cellrank.estimators.GPCCA#
- class cellrank.estimators.GPCCA(object, **kwargs)[source]#
Generalized Perron Cluster Cluster Analysis (GPCCA) [Reuter et al., 2019, Reuter et al., 2018] as implemented in pyGPCCA.
This is our main and recommended estimator. Use it to compute macrostates, automatically and semi-automatically classify these as initial, intermediate and terminal states, compute fate probabilities towards macrostates, uncover driver genes, and much more. To compute and classify macrostates, we run the GPCCA algorithm under the hood, which returns a soft assignmend of cells to macrostates, as well as a coarse-grained transition matrix among the set of macrostates [Reuter et al., 2019, Reuter et al., 2018]. This estimator allows you to inject prior knowledge where available to guide the identification of initial, intermediate and terminal states.
To get started with this estimator, we recommend going over the initial and terminal states tutorial.
- Parameters:
object (
Union
[str
,bool
,ndarray
,spmatrix
,AnnData
,KernelExpression
]) –Can be one of the following types:
anndata.AnnData
- annotated data object.scipy.sparse.spmatrix
,numpy.ndarray
- row-normalized transition matrix.cellrank.kernels.KernelExpression
- kernel expression.str
- key inanndata.AnnData.obsp
where the transition matrix is stored.adata
must be provided in this case.bool
- directionality of the transition matrix that will be used to infer its storage location. If None, the directionality will be determined automatically.adata
must be provided in this case.
kwargs (
Any
) – Keyword arguments forcellrank.kernels.PrecomputedKernel
.
Attributes table#
Absorption probabilities. |
|
Mean and variance of the time until absorption. |
|
Annotated data object. |
|
Direction of |
|
Coarse-grained transition matrix. |
|
Coarse-grained initial distribution. |
|
Coarse-grained stationary distribution. |
|
Eigendecomposition of |
|
Categorical annotation of initial states. |
|
Initial states memberships. |
|
Probability to be an initial state. |
|
Underlying kernel expression. |
|
Potential lineage drivers. |
|
Macrostates of the transition matrix. |
|
Macrostate memberships. |
|
Estimator parameters. |
|
Priming degree. |
|
Schur matrix. |
|
Real Schur vectors of the transition matrix. |
|
Shape of the kernel. |
|
Categorical annotation of terminal states. |
|
Terminal states memberships. |
|
Probability to be a terminal state. |
|
Transition matrix of |
Methods table#
|
Compute absorption probabilities. |
|
Compute the mean time to absorption and optionally its variance. |
|
Compute eigendecomposition of |
|
Compute driver genes per lineage. |
|
Compute the degree of lineage priming. |
|
Compute the macrostates. |
|
Compute Schur decomposition. |
|
Return a copy of self. |
|
Prepare self for terminal states prediction. |
|
De-serialize self from |
|
Plot absorption probabilities. |
|
Plot the coarse-grained transition matrix between macrostates. |
|
Plot lineage drivers discovered by |
|
Show scatter plot of gene-correlations between two lineages. |
|
Plot stacked histogram of macrostates over categorical annotations. |
|
Plot macrostates on an embedding or along pseudotime. |
|
Plot the Schur matrix. |
|
Plot the top eigenvalues in real or complex plane. |
|
Alias for |
|
Compute initial states from macrostates using |
|
Automatically select terminal states from macrostates. |
|
De-serialize self from a file. |
|
Rename |
|
Rename |
|
Set |
|
Set |
|
Serialize self to |
|
Serialize self to a file. |
Attributes#
absorption_probabilities#
- GPCCA.absorption_probabilities#
Absorption probabilities.
Informally, given a (finite, discrete) Markov chain with a set of transient states \(T\) and a set of absorbing states \(A\), the absorption probability for cell \(i\) from \(T\) to reach cell \(j\) from \(R\) is the probability that a random walk initialized in \(i\) will reach absorbing state \(j\).
In our context, states correspond to cells, in particular, absorbing states correspond to cells in terminal states.
absorption_times#
- GPCCA.absorption_times#
Mean and variance of the time until absorption.
