# cellrank.tl.estimators.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]

Clustering and Filtering of Left and Right Eigenvectors based on Markov chains.

This is one of the two main classes of CellRank. We model cellular development as a Markov chain (MC), where each measured cell is represented by a state in the MC. We assume that transition probabilities between these states have already been computed using either the cellrank.tl.kernels.Kernel class directly or the cellrank.tl.transition_matrix() high level function.

The MC is time-homogeneous, i.e. the transition probabilities don’t change over time. Further, it’s discrete, as every state in the MC is given by a measured cell state. The state space is finite, as is the number of measured cells and we consider discrete time-increments.

Parameters

Attributes

 absorption_probabilities Absorption probabilities. adata Annotated data object. 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. recurrent_classes Recurrent classes of the Markov chain. 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_lineage_drivers([lineages, …]) Compute driver genes per lineage. Compute communication classes for the Markov chain. compute_terminal_states([use, percentile, …]) Find approximate recurrent classes of the Markov chain. Return a copy of self, including the underlying adata object. fit(n_lineages[, keys, cluster_key, …]) Run the pipeline, computing the initial or terminal states and optionally the absorption probabilities. plot_absorption_probabilities(data, prop[, …]) Plot discrete states or probabilities in an embedding. Plot eigenvectors in an embedding. plot_lineage_drivers(lineage[, n_genes, use_raw]) Plot lineage drivers discovered by compute_lineage_drivers(). 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. write(fname[, ext]) Serialize self to a file.