights ofx
, array of shape (n_samples,). If None, usew
.kwargs – Keyword arguments for underlying
model
’s fitting function.
- Returns
Fits the model and returns self.
- Return type
- predict(x_test=None, key_added='_x_test', **kwargs)[source]
Run the prediction.
- Parameters
- Returns
Updates and returns the following:
- Return type
- confidence_interval(x_test=None, **kwargs)[source]
Calculate the confidence interval.
Use
default_confidence_interval()
function if underlyingmodel
has not method for confidence interval calculation.- Parameters
x_test (
Optional
[ndarray
]) – Array of shape (n_samples,) used for confidence interval calculation. If None, usex_test
.kwargs – Keyword arguments for underlying
model
’s confidence method or fordefault_confidence_interval()
.
- Returns
Updates the following fields:
conf_int
- Array of shape (n_samples, 2) containing the lower and upper bounds of the confidence interval.
- Return type
- property model: sklearn.base.BaseEstimator
The underlying
sklearn.base.BaseEstimator
.- Return type
- copy()[source]
Return a copy of self.
- Return type
- property adata: cellrank.ul.models._base_model.AnnData
Annotated data object.
- Returns
adata – Annotated data object.
- Return type
- property conf_int: numpy.ndarray
Array of shape (n_samples, 2) containing the lower and upper bounds of the confidence interval.
- Return type
- default_confidence_interval(x_test=None, **kwargs)
Calculate the confidence interval, if the underlying
model
has no method for it.This formula is taken from [DeSalvo, 1970], eq. 5.
- Parameters
x_test (
Optional
[ndarray
]) – Array of shape (n_samples,) used for confidence interval calculation. If None, usex_test
.kwargs – Keyword arguments for underlying
model
’s confidence method or fordefault_confidence_interval()
.
- Returns
Updates the following fields:
conf_int
- Array of shape (n_samples, 2) containing the lower and upper bounds of the confidence interval.x_hat
- Filtered independent variables used when calculating default confidence interval, usually same asx
.y_hat
- Filtered dependent variables used when calculating default confidence interval, usually same asy
.
- Return type
- plot(figsize=(8, 5), same_plot=False, hide_cells=False, perc=None, abs_prob_cmap=<matplotlib.colors.ListedColormap object>, cell_color=None, lineage_color='black', alpha=0.8, lineage_alpha=0.2, title=None, size=15, lw=2, cbar=True, margins=0.015, xlabel='pseudotime', ylabel='expression', conf_int=True, lineage_probability=False, lineage_probability_conf_int=False, lineage_probability_color=None, obs_legend_loc='best', dpi=None, fig=None, ax=None, return_fig=False, save=None, **kwargs)
Plot the smoothed gene expression.
- Parameters
same_plot (
bool
) – Whether to plot all trends in the same plot.hide_cells (
bool
) – Whether to hide the cells.perc (
Optional
[Tuple
[float
,float
]]) – Percentile by which to clip the absorption probabilities.abs_prob_cmap (
ListedColormap
) – Colormap to use when coloring in the absorption probabilities.cell_color (
Optional
[str
]) – Key inanndata.AnnData.obs
oranndata.AnnData.var_names
used for coloring the cells.lineage_color (
str
) – Color for the lineage.alpha (
float
) – Alpha channel for cells.lineage_alpha (
float
) – Alpha channel for lineage confidence intervals.size (
int
) – Size of the points.lw (
float
) – Line width for the smoothed values.cbar (
bool
) – Whether to show colorbar.margins (
float
) – Margins around the plot.xlabel (
str
) – Label on the x-axis.ylabel (
str
) – Label on the y-axis.conf_int (
bool
) – Whether to show the confidence interval.lineage_probability (
bool
) – Whether to show smoothed lineage probability as a dashed line. Note that this will require 1 additional model fit.lineage_probability_conf_int (
Union
[bool
,float
]) – Whether to compute and show smoothed lineage probability confidence interval. Ifself
iscellrank.ul.models.GAMR
, it can also specify the confidence level, the default is 0.95. Only used whenshow_lineage_probability=True
.lineage_probability_color (
Optional
[str
]) – Color to use when plotting the smoothedlineage_probability
. If None, it’s the same aslineage_color
. Only used whenshow_lineage_probability=True
.obs_legend_loc (
Optional
[str
]) – Location of the legend whencell_color
corresponds to a categorical variable.fig (
Optional
[Figure
]) – Figure to use, if None, create a new one.ax (
matplotlib.axes.Axes
) – Ax to use, if None, create a new one.return_fig (
bool
) – If True, return the figure object.save (
Optional
[str
]) – Filename where to save the plot. If None, just shows the plots.kwargs – Keyword arguments for
matplotlib.axes.Axes.legend()
, e.g. to disable the legend, specifyloc=None
. Only available whenshow_lineage_probability=True
.
- Returns
Nothing, just plots the figure. Optionally saves it based on
save
.- Return type
- prepare(gene, lineage, backward=False, time_range=None, data_key='X', time_key='latent_time', use_raw=False, threshold=None, weight_threshold=(0.01, 0.01), filter_cells=None, n_test_points=200)
Prepare the model to be ready for fitting.
- Parameters
gene (
str
) – Gene inadata
.var_names
or inadata
.raw.var_names
.lineage (
Optional
[str
]) – Name of a lineage inadata
.obsm['{lineage_key}']
. If None, all weights will be set to 1.backward (
bool
) – Direction of the process.time_range (
Union
[float
,Tuple
[float
,float
],None
]) –Specify start and end times:
data_key (
str
) – Key inadata
.layers
or ‘X’ foradata
.X
. Ifuse_raw=True
, it’s always set to ‘X’.time_key (
str
) – Key inadata
.obs
where the pseudotime is stored.threshold (
Optional
[float
]) – Consider only cells with weights >threshold
when estimating the test endpoint. If None, use the median of the weights.weight_threshold (
Union
[float
,Tuple
[float
,float
]]) – Set all weights belowweight_threshold
toweight_threshold
if afloat
, or to the second value, if atuple
.filter_cells (
Optional
[float
]) – Filter out all cells with expression values lower than this threshold.n_test_points (
int
) – Number of test points. If None, use the original points based onthreshold
.
- Returns
Nothing, but updates the following fields:
x
- Filtered independent variables of shape (n_filtered_cells, 1) used for fitting.y
- Filtered dependent variables of shape (n_filtered_cells, 1) used for fitting.w
- Filtered weights of shape (n_filtered_cells,) used for fitting.x_all
- Unfiltered independent variables of shape (n_cells, 1).y_all
- Unfiltered dependent variables of shape (n_cells, 1).w_all
- Unfiltered weights of shape (n_cells,).x_test
- Independent variables of shape (n_samples, 1) used for prediction.prepared
- Whether the model is prepared for fitting.
- Return type
- property prepared
Whether the model is prepared for fitting.
- static read(fname)
Deserialize self from a file.
- property w: numpy.ndarray
Filtered weights of shape (n_filtered_cells,) used for fitting.
- Return type
- property w_all: numpy.ndarray
Unfiltered weights of shape (n_cells,).
- Return type
- write(fname, ext='pickle')
Serialize self to a file.