Related to conditional mean first passage times. Corresponds to the expectation of the time until absorption, depending on initialization, and the variance.
adata#
- GPCCA.adata#
Annotated data object.
backward#
coarse_T#
- GPCCA.coarse_T#
Coarse-grained transition matrix.
coarse_initial_distribution#
- GPCCA.coarse_initial_distribution#
Coarse-grained initial distribution.
coarse_stationary_distribution#
- GPCCA.coarse_stationary_distribution#
Coarse-grained stationary distribution.
eigendecomposition#
- GPCCA.eigendecomposition#
Eigendecomposition of
transition_matrix
.For non-symmetric real matrices, left and right eigenvectors will in general be different and complex. We compute both left and right eigenvectors.
- Returns:
A dictionary with the following keys:
’D’ - the eigenvalues.
’eigengap’ - the eigengap.
’params’ - parameters used for the computation.
’V_l’ - left eigenvectors (optional).
’V_r’ - right eigenvectors (optional).
’stationary_dist’ - stationary distribution of
transition_matrix
, if present.
initial_states#
- GPCCA.initial_states#
Categorical annotation of initial states.
By default, all transient cells will be labeled as NaN.
initial_states_memberships#
- GPCCA.initial_states_memberships#
Initial states memberships.
Soft assignment of cells to initial states.
initial_states_probabilities#
- GPCCA.initial_states_probabilities#
Probability to be an initial state.
kernel#
- GPCCA.kernel#
Underlying kernel expression.
lineage_drivers#
- GPCCA.lineage_drivers#
Potential lineage drivers.
Computes Pearson correlation of each gene with fate probabilities for every terminal state. High Pearson correlation indicates potential lineage drivers. Also computes p-values and confidence intervals.
- Returns:
Dataframe of shape
(n_genes, n_lineages * 5)
containing the following columns, one 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.
macrostates#
- GPCCA.macrostates#
Macrostates of the transition matrix.
macrostates_memberships#
- GPCCA.macrostates_memberships#
Macrostate memberships.
Soft assignment of microstates (cells) to macrostates.
params#
- GPCCA.params#
Estimator parameters.
priming_degree#
- GPCCA.priming_degree#
Priming degree.
Given a cell \(i\) and a set of terminal states, this quantifies how committed vs. naive cell \(i\) is, i.e. its degree of pluripotency. Low values correspond to naive cells (high degree of pluripotency), high values correspond to committed cells (low degree of pluripotency).
schur_matrix#
- GPCCA.schur_matrix#
Schur matrix.
The real Schur decomposition is a generalization of the Eigendecomposition and can be computed for any real-valued, square matrix \(A\). It is given by \(A = Q R Q^T\), where \(Q\) contains the real Schur vectors and \(R\) is the Schur matrix. \(Q\) is orthogonal and \(R\) is quasi-upper triangular with 1x1 and 2x2 blocks on the diagonal.
If PETSc and SLEPc are installed, only the leading Schur vectors are computed.
schur_vectors#
- GPCCA.schur_vectors#
Real Schur vectors of the transition matrix.
The real Schur decomposition is a generalization of the Eigendecomposition and can be computed for any real-valued, square matrix \(A\). It is given by \(A = Q R Q^T\), where \(Q\) contains the real Schur vectors and \(R\) is the Schur matrix. \(Q\) is orthogonal and \(R\) is quasi-upper triangular with 1x1 and 2x2 blocks on the diagonal.
If PETSc and SLEPc are installed, only the leading Schur vectors are computed.
shape#
- GPCCA.shape#
Shape of the kernel.
terminal_states#
- GPCCA.terminal_states#
Categorical annotation of terminal states.
By default, all transient cells will be labeled as NaN.
terminal_states_memberships#
- GPCCA.terminal_states_memberships#
Terminal states memberships.
Soft assignment of cells to terminal states.
terminal_states_probabilities#
- GPCCA.terminal_states_probabilities#
Probability to be a terminal state.
transition_matrix#
Methods#
compute_absorption_probabilities#
- GPCCA.compute_absorption_probabilities(keys=None, solver='gmres', use_petsc=True, n_jobs=None, backend='loky', show_progress_bar=True, tol=1e-06, preconditioner=None)#
Compute absorption probabilities.
