cellrank.tl.estimators.CFLARE

class cellrank.tl.estimators.CFLARE(obj, obsp_key=None, **kwargs)[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

Attributes

absorption_probabilities

Absorption probabilities.

absorption_times

Mean and variance of the time until absorption.

adata

Annotated data object.

backward

Direction of kernel.

eigendecomposition

Eigendecomposition of transition_matrix.

kernel

Underlying kernel expression.

lineage_drivers

Potential lineage drivers.

params

Estimator parameters.

priming_degree

Priming degree.

shape

Shape of the kernel.

terminal_states

Categorical annotation of terminal states.

terminal_states_probabilities

Aggregated probability of cells to be in terminal states.

transition_matrix

Transition matrix of kernel.

Methods

compute_absorption_probabilities([keys, ...])

Compute absorption probabilities.

compute_eigendecomposition([k, which, ...])

Compute eigendecomposition of transition_matrix.

compute_lineage_drivers([lineages, method, ...])

Compute driver genes per lineage.

compute_lineage_priming([method, early_cells])

Compute the degree of lineage priming.

compute_terminal_states(*args, **kwargs)

Compute terminal states of the process.

copy(*[, deep])

Return a copy of self.

fit([k])

Prepare self for terminal states prediction.

from_adata(adata, obsp_key)

Deserialize self from anndata.AnnData.

plot_absorption_probabilities([states, ...])

Plot continuous or categorical observations in an embedding or along pseudotime.

plot_lineage_drivers(lineage[, n_genes, ...])

Plot lineage drivers discovered by compute_lineage_drivers().

plot_lineage_drivers_correlation(lineage_x, ...)

Show scatter plot of gene-correlations between two lineages.

plot_spectrum([n, real_only, show_eigengap, ...])

Plot the top eigenvalues in real or complex plane.

plot_terminal_states([states, color, ...])

Plot continuous or categorical observations in an embedding or along pseudotime.

predict([use, percentile, method, ...])

Find approximate recurrent classes of the Markov chain.

read(fname[, adata, copy])

Deserialize self from a file.

rename_terminal_states(new_names)

Rename categories in terminal_states.

set_terminal_states(labels[, cluster_key, ...])

Manually define terminal states.

to_adata([keep, copy])

Serialize self to anndata.Anndata.

write(fname[, write_adata, ext])

Serialize self to a file.