- property x: numpy.ndarray
Filtered independent variables of shape (n_filtered_cells, 1) used for fitting.
- Return type
- property x_all: numpy.ndarray
Unfiltered independent variables of shape (n_cells, 1).
- Return type
- property x_hat: numpy.ndarray
Filtered independent variables used when calculating default confidence interval, usually same as
x
.- Return type
- property x_test: numpy.ndarray
Independent variables of shape (n_samples, 1) used for prediction.
- Return type
- property y: numpy.ndarray
Filtered dependent variables of shape (n_filtered_cells, 1) used for fitting.
- Return type
- property y_all: numpy.ndarray
Unfiltered dependent variables of shape (n_cells, 1).
- Return type
- property y_hat: numpy.ndarray
Filtered dependent variables used when calculating default confidence interval, usually same as
y
.- Return type
GAMR¶
- class cellrank.ul.models.GAMR(adata, n_knots=5, distribution='gaussian', basis='cr', knotlocs='auto', offset='default', smoothing_penalty=1.0, **kwargs)[source]
Wrapper around R’s mgcv package for fitting Generalized Additive Models (GAMs).
- Parameters
adata (
anndata.AnnData
) – Annotated data object.n_knots (
int
) – Number of knots.distribution (
str
) – Distribution family in rpy2.robjects.r, such as ‘gaussian’ or ‘nb’ for negative binomial. If ‘nb’, raw count data inadata
.raw
is always used.basis (
str
) – Basis for the smoothing term. See here for valid options.knotlocs (
str
) –Position of the knots. Can be one of the following:
’auto’ - let mgcv handle the knot positions.
’density’ - position the knots based on the density of the pseudotime.
offset (
Union
[str
,ndarray
,None
]) – Offset term for the GAM. Only available whendistribution='nb'
. If ‘default’, it is calculated according to [Robinson and Oshlack, 2010]. The values are saved inadata
.obs['cellrank_offset']
. If None, no offset is used.smoothing_penalty (
float
) – Penalty for the smoothing term. The larger the value, the smoother the fitted curve.kwargs – Keyword arguments for
gam.control
. See here for reference.
- prepare(*args, **kwargs)[source]
Prepare the model to be ready for fitting. This also removes the zero and negative weights and prepares the design matrix.
- Parameters
lineage – Name of a lineage in
adata
.obsm['{lineage_key}']
. If None, all weights will be set to 1.backward – Direction of the process.
time_range –
Specify start and end times:
data_key – Key in
adata
.layers
or ‘X’ foradata
.X
. Ifuse_raw=True
, it’s always set to ‘X’.time_key – Key in
adata
.obs
where the pseudotime is stored.use_raw – Whether to access
adata
.raw
or not.threshold – Consider only cells with weights >
threshold
when estimating the test endpoint. If None, use the median of the weights.weight_threshold – Set all weights below
weight_threshold
toweight_threshold
if afloat
, or to the second value, if atuple
.filter_cells – Filter out all cells with expression values lower than this threshold.
n_test_points – Number of test points. If None, use the original points based on
threshold
.
- Returns
Nothing, but updates the following fields:
x
- Filtered independent variables of shape (n_filtered_cells, 1) used for fitting.y
- Filtered dependent variables of shape (n_filtered_cells, 1) used for fitting.w
- Filtered weights of shape (n_filtered_cells,) used for fitting.x_all
- Unfiltered independent variables of shape (n_cells, 1).y_all
- Unfiltered dependent variables of shape (n_cells, 1).w_all
- Unfiltered weights of shape (n_cells,).x_test
- Independent variables of shape (n_samples, 1) used for prediction.prepared
- Whether the model is prepared for fitting.
- Return type
- fit(x=None, y=None, w=None, **kwargs)[source]
Fit the model.
- Parameters
x (
Optional
[ndarray
]) – Independent variables, array of shape (n_samples, 1). If None, usex
.y (
Optional
[ndarray
]) – Dependent variables, array of shape (n_samples, 1). If None, usey
.w (
Optional
[ndarray
]) – Optional weights ofx
, array of shape (n_samples,). If None, usew
.kwargs – Keyword arguments for underlying
model
’s fitting function.
- Returns
Fits the model and returns self. Updates the following fields by filtering out 0 weights
w
:- Return type
- predict(x_test=None, key_added='_x_test', level=None, **kwargs)[source]
Run the prediction. This method can also compute the confidence interval.
- Parameters
x_test (
Optional
[ndarray
]) – Array of shape (n_samples,) used for prediction. If None, usex_test
.key_added (
str
) – Attribute name where to save thex_test
for later use. If None, don’t save it.kwargs – Keyword arguments for underlying
model
’s prediction method.level (
Optional
[float
]) – Confidence level for confidence interval calculation. If None, don’t compute the confidence interval. Must be in the interval [0, 1].
- Returns
Updates and returns the following:
- Return type
- confidence_interval(x_test=None, level=0.95, **kwargs)[source]
Calculate the confidence interval. Internally, this method calls
cellrank.ul.models.GAMR.predict()
to extract the confidence interval, if needed.- Parameters
- Returns
Updates the following fields:
conf_int
- Array of shape (n_samples, 2) containing the lower and upper bounds of the confidence interval.
- Return type
- property adata: cellrank.ul.models._base_model.AnnData
Annotated data object.
- Returns
adata – Annotated data object.
- Return type
- property conf_int: numpy.ndarray
Array of shape (n_samples, 2) containing the lower and upper bounds of the confidence interval.
- Return type
- default_confidence_interval(x_test=None, **kwargs)
Calculate the confidence interval, if the underlying
model
has no method for it.This formula is taken from [DeSalvo, 1970], eq. 5.
- Parameters
x_test (
Optional
[ndarray
]) – Array of shape (n_samples,) used for confidence interval calculation. If None, usex_test
.kwargs – Keyword arguments for underlying
model
’s confidence method or fordefault_confidence_interval()
.
- Returns
Updates the following fields:
conf_int
- Array of shape (n_samples, 2) containing the lower and upper bounds of the confidence interval.x_hat
- Filtered independent variables used when calculating default confidence interval, usually same asx
.y_hat
- Filtered dependent variables used when calculating default confidence interval, usually same asy
.
- Return type
- property model: Any
The underlying model.
- Return type
- plot(figsize=(8, 5), same_plot=False, hide_cells=False, perc=None, abs_prob_cmap=<matplotlib.colors.ListedColormap object>, cell_color=None, lineage_color='black', alpha=0.8, lineage_alpha=0.2, title=None, size=15, lw=2, cbar=True, margins=0.015, xlabel='pseudotime', ylabel='expression', conf_int=True, lineage_probability=False, lineage_probability_conf_int=False, lineage_probability_color=None, obs_legend_loc='best', dpi=None, fig=None, ax=None, return_fig=False, save=None, **kwargs)
Plot the smoothed gene expression.