For each cell, this computes the probability of being absorbed in any of the
terminal_states
. In particular, this corresponds to the probability that a random walk initialized in transient cell \(i\) will reach any cell from a fixed transient state before reaching a cell from any other transient state.- Parameters:
keys (
Optional
[Sequence
[str
]]) – Terminal states for which to compute the absorption probabilities. If None, use all states defined interminal_states
.solver (
Union
[str
,Literal
['direct'
,'gmres'
,'lgmres'
,'bicgstab'
,'gcrotmk'
]]) –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()
.n_jobs (
Optional
[int
]) – Number of parallel jobs to use when using an iterative solver.backend (
Literal
['loky'
,'multiprocessing'
,'threading'
]) – Which backend to use for multiprocessing. Seejoblib.Parallel
for valid options.show_progress_bar (
bool
) – Whether to show progress bar. Only used whensolver != 'direct'
.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 valid options, see here. We recommend the ‘ilu’ preconditioner for badly conditioned problems.
- Return type:
- Returns:
: Nothing, just updates the following field:
absorption_probabilities
- Absorption probabilities.
compute_absorption_times#
- GPCCA.compute_absorption_times(keys=None, calculate_variance=False, solver='gmres', use_petsc=True, n_jobs=None, backend='loky', show_progress_bar=None, tol=1e-06, preconditioner=None)#
Compute the mean time to absorption and optionally its variance.
- Parameters:
keys (
Optional
[Sequence
[str
]]) – Terminal states for which to compute the absorption probabilities. If None, use all states defined interminal_states
.calculate_variance (
bool
) – Whether to calculate the variance.solver (
Union
[str
,Literal
['direct'
,'gmres'
,'lgmres'
,'bicgstab'
,'gcrotmk'
]]) –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()
.n_jobs (
Optional
[int
]) – Number of parallel jobs to use when using an iterative solver.backend (
Literal
['loky'
,'multiprocessing'
,'threading'
]) – Which backend to use for multiprocessing. Seejoblib.Parallel
for valid options.show_progress_bar (
Optional
[bool
]) – Whether to show progress bar. Only used whensolver != 'direct'
.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 valid options, see here. We recommend the ‘ilu’ preconditioner for badly conditioned problems.
- Return type:
- Returns:
: Nothing, just updates the following field:
absorption_times
- Mean and variance of the time until absorption.
compute_eigendecomposition#
- GPCCA.compute_eigendecomposition(k=20, which='LR', alpha=1.0, 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 eigenvectors or eigenvalues to compute.which (
Literal
['LR'
,'LM'
]) –How to sort the eigenvalues. Valid option are:
’LR’ - the largest real part.
’LM’ - the 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
) – Whether to compute only eigenvalues.
- Return type:
EigenMixin
- Returns:
: Self and updates the following field:
eigendecomposition
- Eigendecomposition oftransition_matrix
.
compute_lineage_drivers#
- GPCCA.compute_lineage_drivers(lineages=None, method=TestMethod.FISHER, cluster_key=None, clusters=None, layer=None, use_raw=False, confidence_level=0.95, n_perms=1000, seed=None, **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
]) – Lineage names fromabsorption_probabilities
. If None, use all lineages.method (
Literal
['fisher'
,'perm_test'
]) –Mode to use when calculating p-values and confidence intervals. Valid options are:
’fisher’ - use Fisher transformation [Fisher, 1921].
’perm_test’ - use permutation test.
cluster_key (
Optional
[str
]) – Key fromanndata.AnnData.obs
to obtain cluster annotations. These are considered forclusters
.clusters (
Union
[str
,Sequence
,None
]) – Restrict the correlations to these clusters.layer (
Optional
[str
]) – Key fromanndata.AnnData.layers
from which to get the expression. If None or ‘X’, useanndata.AnnData.X
.use_raw (
bool
) – Whether or not to useanndata.AnnData.raw
to correlate gene expression.confidence_level (
float
) – Confidence level for the confidence interval calculation. Must be in interval [0, 1].n_perms (
int
) – Number of permutations to use whenmethod = 'perm_test'
.seed (
Optional
[int
]) – Random seed whenmethod = 'perm_test'
.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:
- Returns:
: Dataframe of shape
(n_genes, n_lineages * 5)
containing the following columns, one 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.
Also updates the following field:
lineage_drivers
- the samepandas.DataFrame
as described above.
compute_lineage_priming#
- GPCCA.compute_lineage_priming(method='kl_divergence', early_cells=None)#
Compute the degree of lineage priming.
