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)[source]

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

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 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. 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 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. Manually select terminal states from macrostates. write(fname[, ext]) Serialize self to a file.