- Parameters
same_plot (
bool
) – Whether to plot all trends in the same plot.hide_cells (
bool
) – Whether to hide the cells.perc (
Optional
[Tuple
[float
,float
]]) – Percentile by which to clip the absorption probabilities.abs_prob_cmap (
ListedColormap
) – Colormap to use when coloring in the absorption probabilities.cell_color (
Optional
[str
]) – Key inanndata.AnnData.obs
oranndata.AnnData.var_names
used for coloring the cells.lineage_color (
str
) – Color for the lineage.alpha (
float
) – Alpha channel for cells.lineage_alpha (
float
) – Alpha channel for lineage confidence intervals.size (
int
) – Size of the points.lw (
float
) – Line width for the smoothed values.cbar (
bool
) – Whether to show colorbar.margins (
float
) – Margins around the plot.xlabel (
str
) – Label on the x-axis.ylabel (
str
) – Label on the y-axis.conf_int (
bool
) – Whether to show the confidence interval.lineage_probability (
bool
) – Whether to show smoothed lineage probability as a dashed line. Note that this will require 1 additional model fit.lineage_probability_conf_int (
Union
[bool
,float
]) – Whether to compute and show smoothed lineage probability confidence interval. Ifself
iscellrank.ul.models.GAMR
, it can also specify the confidence level, the default is 0.95. Only used whenshow_lineage_probability=True
.lineage_probability_color (
Optional
[str
]) – Color to use when plotting the smoothedlineage_probability
. If None, it’s the same aslineage_color
. Only used whenshow_lineage_probability=True
.obs_legend_loc (
Optional
[str
]) – Location of the legend whencell_color
corresponds to a categorical variable.fig (
Optional
[Figure
]) – Figure to use, if None, create a new one.ax (
matplotlib.axes.Axes
) – Ax to use, if None, create a new one.return_fig (
bool
) – If True, return the figure object.save (
Optional
[str
]) – Filename where to save the plot. If None, just shows the plots.kwargs – Keyword arguments for
matplotlib.axes.Axes.legend()
, e.g. to disable the legend, specifyloc=None
. Only available whenshow_lineage_probability=True
.
- Returns
Nothing, just plots the figure. Optionally saves it based on
save
.- Return type
- property prepared
Whether the model is prepared for fitting.
- static read(fname)
Deserialize self from a file.
- property w: numpy.ndarray
Filtered weights of shape (n_filtered_cells,) used for fitting.
- Return type
- property w_all: numpy.ndarray
Unfiltered weights of shape (n_cells,).
- Return type
- write(fname, ext='pickle')
Serialize self to a file.
- property x: numpy.ndarray
Filtered independent variables of shape (n_filtered_cells, 1) used for fitting.
- Return type
- property x_all: numpy.ndarray
Unfiltered independent variables of shape (n_cells, 1).
- Return type
- property x_hat: numpy.ndarray
Filtered independent variables used when calculating default confidence interval, usually same as
x
.- Return type
- property x_test: numpy.ndarray
Independent variables of shape (n_samples, 1) used for prediction.
- Return type
- property y: numpy.ndarray
Filtered dependent variables of shape (n_filtered_cells, 1) used for fitting.
- Return type
- property y_all: numpy.ndarray
Unfiltered dependent variables of shape (n_cells, 1).
- Return type
- property y_hat: numpy.ndarray
Filtered dependent variables used when calculating default confidence interval, usually same as
y
.- Return type
Base Classes¶
BaseEstimator¶
- class cellrank.tl.estimators.BaseEstimator(obj, inplace=True, read_from_adata=False, obsp_key=None, g2m_key='G2M_score', s_key='S_score', write_to_adata=True, key=None)[source]¶
Base class for all estimators.
- Parameters
obj (
Union
[KernelExpression
, ~AnnData,spmatrix
,ndarray
]) – Either acellrank.tl.kernels.Kernel
object, ananndata.AnnData
object which stores the transition matrix in.obsp
attribute ornumpy
orscipy
array.inplace (
bool
) – Whether to modifyadata
object inplace or make a copy.read_from_adata (
bool
) – Whether to read available attributes inadata
, if present.obsp_key (
Optional
[str
]) – Key inobj.obsp
whenobj
is ananndata.AnnData
object.g2m_key (
Optional
[str
]) – Key inadata
.obs
. Can be used to detect cell-cycle driven start- or endpoints.s_key (
Optional
[str
]) – Key inadata
.obs
. Can be used to detect cell-cycle driven start- or endpoints.write_to_adata (
bool
) – Whether to write the transition matrix toadata
.obsp
and the parameters toadata
.uns
.key (
Optional
[str
]) – Key used when writing transition matrix toadata
. If None, thekey
is set to ‘T_bwd’ ifbackward
is True, else ‘T_fwd’. Only used whenwrite_to_adata=True
.
- set_terminal_states(labels, cluster_key=None, en_cutoff=None, p_thresh=None, add_to_existing=False, **kwargs)[source]¶
Manually define terminal states.
- Parameters
labels (
Union
[Series
,Dict
[str
,Sequence
[Any
]]]) –Defines the terminal states. Valid options are:
categorical
pandas.Series
where each category corresponds to one terminal state. NaN entries denote cells that do not belong to any terminal state, i.e. these are either initial or transient cells.dict
where keys are terminal states and values are lists of cell barcodes corresponding to annotations inadata
.obs_names
. If only 1 key is provided, values should correspond to terminal state clusters if a categoricalpandas.Series
can be found inadata
.obs
.
cluster_key (
Optional
[str
]) – Key fromadata.obs
where categorical cluster labels are stored. These are used to associate names and colors with each terminal state. Each terminal state will be given the name and color corresponding to the cluster it mostly overlaps with.en_cutoff (
Optional
[float
]) – Ifcluster_key
is given, this parameter determines when an approximate recurrent class will be labeled as ‘Unknown’, based on the entropy of the distribution of cells over transcriptomic clusters.p_thresh (
Optional
[float
]) – If cell cycle scores were provided, a Wilcoxon rank-sum test is conducted to identify cell-cycle states. If the test returns a positive statistic and a p-value smaller thanp_thresh
, a warning will be issued.add_to_existing (
bool
) – Whether the new terminal states should be added to pre-existing ones. Cells already assigned to a terminal state will be re-assigned to the new terminal state if there’s a conflict between old and new annotations. This throws an error if no previous annotations corresponding to terminal states have been found.
- Returns
Nothing, but updates the following fields:
terminal_states_probabilities
terminal_states
- Return type
- compute_absorption_probabilities(keys=None, check_irreducibility=False, solver='gmres', use_petsc=True, time_to_absorption=None, n_jobs=None, backend='loky', show_progress_bar=True, tol=1e-06, preconditioner=None)[source]¶
Compute absorption probabilities of a Markov chain.
For each cell, this computes the probability of it reaching any of the approximate recurrent classes defined by
terminal_states
.- Parameters
keys (
Optional
[Sequence
[str
]]) – Keys defining the recurrent classes.check_irreducibility (
bool
) – Check whether the transition matrix is irreducible.solver (
str
) –Solver to use for the linear problem. Options are ‘direct’, ‘gmres’, ‘lgmres’, ‘bicgstab’ or ‘gcrotmk’ when
use_petsc=False
or one ofpetsc4py.PETSc.KPS.Type
otherwise.Information on the
scipy
iterative solvers can be found inscipy.sparse.linalg()
or forpetsc4py
solver here.use_petsc (
bool
) – Whether to use solvers frompetsc4py
orscipy
. Recommended for large problems. If no installation is found, defaults toscipy.sparse.linalg.gmres()
.time_to_absorption (
Union
[str
,Sequence
[Union
[str
,Sequence
[str
]]],Dict
[Union
[str
,Sequence
[str
]],str
],None
]) –Whether to compute mean time to absorption and its variance to specific absorbing states.