It returns a score in [0, 1] 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'
. If adict
, the key specifies a cluster key inanndata.AnnData.obs
and the values specify cluster labels containing early cells.
- Return type:
- Returns:
: The priming degree.
Also updates the following field:
priming_degree
- Priming degree.
compute_macrostates#
- GPCCA.compute_macrostates(n_states=None, n_cells=30, cluster_key=None, **kwargs)[source]#
Compute the macrostates.
- Parameters:
n_states (
Union
[int
,Sequence
[int
],None
]) – Number of macrostates to compute. If aSequence
, use the minChi criterion [Reuter et al., 2018]. If None, use the eigengap heuristic.n_cells (
Optional
[int
]) – Number of most likely cells from each macrostate to select.cluster_key (
Optional
[str
]) – If a key to cluster labels is given, names and colors of the states will be associated with the clusters.kwargs (
Any
) – Keyword arguments forcompute_schur()
.
- Return type:
- Returns:
: Returns self and updates the following fields:
macrostates
- Macrostates of the transition matrix.macrostates_memberships
- Macrostate memberships.coarse_T
- Coarse-grained transition matrix.coarse_initial_distribution
- Coarse-grained initial distribution.coarse_stationary_distribution
- Coarse-grained stationary distribution.schur_vectors
- Real Schur vectors of the transition matrix.schur_matrix
- Schur matrix.eigendecomposition
- Eigendecomposition oftransition_matrix
.
compute_schur#
- GPCCA.compute_schur(n_components=20, initial_distribution=None, method='krylov', which='LR', alpha=1.0, verbose=None)#
Compute Schur decomposition.
- Parameters:
n_components (
int
) – Number of Schur vectors to compute.initial_distribution (
Optional
[ndarray
]) – Input distribution over all cells. If None, uniform distribution is used.method (
Literal
['krylov'
,'brandts'
]) –Method for calculating the Schur vectors. Valid options are:
’krylov’ - an iterative procedure that computes a partial, sorted Schur decomposition for large, sparse matrices.
’brandts’ - full sorted Schur decomposition of a dense matrix.
For benefits of each method, see
pygpcca.GPCCA
.which (
Literal
['LR'
,'LM'
]) –How to sort the eigenvalues. Valid option are:
’LR’ - the largest real part.
’LM’ - the largest magnitude.
alpha (
float
) – Used to compute the eigengap.alpha
is the weight given to the deviation of an eigenvalue from one.verbose (
Optional
[bool
]) – Whether to print extra information when computing the Schur decomposition. If None, it’s disabled whenmethod = 'krylov'
.
- Return type:
SchurMixin
- Returns:
: Self and just updates the following fields:
schur_vectors
- Real Schur vectors of the transition matrix.schur_matrix
- Schur matrix.eigendecomposition
- Eigendecomposition oftransition_matrix
.
copy#
fit#
- GPCCA.fit(n_states=None, n_cells=30, cluster_key=None, **kwargs)[source]#
Prepare self for terminal states prediction.
- Parameters:
n_states (
Union
[int
,Sequence
[int
],None
]) – Number of macrostates to compute. If aSequence
, use the minChi criterion [Reuter et al., 2018]. If None, use the eigengap heuristic.n_cells (
Optional
[int
]) – Number of most likely cells from each macrostate to select.cluster_key (
Optional
[str
]) – If a key to cluster labels is given, names and colors of the states will be associated with the clusters.kwargs (
Any
) – Keyword arguments forcompute_schur()
.
- Return type:
- Returns:
: Returns self and updates the following fields:
macrostates
- Macrostates of the transition matrix.macrostates_memberships
- Macrostate memberships.coarse_T
- Coarse-grained transition matrix.coarse_initial_distribution
- Coarse-grained initial distribution.coarse_stationary_distribution
- Coarse-grained stationary distribution.schur_vectors
- Real Schur vectors of the transition matrix.schur_matrix
- Schur matrix.eigendecomposition
- Eigendecomposition oftransition_matrix
.
from_adata#
- classmethod GPCCA.from_adata(adata, obsp_key)#
De-serialize self from
anndata.AnnData
.- Parameters:
adata (
anndata.AnnData
) – Annotated data object.obsp_key (
str
) – Key inanndata.AnnData.obsp
where the transition matrix is stored.
