cellrank.tl.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)

Generalized Perron Cluster Cluster Analysis [GPCCA18].

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 [GPCCA18].

Parameters

• obj (Union[KernelExpression, ~AnnData, spmatrix, ndarray]) – Either a cellrank.tl.kernels.Kernel object, an anndata.AnnData object which stores the transition matrix in .obsp attribute or numpy or scipy array.

• inplace (bool) – Whether to modify adata object inplace or make a copy.

• read_from_adata (bool) – Whether to read available attributes in adata, if present.

• obsp_key (Optional[str]) – Key in obj.obsp when obj is an anndata.AnnData object.

• g2m_key (Optional[str]) – Key in adata .obs. Can be used to detect cell-cycle driven start- or endpoints.

• s_key (Optional[str]) – Key in adata .obs. Can be used to detect cell-cycle driven start- or endpoints.

• write_to_adata (bool) – Whether to write the transition matrix to adata .obsp and the parameters to adata .uns.

• key (Optional[str]) – Key used when writing transition matrix to adata. If None, the key is set to ‘T_bwd’ if backward is True, else ‘T_fwd’. Only used when write_to_adata=True.

Attributes

absorption_probabilities	Absorption probabilities.
adata	Annotated data object.
coarse_T	Coarse-grained transition matrix.
coarse_initial_distribution	Coarse initial distribution.
coarse_stationary_distribution	Coarse stationary distribution.
diff_potential	Differentiation potential.
eigendecomposition	Eigendecomposition.
is_irreducible	Whether the Markov chain is irreducible or not.
issparse	Whether the transition matrix is sparse or not.
kernel	Underlying kernel.
lineage_absorption_times	Lineage absorption times.
lineage_drivers	Lineage drivers.
macrostates	Macrostates.
macrostates_memberships	Macrostates memberships.
recurrent_classes	Recurrent classes of the Markov chain.
schur	Schur vectors.
schur_matrix	Schur matrix.
terminal_states	Terminal states.
terminal_states_probabilities	Terminal states probabilities.
transient_classes	Transient classes of the Markov chain.
transition_matrix	Transition matrix.

Methods

compute_absorption_probabilities([keys, …])	Compute absorption probabilities of a Markov chain.
compute_eigendecomposition([k, which, …])	Compute eigendecomposition of transition matrix.
compute_gdpt([n_components, key_added])	Compute generalized Diffusion pseudotime from [Haghverdi16] using the real Schur decomposition.
compute_lineage_drivers([lineages, method, …])	Compute driver genes per lineage.
compute_macrostates([n_states, n_cells, …])	Compute the macrostates.
compute_partition()	Compute communication classes for the Markov chain.
compute_schur([n_components, …])	Compute the Schur decomposition.
compute_terminal_states([method, n_cells, …])	Automatically select terminal states from macrostates.
copy()	Return a copy of self, including the underlying adata object.
fit([n_lineages, cluster_key, keys, method, …])	Run the pipeline, computing the macrostates, initial or terminal states and optionally the absorption probabilities.
plot_absorption_probabilities(data, prop[, …])	Plot discrete states or probabilities in an embedding.
plot_coarse_T([show_stationary_dist, …])	Plot the coarse-grained transition matrix between macrostates.
plot_eigendecomposition([left])	Plot eigenvectors in an embedding.
plot_lineage_drivers(lineage[, n_genes, use_raw])	Plot lineage drivers discovered by compute_lineage_drivers().
plot_macrostates(data, prop[, discrete, …])	Plot discrete states or probabilities in an embedding.
plot_schur(vectors, prop[, use, abs_value, …])	Plot vectors in an embedding.
plot_schur_matrix([title, cmap, figsize, …])	Plot the Schur matrix.
plot_spectrum([n, real_only, show_eigengap, …])	Plot the top eigenvalues in real or complex plane.
plot_terminal_states(data, prop[, discrete, …])	Plot discrete states or probabilities in an embedding.
read(fname)	Deserialize self from a file.
rename_terminal_states(new_names[, update_adata])	Rename the names of terminal_states.
set_terminal_states(labels[, cluster_key, …])	Set the approximate recurrent classes, if they are known a priori.
set_terminal_states_from_macrostates([…])	Manually select terminal states from macrostates.
write(fname[, ext])	Serialize self to a file.

Compute eigendecomposition

Compute Schur vectors

Compute Schur matrix

Compute macrostates

Compute coarse-grained transition matrix

Compute terminal states using GPCCA

Compute absorption probabilities

Compute potential lineage drivers

Fit estimator

Plot initial states

Plot terminal states

Plot lineages