If a
dict
, can be specified as{'Alpha': 'var', ...}
to also compute variance. In case when states are atuple
, time to absorption will be computed to the subset of these states, such as[('Alpha', 'Beta'), ...]
or{('Alpha', 'Beta'): 'mean', ...}
. Can be specified as'all'
to compute it to any absorbing state inkeys
, which is more efficient than listing all absorbing states.It might be beneficial to disable the progress bar as
show_progress_bar=False
, because many linear systems are being solved.n_jobs (
Optional
[int
]) – Number of parallel jobs to use when using an iterative solver. Whenuse_petsc=True
or for quickly-solvable problems, we recommend higher number (>=8) of jobs in order to fully saturate the cores.backend (
str
) – Which backend to use for multiprocessing. Seejoblib.Parallel
for valid options.show_progress_bar (
bool
) – Whether to show progress bar when the solver isn’t a direct one.tol (
float
) – Convergence tolerance for the iterative solver. The default is fine for most cases, only consider decreasing this for severely ill-conditioned matrices.preconditioner (
Optional
[str
]) – Preconditioner to use, only available whenuse_petsc=True
. For available values, see here or the values of petsc4py.PETSc.PC.Type. We recommended ‘ilu’ preconditioner for badly conditioned problems.
- Returns
Nothing, but updates the following fields:
absorption_probabilities
- probabilities of being absorbed into the terminal states.lineage_absorption_times
- mean times until absorption to subset absorbing states and optionally their variances saved as'{lineage} mean'
and'{lineage} var'
, respectively, for each subset of absorbing states specified intime_to_absorption
.
- Return type
- compute_lineage_priming(method='kl_divergence', early_cells=None)[source]¶
Compute the degree of lineage priming.
This method computes how naive vs. committed each individual cell is. It returns a score where 0 stands for naive and 1 stands for committed.
- Parameters
method (
Literal
[‘kl_divergence’, ‘entropy’]) –The method used to compute the degree of lineage priming. Valid options are:
’kl_divergence’: as in [Velten et al., 2017], computes KL-divergence between the fate probabilities of a cell and the average fate probabilities. Computation of average fate probabilities can be restricted to a set of user-defined
early_cells
.’entropy’: as in [Setty et al., 2019], computes entropy over a cell’s fate probabilities.
early_cells (
Union
[Mapping
[str
,Sequence
[str
]],Sequence
[str
],None
]) – Cell ids or a mask marking early cells. If None, use all cells. Only used whenmethod='kl_divergence'
. Cell ids or a mask marking early cells. If None, use all cells. Only used whenmethod='kl_divergence'
. If adict
, the key species a cluster key inanndata.AnnData.obs
and the values specify cluster labels containing early cells.
- Returns
- Return type
The priming degree.
- compute_lineage_drivers(lineages=None, method='fischer', cluster_key=None, clusters=None, layer='X', use_raw=False, confidence_level=0.95, n_perms=1000, seed=None, return_drivers=True, **kwargs)[source]¶
Compute driver genes per lineage.
Correlates gene expression with lineage probabilities, for a given lineage and set of clusters. Often, it makes sense to restrict this to a set of clusters which are relevant for the specified lineages.
- Parameters
lineages (
Union
[str
,Sequence
,None
]) – Either a set of lineage names fromabsorption_probabilities
.names or None, in which case all lineages are considered.method (
str
) –Mode to use when calculating p-values and confidence intervals. Valid options are:
’fischer’ - use Fischer transformation [Fisher, 1921].
’perm_test’ - use permutation test.
cluster_key (
Optional
[str
]) – Key fromadata
.obs
to obtain cluster annotations. These are considered forclusters
.clusters (
Union
[str
,Sequence
,None
]) – Restrict the correlations to these clusters.layer (
str
) – Key fromadata
.layers
.use_raw (
bool
) – Whether or not to useadata
.raw
to correlate gene expression. If using a layer other than.X
, this must be set to False.confidence_level (
float
) – Confidence level for the confidence interval calculation. Must be in [0, 1].n_perms (
int
) – Number of permutations to use whenmethod='perm_test'
.return_drivers (
bool
) – Whether to return the drivers. This also contains the lower and upperconfidence_level
confidence interval bounds.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.
- Return type
Optional
[DataFrame
]- Returns
Dataframe of shape
(n_genes, n_lineages * 5)
containing the following columns, 1 for each lineage –{lineage} corr
- correlation between the gene expression and absorption probabilities.{lineage} pval
- calculated p-values for double-sided test.{lineage} qval
- corrected p-values using Benjamini-Hochberg method at level 0.05.{lineage} ci low
- lower bound of theconfidence_level
correlation confidence interval.{lineage} ci high
- upper bound of theconfidence_level
correlation confidence interval.
Only if
return_drivers=True
.Otherwise, updates
adata
.var
oradata
.raw.var
, dependinguse_raw
with –'{direction} {lineage} corr'
- the potential lineage drivers.'{direction} {lineage} qval'
- the corrected p-values.
Also updates the following fields –
lineage_drivers
- same as the returned values.
- plot_lineage_drivers(lineage, n_genes=8, ncols=None, use_raw=False, title_fmt='{gene} qval={qval:.4e}', figsize=None, dpi=None, save=None, **kwargs)[source]¶
Plot lineage drivers discovered by
compute_lineage_drivers()
.- Parameters
lineage (
str
) – Lineage for which to plot the driver genes.n_genes (
int
) – Top most correlated genes to plot.use_raw (
bool
) – Whether to look inadata
.raw.var
oradata
.var
.title_fmt (
str
) – Title format. Possible keywords include {gene}, {qval}, {corr} for gene name, q-value and correlation, respectively.figsize (
Optional
[Tuple
[float
,float
]]) – Size of the figure.save (
Union
[str
,Path
,None
]) – Filename where to save the plot.kwargs – Keyword arguments for
scvelo.pl.scatter()
.
- Returns
Nothing, just plots the figure. Optionally saves it based on
save
.- Return type
- plot_lineage_drivers_correlation(lineage_x, lineage_y, color=None, gene_sets=None, gene_sets_colors=None, use_raw=False, cmap='RdYlBu_r', fontsize=12, adjust_text=False, legend_loc='best', figsize=(4, 4), dpi=None, save=None, show=True, **kwargs)[source]¶
Show scatter plot of gene-correlations between two lineages.