- Return type:
BaseEstimator
- Returns:
: The de-serialized object.
plot_absorption_probabilities#
- GPCCA.plot_absorption_probabilities(states=None, color=None, mode=PlotMode.EMBEDDING, time_key='latent_time', same_plot=True, title=None, cmap='viridis', **kwargs)#
Plot absorption probabilities.
- Parameters:
states (
Union
[str
,Sequence
[str
],None
]) – Subset of the macrostates to show. IfNone
, plot all macrostates.color (
Optional
[str
]) – Key inanndata.AnnData.obs
oranndata.AnnData.var
used to color the observations.time_key (
str
) – Key inanndata.AnnData.obs
where pseudotime is stored. Only used whenmode = 'time'
.title (
Union
[str
,Sequence
[str
],None
]) – Title of the plot.same_plot (
bool
) – Whether to plot the data on the same plot or not. Only use whenmode = 'embedding'
. If True anddiscrete = False
,color
is ignored.cmap (
str
) – Colormap for continuous annotations.kwargs (
Any
) – Keyword arguments forscvelo.pl.scatter()
.
- Return type:
- Returns:
: Nothing, just plots the figure. Optionally saves it based on
save
.
plot_coarse_T#
- GPCCA.plot_coarse_T(show_stationary_dist=True, show_initial_dist=False, order='stability', 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 showcoarse_stationary_distribution
, if present.show_initial_dist (
bool
) – Whether to showcoarse_initial_distribution
.order (
Optional
[Literal
['stability'
,'incoming'
,'outgoing'
,'stat_dist'
]]) –How to order the coarse-grained transition matrix. Valid options are:
’stability’ - order by the values on the diagonal.
’incoming’ - order by the incoming mass, excluding the diagonal.
’outgoing’ - order by the outgoing mass, excluding the diagonal.
’stat_dist’ - order by coarse stationary distribution. If not present, use ‘stability’.
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 (
Any
) – Keyword arguments formatplotlib.pyplot.imshow()
.
- Return type:
- Returns:
: Nothing, just plots the figure. Optionally saves it based on
save
.
plot_lineage_drivers#
- GPCCA.plot_lineage_drivers(lineage, n_genes=8, use_raw=False, ascending=False, ncols=None, 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 access inanndata.AnnData.raw
or not.ascending (
bool
) – Whether to sort the genes in ascending order.title_fmt (
str
) – Title format. Can include {gene}, {pval}, {qval} or {corr}, which will be substituted with the actual values.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:
: Nothing, just plots the figure. Optionally saves it based on
save
.
plot_lineage_drivers_correlation#
- GPCCA.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.color (
Optional
[str
]) – Key inanndata.AnnData.var
oranndata.AnnData.varm
, preferring for the former.gene_sets (
Optional
[Dict
[str
,Sequence
[str
]]]) – Gene sets annotations of the form {‘gene_set_name’: [‘gene_1’, ‘gene_2’], …}.gene_sets_colors (
Optional
[Sequence
[str
]]) – 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 accessanndata.AnnData.raw
or not.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:
- Returns:
: The axes object, if
show = False
. Nothing, just plots the figure. Optionally saves it based onsave
.
Notes
This plot is based on the following notebook by Maren Büttner.
plot_macrostate_composition#
- GPCCA.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 fromanndata.AnnData.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.
- Return type:
- Returns:
: The axes object, if
show = False
. Nothing, just plots the figure. Optionally saves it based onsave
.
plot_macrostates#
- GPCCA.plot_macrostates(which, states=None, color=None, discrete=True, mode=PlotMode.EMBEDDING, time_key='latent_time', same_plot=True, title=None, cmap='viridis', **kwargs)#
Plot macrostates on an embedding or along pseudotime.
