cellrank.tl.estimators.GPCCA.predict

GPCCA.predict(method=TermStatesMethod.STABILITY, n_cells=30, alpha=1, stability_threshold=0.96, n_states=None)

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 of coarse_T.

  • ’stability’ - select states which have a stability >= stability_threshold. The stability is given by the diagonal elements of coarse_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 when method = 'eigengap' or method = 'eigengap_coarse'.

• stability_threshold (float) – Threshold used when method = 'stability'.

• n_states (Optional[int]) – Number of states used when method = 'top_n'.

Return type

None

Returns

Nothing, just updates the following fields:

• terminal_states - Categorical annotation of terminal states.

• terminal_states_memberships - Terminal state membership matrix.

• terminal_states_probabilities - Aggregated probability of cells to be in terminal states.