Optionally, you can pass a
dict
of gene names that will be annotated in the plot.- Parameters
lineage_x (
str
) – Name of the lineage on the x-axis.lineage_y (
str
) – Name of the lineage on the y-axis.gene_sets (
Optional
[Dict
[str
,Iterable
]]) – Gene sets annotations of the form {‘gene_set_name’: [‘gene_1’, ‘gene_2’], …}.gene_sets_colors (
Optional
[Iterable
]) – List of colors where each entry corresponds to a gene set fromgenes_sets
. If None and keys ingene_sets
correspond to lineage names, use the lineage colors. Otherwise, use default colors.use_raw (
bool
) – Whether to accessadata
.raw.var
oradata
.var
.cmap (
str
) – Colormap to use.fontsize (
int
) – Size of the text when plottinggene_sets
.adjust_text (
bool
) – Whether to automatically adjust text in order to reduce overlap.legend_loc (
Optional
[str
]) – Position of the legend. If None, don’t show the legend. Only used whengene_sets!=None
.figsize (
Optional
[Tuple
[float
,float
]]) – Size of the figure.save (
Union
[str
,Path
,None
]) – Filename where to save the plot.show (
bool
) – If False, returnmatplotlib.pyplot.Axes
.kwargs (
Any
) – Keyword arguments forscanpy.pl.scatter()
.
- Return type
Optional
[Axes
]- Returns
matplotlib.pyplot.Axes
– The axis object ifshow=False
.None – Nothing, just plots the figure. Optionally saves it based on
save
.
Notes
This plot is based on the following notebook by Maren Büttner.
- fit(keys=None, compute_absorption_probabilities=True, **kwargs)[source]¶
Run the pipeline.
- Parameters
- Returns
Nothing, just makes available the following fields:
terminal_states_probabilities
terminal_states
absorption_probabilities
priming_degree
- Return type
Kernel¶
- class cellrank.tl.kernels.Kernel(adata, backward=False, compute_cond_num=False, check_connectivity=False, **kwargs)[source]¶
A base class from which all kernels are derived.
These kernels read from a given AnnData object, usually the KNN graph and additional variables, to compute a weighted, directed graph. Every kernel object has a direction. The kernels defined in the derived classes are not strictly kernels in the mathematical sense because they often only take one input argument - however, they build on other functions which have computed a similarity based on two input arguments. The role of the kernels defined here is to add directionality to these symmetric similarity relations or to transform them.
- Parameters
adata (
anndata.AnnData
) – Annotated data object.backward (
bool
) – Direction of the process.compute_cond_num (
bool
) – Whether to compute condition number of the transition matrix. Note that this might be costly, since it does not use sparse implementation.check_connectivity (
bool
) – Check whether the underlying KNN graph is connected.kwargs (
Any
) – Keyword arguments which can specify key to be read fromadata
object.
- property adata: anndata._core.anndata.AnnData¶
Annotated data object.
- Returns
Annotated data object.
- Return type
- compute_projection(basis='umap', key_added=None, copy=False)¶
Compute a projection of the transition matrix in the embedding.
Projections can only be calculated for kNN based kernels. The projected matrix can be then visualized as:
scvelo.pl.velocity_embedding(adata, vkey='T_fwd', basis='umap')
- Parameters
basis (
str
) – Basis inadata
.obsm
for which to compute the projection.key_added (
Optional
[str
]) – If not None andcopy=False
, save the result toadata
.obsm['{key_added}']
. Otherwise, save the result to ‘T_fwd_{basis}’ or T_bwd_{basis}, depending on the direction.copy (
bool
) – Whether to return the projection or modifyadata
inplace.
- Return type
- Returns
If
copy=True
, the projection array of shape (n_cells, n_components).Otherwise, it modifies
anndata.AnnData.obsm
with a key based onkey_added
.
- abstract compute_transition_matrix(*args, **kwargs)¶
Compute a transition matrix.
- property condition_number: Optional[int]¶
Condition number of the transition matrix.
- abstract copy()¶
Return a copy of itself. Note that the underlying
adata
object is not copied.- Return type
KernelExpression
- property kernels: List[cellrank.tl.kernels._base_kernel.Kernel]¶
Get the kernels of the kernel expression, except for constants.
- property params: Dict[str, Any]¶
Parameters which are used to compute the transition matrix.
- plot_random_walks(n_sims, 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
],Dict
[str
,Union
[str
,Sequence
[str
]]],None
]) – Cells from which to sample the starting points. If None, use all cells. Can be specified as either adict
with a key corresponding to cluster key inanndata.AnnData.obs
and values to clusters or just a sequence of cell ids inanndata.AnnData.obs_names
. For example{'clusters': ['Ngn3 low EP', 'Ngn3 high EP']}
means that starting points for random walks will be samples uniformly from the these clusters.stop_ixs (
Union
[Sequence
[str
],Dict
[str
,Union
[str
,Sequence
[str
]]],None
]) – Cells which when hit, the random walk is terminated. If None, terminate aftermax_iters
. Can be specified as either adict
with a key corresponding to cluster key inanndata.AnnData.obs
and values to clusters or just a sequence of cell ids inanndata.AnnData.obs_names
. For example{'clusters': ['Alpha', 'Beta']}
andsuccesive_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 inanndata.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. Seejoblib.Parallel
for valid options.figsize (
Optional
[Tuple
[float
,float
]]) – Size of the figure.save (
Union
[str
,Path
,None
]) – Filename where to save the plot.kwargs (
Any
) – Keyword arguments forscvelo.pl.scatter()
.
- Return type
- Returns
None – 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(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 compute_flow.cluster_key (
str
) – Key inadata
.obs
where clustering is stored.time_key (
str
) – Key inadata
.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 byclusters
.xticks_step_size (
Optional
[int
]) – Show only every n-th ticks on 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.save (
Union
[str
,Path
,None
]) – Filename where to save the plot.show (
bool
) – If False, returnmatplotlib.pyplot.Axes
.
- Return type
Optional
[Axes
]- Returns
matplotlib.pyplot.Axes
– The axis object ifshow=False
.None – Nothing, just plots the figure. Optionally saves it based on
save
.
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.
- static read(fname)¶
Deserialize self from a file.
- property transition_matrix: Union[numpy.ndarray, scipy.sparse.base.spmatrix]¶
Return row-normalized transition matrix.
If not present, it is computed iff all underlying kernels have been initialized.
- write(fname, ext='pickle')¶
Serialize self to a file.
- write_to_adata(key=None)¶
Write the transition matrix and parameters used for computation to the underlying
adata
object.- Parameters
key (
Optional
[str
]) – Key used when writing transition matrix toadata
. If None, thekey
is set to ‘T_bwd’ ifbackward
is True, else ‘T_fwd’.- Returns
Updates the
adata
with the following fields:.obsp['{key}']
- the transition matrix..uns['{key}_params']
- parameters used for calculation.
- Return type
ExperimentalTime Kernel¶
- class cellrank.tl.kernels.ExperimentalTimeKernel(adata, backward=False, time_key='exp_time', compute_cond_num=False)[source]¶
Kernel base class which computes directed transition probabilities based on experimental time.
Optionally, we apply a density correction as described in [Coifman et al., 2005], where we use the implementation of [Haghverdi et al., 2016].
- Parameters
adata (
anndata.AnnData
) – Annotated data object.backward (
bool
) – Direction of the process.time_key (
str
) – Key inadata
.obs
where experimental time is stored. The experimental time can be of either of a numeric or an ordered categorical type.compute_cond_num (
bool
) – Whether to compute condition number of the transition matrix. Note that this might be costly, since it does not use sparse implementation.