- Parameters:
which (
Literal
['all'
,'initial'
,'terminal'
]) –Which macrostates to plot. Valid options are:
'all'
- plot all macrostates.'initial'
- plot macrostates marked asinitial_states
.'terminal'
- plot macrostates marked asterminal_states
.
states (
Union
[str
,Sequence
[str
],None
]) – Subset of the macrostates to show. If obj:None, plot all macrostates.color (
Optional
[str
]) – Key inanndata.AnnData.obs
oranndata.AnnData.var
used to color the observations.discrete (
bool
) – Whether to plot the data as continuous or discrete observations. If the data cannot be plotted as continuous observations, it will be plotted as discrete.time_key (
str
) – Key inanndata.AnnData.obs
where pseudotime is stored. Only used whenmode = 'time'
.title (
Union
[str
,Sequence
[str
],None
]) – Title of the plot.same_plot (
bool
) – Whether to plot the data on the same plot or not. Only use whenmode = 'embedding'
. If True anddiscrete = False
,color
is ignored.cmap (
str
) – Colormap for continuous annotations.kwargs (
Any
) – Keyword arguments forscvelo.pl.scatter()
.
- Return type:
- Returns:
: Nothing, just plots the figure. Optionally saves it based on
save
.
plot_schur_matrix#
- GPCCA.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
.
plot_spectrum#
- GPCCA.plot_spectrum(n=None, real_only=None, show_eigengap=True, show_all_xticks=True, legend_loc=None, title=None, marker='.', figsize=(5, 5), dpi=100, save=None, **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 (
Optional
[bool
]) – Whether to plot only the real part of the spectrum. If None, plot real spectrum if no complex eigenvalues are present.show_eigengap (
bool
) – Whenreal_only = True
, this determines whether to show the inferred eigengap as a dotted line.show_all_xticks (
bool
) – Whenreal_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.marker (
str
) – Marker symbol used, valid options can be found inmatplotlib.markers
.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.kwargs (
Any
) – Keyword arguments formatplotlib.pyplot.scatter()
.
- Return type:
- Returns:
: Nothing, just plots the figure. Optionally saves it based on
save
.
predict#
- GPCCA.predict(*args, **kwargs)[source]#
Alias for
predict_terminal_states()
.- Return type:
predict_initial_states#
- GPCCA.predict_initial_states(n_states=1, n_cells=30, allow_overlap=False)[source]#
Compute initial states from macrostates using
coarse_stationary_distribution
.- Parameters:
- Return type:
- Returns:
: Returns self and updates the following fields:
initial_states
- Categorical annotation of initial states.initial_states_probabilities
- Probability to be an initial state.initial_states_memberships
- Initial states memberships.
predict_terminal_states#
- GPCCA.predict_terminal_states(method=TermStatesMethod.STABILITY, n_cells=30, alpha=1, stability_threshold=0.96, n_states=None, allow_overlap=False)[source]#
Automatically select terminal states from macrostates.
- Parameters:
method (
Literal
['stability'
,'top_n'
,'eigengap'
,'eigengap_coarse'
]) –How to select the terminal states. Valid option are:
’eigengap’ - select the number of states based on the eigengap of
transition_matrix
.’eigengap_coarse’ - select the number of states based on the eigengap of the diagonal of
coarse_T
.’top_n’ - select top
n_states
based on the probability of the diagonal ofcoarse_T
.’stability’ - select states which have a stability >=
stability_threshold
. The stability is given by the diagonal elements ofcoarse_T
.
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. Only used whenmethod = 'eigengap'
ormethod = 'eigengap_coarse'
.stability_threshold (
float
) – Threshold used whenmethod = 'stability'
.n_states (
Optional
[int
]) – Number of states used whenmethod = 'top_n'
.allow_overlap (
bool
) – Whether to allow overlapping names between initial and terminal states.
- Return type:
- Returns:
: Returns self and updates the following fields:
terminal_states
- Categorical annotation of terminal states.terminal_states_probabilities
- Probability to be a terminal state.terminal_states_memberships
- Terminal states memberships.
read#
- static GPCCA.read(fname, adata=None, copy=False)#
De-serialize self from a file.
- Parameters:
fname (
Union
[str
,Path
]) – Filename from which to read the object.adata (
Optional
[AnnData
]) –anndata.AnnData
object to assign to the saved object. Only used when the saved object hasadata
and it was saved without it.copy (
bool
) – Whether to copyadata
before assigning it or not. Ifadata
is a view, it is always copied.