- plot_single_flow(cluster, cluster_key, time_key=None, *args, **kwargs)[source]¶
Visualize outgoing flow from a cluster of cells [Mittnenzweig et al., 2021].
- Parameters
cluster (
str
) – Cluster for which to visualize outgoing compute_flow.cluster_key (
str
) – Key inadata
.obs
where clustering is stored.time_key (
Optional
[str
]) – Key inadata
.obs
where experimental time is stored.clusters – Visualize flow only for these clusters. If None, use all clusters.
time_points – Visualize flow only for these time points. If None, use all time points.
min_flow – Only show flow edges with flow greater than this value. Flow values are always in [0, 1].
remove_empty_clusters – Whether to remove clusters with no incoming flow edges.
ascending – Whether to sort the cluster by ascending or descending incoming flow. If None, use the order as in defined by
clusters
.alpha – Alpha value for cell proportions.
xticks_step_size – Show only every n-th ticks on x-axis. If None, don’t show any ticks.
legend_loc – Position of the legend. If None, do not show the legend.
figsize – Size of the figure.
dpi – Dots per inch.
save – Filename where to save the plot.
show – If False, return
matplotlib.pyplot.Axes
.
- Return type
- Returns
matplotlib.pyplot.Axes
– The axis object ifshow=False
.None – Nothing, just plots the figure. Optionally saves it based on
save
.
- property experimental_time: pandas.core.series.Series¶
Experimental time.
- Return type
Series
- property adata: anndata._core.anndata.AnnData¶
Annotated data object.
- Returns
Annotated data object.
- Return type
- compute_projection(basis='umap', key_added=None, copy=False)¶
Compute a projection of the transition matrix in the embedding.
Projections can only be calculated for kNN based kernels. The projected matrix can be then visualized as:
scvelo.pl.velocity_embedding(adata, vkey='T_fwd', basis='umap')
- Parameters
basis (
str
) – Basis inadata
.obsm
for which to compute the projection.key_added (
Optional
[str
]) – If not None andcopy=False
, save the result toadata
.obsm['{key_added}']
. Otherwise, save the result to ‘T_fwd_{basis}’ or T_bwd_{basis}, depending on the direction.copy (
bool
) – Whether to return the projection or modifyadata
inplace.
- Return type
- Returns
If
copy=True
, the projection array of shape (n_cells, n_components).Otherwise, it modifies
anndata.AnnData.obsm
with a key based onkey_added
.
- abstract compute_transition_matrix(*args, **kwargs)¶
Compute a transition matrix.
- property condition_number: Optional[int]¶
Condition number of the transition matrix.
- property kernels: List[cellrank.tl.kernels._base_kernel.Kernel]¶
Get the kernels of the kernel expression, except for constants.
- property params: Dict[str, Any]¶
Parameters which are used to compute the transition matrix.
- plot_random_walks(n_sims, 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
],Dict
[str
,Union
[str
,Sequence
[str
]]],None
]) – Cells from which to sample the starting points. If None, use all cells. Can be specified as either adict
with a key corresponding to cluster key inanndata.AnnData.obs
and values to clusters or just a sequence of cell ids inanndata.AnnData.obs_names
. For example{'clusters': ['Ngn3 low EP', 'Ngn3 high EP']}
means that starting points for random walks will be samples uniformly from the these clusters.stop_ixs (
Union
[Sequence
[str
],Dict
[str
,Union
[str
,Sequence
[str
]]],None
]) – Cells which when hit, the random walk is terminated. If None, terminate aftermax_iters
. Can be specified as either adict
with a key corresponding to cluster key inanndata.AnnData.obs
and values to clusters or just a sequence of cell ids inanndata.AnnData.obs_names
. For example{'clusters': ['Alpha', 'Beta']}
andsuccesive_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 inanndata.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. Seejoblib.Parallel
for valid options.figsize (
Optional
[Tuple
[float
,float
]]) – Size of the figure.save (
Union
[str
,Path
,None
]) – Filename where to save the plot.kwargs (
Any
) – Keyword arguments forscvelo.pl.scatter()
.
- Return type
- Returns
None – 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
.
- static read(fname)¶
Deserialize self from a file.
- property transition_matrix: Union[numpy.ndarray, scipy.sparse.base.spmatrix]¶
Return row-normalized transition matrix.
If not present, it is computed iff all underlying kernels have been initialized.
- write(fname, ext='pickle')¶
Serialize self to a file.
- write_to_adata(key=None)¶
Write the transition matrix and parameters used for computation to the underlying
adata
object.- Parameters
key (
Optional
[str
]) – Key used when writing transition matrix toadata
. If None, thekey
is set to ‘T_bwd’ ifbackward
is True, else ‘T_fwd’.- Returns
Updates the
adata
with the following fields:.obsp['{key}']
- the transition matrix..uns['{key}_params']
- parameters used for calculation.
- Return type
TransportMap Kernel¶
- class cellrank.tl.kernels.TransportMapKernel(*args, **kwargs)[source]¶
Kernel base class which computes transition matrix based on transport maps for consecutive time pairs.
- property transport_maps: Optional[Dict[Tuple[float, float], anndata._core.anndata.AnnData]]¶
Transport maps for consecutive time pairs.
- property adata: anndata._core.anndata.AnnData¶
Annotated data object.
- Returns
Annotated data object.
- Return type
- compute_projection(basis='umap', key_added=None, copy=False)¶
Compute a projection of the transition matrix in the embedding.
Projections can only be calculated for kNN based kernels. The projected matrix can be then visualized as:
scvelo.pl.velocity_embedding(adata, vkey='T_fwd', basis='umap')
- Parameters
basis (
str
) – Basis inadata
.obsm
for which to compute the projection.key_added (
Optional
[str
]) – If not None andcopy=False
, save the result toadata
.obsm['{key_added}']
. Otherwise, save the result to ‘T_fwd_{basis}’ or T_bwd_{basis}, depending on the direction.copy (
bool
) – Whether to return the projection or modifyadata
inplace.
- Return type
- Returns
If
copy=True
, the projection array of shape (n_cells, n_components).Otherwise, it modifies
anndata.AnnData.obsm
with a key based onkey_added
.
- abstract compute_transition_matrix(*args, **kwargs)¶
Compute a transition matrix.
- property condition_number: Optional[int]¶
Condition number of the transition matrix.
- copy()¶
Return a copy of self.
- Return type
- property experimental_time: pandas.core.series.Series¶
Experimental time.
- Return type
Series
- property kernels: List[cellrank.tl.kernels._base_kernel.Kernel]¶
Get the kernels of the kernel expression, except for constants.
- property params: Dict[str, Any]¶
Parameters which are used to compute the transition matrix.
- plot_random_walks(n_sims, 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
],Dict
[str
,Union
[str
,Sequence
[str
]]],None
]) – Cells from which to sample the starting points. If None, use all cells. Can be specified as either adict
with a key corresponding to cluster key inanndata.AnnData.obs
and values to clusters or just a sequence of cell ids inanndata.AnnData.obs_names
. For example{'clusters': ['Ngn3 low EP', 'Ngn3 high EP']}
means that starting points for random walks will be samples uniformly from the these clusters.stop_ixs (
Union
[Sequence
[str
],Dict
[str
,Union
[str
,Sequence
[str
]]],None
]) – Cells which when hit, the random walk is terminated. If None, terminate aftermax_iters
. Can be specified as either adict
with a key corresponding to cluster key inanndata.AnnData.obs
and values to clusters or just a sequence of cell ids inanndata.AnnData.obs_names
. For example{'clusters': ['Alpha', 'Beta']}
andsuccesive_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 inanndata.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. Seejoblib.Parallel
for valid options.figsize (
Optional
[Tuple
[float
,float
]]) – Size of the figure.save (
Union
[str
,Path
,None
]) – Filename where to save the plot.kwargs (
Any
) – Keyword arguments forscvelo.pl.scatter()
.
- Return type
- Returns
None – 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(cluster, cluster_key, time_key=None, *args, **kwargs)¶
Visualize outgoing flow from a cluster of cells [Mittnenzweig et al., 2021].
- Parameters
cluster (
str
) – Cluster for which to visualize outgoing compute_flow.cluster_key (
str
) – Key inadata
.obs
where clustering is stored.time_key (
Optional
[str
]) – Key inadata
.obs
where experimental time is stored.clusters – Visualize flow only for these clusters. If None, use all clusters.
time_points – Visualize flow only for these time points. If None, use all time points.
min_flow – Only show flow edges with flow greater than this value. Flow values are always in [0, 1].
remove_empty_clusters – Whether to remove clusters with no incoming flow edges.
ascending – Whether to sort the cluster by ascending or descending incoming flow. If None, use the order as in defined by
clusters
.alpha – Alpha value for cell proportions.
xticks_step_size – Show only every n-th ticks on x-axis. If None, don’t show any ticks.
legend_loc – Position of the legend. If None, do not show the legend.
figsize – Size of the figure.
dpi – Dots per inch.
save – Filename where to save the plot.
show – If False, return
matplotlib.pyplot.Axes
.
- Return type
- Returns
matplotlib.pyplot.Axes
– The axis object ifshow=False
.None – Nothing, just plots the figure. Optionally saves it based on
save
.
- static read(fname)¶
Deserialize self from a file.
- property transition_matrix: Union[numpy.ndarray, scipy.sparse.base.spmatrix]¶
Return row-normalized transition matrix.
If not present, it is computed iff all underlying kernels have been initialized.
- write(fname, ext='pickle')¶
Serialize self to a file.
- write_to_adata(key=None)¶
Write the transition matrix and parameters used for computation to the underlying
adata
object.- Parameters
key (
Optional
[str
]) – Key used when writing transition matrix toadata
. If None, thekey
is set to ‘T_bwd’ ifbackward
is True, else ‘T_fwd’.- Returns
Updates the
adata
with the following fields:.obsp['{key}']
- the transition matrix..uns['{key}_params']
- parameters used for calculation.
- Return type
Similarity Scheme¶
- class cellrank.tl.kernels.SimilaritySchemeABC[source]¶
Base class for all similarity schemes.
- abstract __call__(v, D, softmax_scale=1.0)[source]¶
Compute transition probability of a cell to its nearest neighbors using RNA velocity.
- Parameters
v (
ndarray
) – Array of shape(n_genes,)
or(n_neighbors, n_genes)
containing the velocity vector(s). The second case is used for the backward process.D (
ndarray
) – Array of shape(n_neighbors, n_genes)
corresponding to the transcriptomic displacement of the current cell with respect to ist nearest neighbors.softmax_scale (
float
) – Scaling factor for the softmax function.
- Returns
The probability and logits arrays of shape
(n_neighbors,)
.- Return type
Threshold Scheme¶
- class cellrank.tl.kernels.ThresholdSchemeABC[source]¶
Base class for all connectivity biasing schemes.
- abstract __call__(cell_pseudotime, neigh_pseudotime, neigh_conn, **kwargs)[source]¶
Calculate biased connections for a given cell.
- Parameters
- Returns
- Return type
Array of shape
(n_neighbors,)
containing the biased connectivities.
- bias_knn(conn, pseudotime, n_jobs=None, backend='loky', show_progress_bar=True, **kwargs)[source]¶
Bias cell-cell connectivities of a KNN graph.
- Parameters
conn (
csr_matrix
) – Sparse matrix of shape(n_cells, n_cells)
containing the nearest neighbor connectivities.pseudotime (
ndarray
) – Pseudotemporal ordering of cells.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. Seejoblib.Parallel
for valid options.
- Returns
- Return type
The biased connectivities.
BaseModel¶
- class cellrank.ul.models.BaseModel(adata, model)[source]¶
Base class for all model classes.
- Parameters
adata (
anndata.AnnData
) – Annotated data object.model (
Any
) – The underlying model that is used for fitting and prediction.
- property prepared¶
Whether the model is prepared for fitting.
- property adata: cellrank.ul.models._base_model.AnnData¶
Annotated data object.
- Returns
adata – Annotated data object.
- Return type
- property x_all: numpy.ndarray¶
Unfiltered independent variables of shape (n_cells, 1).
- Return type
- property y_all: numpy.ndarray¶
Unfiltered dependent variables of shape (n_cells, 1).
- Return type
- property x: numpy.ndarray¶
Filtered independent variables of shape (n_filtered_cells, 1) used for fitting.
- Return type
- property y: numpy.ndarray¶
Filtered dependent variables of shape (n_filtered_cells, 1) used for fitting.
- Return type
- property w: numpy.ndarray¶
Filtered weights of shape (n_filtered_cells,) used for fitting.
- Return type
- property x_test: numpy.ndarray¶
Independent variables of shape (n_samples, 1) used for prediction.
- Return type
- property x_hat: numpy.ndarray¶
Filtered independent variables used when calculating default confidence interval, usually same as
x
.- Return type
- property y_hat: numpy.ndarray¶
Filtered dependent variables used when calculating default confidence interval, usually same as
y
.- Return type
- property conf_int: numpy.ndarray¶
Array of shape (n_samples, 2) containing the lower and upper bounds of the confidence interval.
- Return type
- prepare(gene, lineage, backward=False, time_range=None, data_key='X', time_key='latent_time', use_raw=False, threshold=None, weight_threshold=(0.01, 0.01), filter_cells=None, n_test_points=200)[source]¶
Prepare the model to be ready for fitting.
- Parameters
gene (
str
) – Gene inadata
.var_names
or inadata
.raw.var_names
.lineage (
Optional
[str
]) – Name of a lineage inadata
.obsm['{lineage_key}']
. If None, all weights will be set to 1.backward (
bool
) – Direction of the process.time_range (
Union
[float
,Tuple
[float
,float
],None
]) –Specify start and end times:
data_key (
str
) – Key inadata
.layers
or ‘X’ foradata
.X
. Ifuse_raw=True
, it’s always set to ‘X’.time_key (
str
) – Key inadata
.obs
where the pseudotime is stored.threshold (
Optional
[float
]) – Consider only cells with weights >threshold
when estimating the test endpoint. If None, use the median of the weights.weight_threshold (
Union
[float
,Tuple
[float
,float
]]) – Set all weights belowweight_threshold
toweight_threshold
if afloat
, or to the second value, if atuple
.filter_cells (
Optional
[float
]) – Filter out all cells with expression values lower than this threshold.n_test_points (
int
) – Number of test points. If None, use the original points based onthreshold
.