- Return type:
IOMixin
- Returns:
: The de-serialized object.
rename_initial_states#
- GPCCA.rename_initial_states(old_new)#
Rename
initial_states
.- Parameters:
old_new (
Dict
[str
,str
]) – Dictionary that maps old names to unique new names.- Return type:
TermStatesEstimator
- Returns:
: Returns self and updates the following field:
initial_states
- Categorical annotation of initial states.
rename_terminal_states#
- GPCCA.rename_terminal_states(old_new)#
Rename
terminal_states
.- Parameters:
old_new (
Dict
[str
,str
]) – Dictionary that maps old names to unique new names.- Return type:
TermStatesEstimator
- Returns:
: Returns self and updates the following field:
terminal_states
- Categorical annotation of terminal states.
set_initial_states#
- GPCCA.set_initial_states(states=None, n_cells=30, allow_overlap=False, cluster_key=None, **kwargs)[source]#
Set
initial_states
.- Parameters:
states (
Union
[str
,Sequence
[str
],Dict
[str
,Sequence
[str
]],Series
,None
]) –Which states to select. Valid options are:
str
,Sequence
- subset ofmacrostates
. Multiple states can be combined using','
, such as['Alpha, Beta', 'Epsilon']
.dict
- keys correspond to initial states and values to cell IDs inobs_names
.Series
- categorical series where each category corresponds to a macrostate. NaN values mark cells that should not be marked asinitial_states
.None
- select allmacrostates
.
n_cells (
int
) – Number of most likely cells from each macrostate to select.allow_overlap (
bool
) – Whether to allow overlapping names between initial and terminal states.cluster_key (
Optional
[str
]) – Key inanndata.AnnData.obs
to associate names and colors withinitial_states
. Each state will be given the name and color corresponding to the cluster it mostly overlaps with. Only used whenstates
is adict
orSeries
.kwargs (
Any
) – Additional keyword arguments.
- Return type:
- Returns:
: Returns self and updates the following fields:
initial_states
- Categorical annotation of initial states.initial_states_probabilities
- Probability to be an initial state.initial_states_memberships
- Initial states memberships.
set_terminal_states#
- GPCCA.set_terminal_states(states=None, n_cells=30, allow_overlap=False, cluster_key=None, **kwargs)[source]#
Set
terminal_states
.- Parameters:
states (
Union
[str
,Sequence
[str
],Dict
[str
,Sequence
[str
]],Series
,None
]) –Which states to select. Valid options are:
str
,Sequence
- subset ofmacrostates
. Multiple states can be combined using','
, such as['Alpha, Beta', 'Epsilon']
.dict
- keys correspond to terminal states and values to cell IDs inobs_names
.Series
- categorical series where each category corresponds to a macrostate. NaN values mark cells that should not be marked asterminal_states
.None
- select allmacrostates
.
n_cells (
int
) – Number of most likely cells from each macrostate to select.allow_overlap (
bool
) – Whether to allow overlapping names between initial and terminal states.cluster_key (
Optional
[str
]) – Key inanndata.AnnData.obs
to associate names and colors withterminal_states
. Each state will be given the name and color corresponding to the cluster it mostly overlaps with. Only used whenstates
is adict
orSeries
.kwargs (
Any
) – Additional keyword arguments.
- Return type:
- Returns:
: Returns self and updates the following fields:
terminal_states
- Categorical annotation of terminal states.terminal_states_probabilities
- Probability to be a terminal state.terminal_states_memberships
- Terminal states memberships.
to_adata#
- GPCCA.to_adata(keep=('X', 'raw'), *, copy=True)#
Serialize self to
anndata.Anndata
.- Parameters:
keep (
Union
[Literal
['all'
],Sequence
[Literal
['X'
,'raw'
,'layers'
,'obs'
,'var'
,'obsm'
,'varm'
,'obsp'
,'varp'
,'uns'
]]]) –Which attributes to keep from the underlying
adata
. Valid options are:’all’ - keep all attributes specified in the signature.
typing.Sequence
- keep only subset of these attributes.dict
- the keys correspond the attribute names and values to a subset of keys which to keep from this attribute. If the values are specified either as True or ‘all’, everything from this attribute will be kept.
copy (
Union
[bool
,Sequence
[Literal
['X'
,'raw'
,'layers'
,'obs'
,'var'
,'obsm'
,'varm'
,'obsp'
,'varp'
,'uns'
]]]) – Whether to copy the data. Can be specified on per-attribute basis. Useful for attributes that are array-like.
- Return type:
- Returns:
: Annotated data object.