- Returns
Nothing, but updates the following fields:
x
- Filtered independent variables of shape (n_filtered_cells, 1) used for fitting.y
- Filtered dependent variables of shape (n_filtered_cells, 1) used for fitting.w
- Filtered weights of shape (n_filtered_cells,) used for fitting.x_all
- Unfiltered independent variables of shape (n_cells, 1).y_all
- Unfiltered dependent variables of shape (n_cells, 1).w_all
- Unfiltered weights of shape (n_cells,).x_test
- Independent variables of shape (n_samples, 1) used for prediction.prepared
- Whether the model is prepared for fitting.
- Return type
- abstract fit(x=None, y=None, w=None, **kwargs)[source]¶
Fit the model.
- Parameters
x (
Optional
[ndarray
]) – Independent variables, array of shape (n_samples, 1). If None, usex
.y (
Optional
[ndarray
]) – Dependent variables, array of shape (n_samples, 1). If None, usey
.w (
Optional
[ndarray
]) – Optional weights ofx
, array of shape (n_samples,). If None, usew
.kwargs – Keyword arguments for underlying
model
’s fitting function.
- Returns
Fits the model and returns self.
- Return type
- abstract predict(x_test=None, key_added='_x_test', **kwargs)[source]¶
Run the prediction.
- Parameters
- Returns
Updates and returns the following:
- Return type
- abstract confidence_interval(x_test=None, **kwargs)[source]¶
Calculate the confidence interval.
Use
default_confidence_interval()
function if underlyingmodel
has not method for confidence interval calculation.- Parameters
x_test (
Optional
[ndarray
]) – Array of shape (n_samples,) used for confidence interval calculation. If None, usex_test
.kwargs – Keyword arguments for underlying
model
’s confidence method or fordefault_confidence_interval()
.
- Returns
Updates the following fields:
conf_int
- Array of shape (n_samples, 2) containing the lower and upper bounds of the confidence interval.
- Return type
- default_confidence_interval(x_test=None, **kwargs)[source]¶
Calculate the confidence interval, if the underlying
model
has no method for it.This formula is taken from [DeSalvo, 1970], eq. 5.
- Parameters
x_test (
Optional
[ndarray
]) – Array of shape (n_samples,) used for confidence interval calculation. If None, usex_test
.kwargs – Keyword arguments for underlying
model
’s confidence method or fordefault_confidence_interval()
.
- Returns
Updates the following fields:
conf_int
- Array of shape (n_samples, 2) containing the lower and upper bounds of the confidence interval.x_hat
- Filtered independent variables used when calculating default confidence interval, usually same asx
.y_hat
- Filtered dependent variables used when calculating default confidence interval, usually same asy
.
- Return type
- plot(figsize=(8, 5), same_plot=False, hide_cells=False, perc=None, abs_prob_cmap=<matplotlib.colors.ListedColormap object>, cell_color=None, lineage_color='black', alpha=0.8, lineage_alpha=0.2, title=None, size=15, lw=2, cbar=True, margins=0.015, xlabel='pseudotime', ylabel='expression', conf_int=True, lineage_probability=False, lineage_probability_conf_int=False, lineage_probability_color=None, obs_legend_loc='best', dpi=None, fig=None, ax=None, return_fig=False, save=None, **kwargs)[source]¶
Plot the smoothed gene expression.
- Parameters
same_plot (
bool
) – Whether to plot all trends in the same plot.hide_cells (
bool
) – Whether to hide the cells.perc (
Optional
[Tuple
[float
,float
]]) – Percentile by which to clip the absorption probabilities.abs_prob_cmap (
ListedColormap
) – Colormap to use when coloring in the absorption probabilities.cell_color (
Optional
[str
]) – Key inanndata.AnnData.obs
oranndata.AnnData.var_names
used for coloring the cells.lineage_color (
str
) – Color for the lineage.alpha (
float
) – Alpha channel for cells.lineage_alpha (
float
) – Alpha channel for lineage confidence intervals.size (
int
) – Size of the points.lw (
float
) – Line width for the smoothed values.cbar (
bool
) – Whether to show colorbar.margins (
float
) – Margins around the plot.xlabel (
str
) – Label on the x-axis.ylabel (
str
) – Label on the y-axis.conf_int (
bool
) – Whether to show the confidence interval.lineage_probability (
bool
) – Whether to show smoothed lineage probability as a dashed line. Note that this will require 1 additional model fit.lineage_probability_conf_int (
Union
[bool
,float
]) – Whether to compute and show smoothed lineage probability confidence interval. Ifself
iscellrank.ul.models.GAMR
, it can also specify the confidence level, the default is 0.95. Only used whenshow_lineage_probability=True
.lineage_probability_color (
Optional
[str
]) – Color to use when plotting the smoothedlineage_probability
. If None, it’s the same aslineage_color
. Only used whenshow_lineage_probability=True
.obs_legend_loc (
Optional
[str
]) – Location of the legend whencell_color
corresponds to a categorical variable.fig (
Optional
[Figure
]) – Figure to use, if None, create a new one.ax (
matplotlib.axes.Axes
) – Ax to use, if None, create a new one.return_fig (
bool
) – If True, return the figure object.save (
Optional
[str
]) – Filename where to save the plot. If None, just shows the plots.kwargs – Keyword arguments for
matplotlib.axes.Axes.legend()
, e.g. to disable the legend, specifyloc=None
. Only available whenshow_lineage_probability=True
.
- Returns
Nothing, just plots the figure. Optionally saves it based on
save
.- Return type
Lineage¶
- class cellrank.tl.Lineage(input_array: numpy.ndarray, *, names: Iterable[str], colors: Optional[Iterable[cellrank.tl._lineage.ColorLike]] = None)[source]¶
Lightweight
numpy.ndarray
wrapper that adds names and colors.- Parameters
input_array – Input array containing lineage probabilities, each lineage being stored in a column.
names – Names of the lineages.
colors – Colors of the lineages.
- property X: numpy.ndarray¶
Convert self to numpy array, losing names and colors.
- Return type
- property T¶
Transpose of self.
- priming_degree(method='kl_divergence', early_cells=None)[source]¶
Compute the degree of lineage priming.
This method computes how naive vs. committed each individual cell is. It returns a score where 0 stands for naive and 1 stands for committed.
- Parameters
method (
Literal
[‘kl_divergence’, ‘entropy’]) –The method used to compute the degree of lineage priming. Valid options are:
’kl_divergence’: as in [Velten et al., 2017], computes KL-divergence between the fate probabilities of a cell and the average fate probabilities. Computation of average fate probabilities can be restricted to a set of user-defined
early_cells
.’entropy’: as in [Setty et al., 2019], computes entropy over a cell’s fate probabilities.
early_cells (
Optional
[ndarray
]) – Cell ids or a mask marking early cells. If None, use all cells. Only used whenmethod='kl_divergence'
.
- Returns
- Return type
The priming degree.