cellrank.tl.estimators.GPCCA.compute_schur

GPCCA.compute_schur(n_components=10, initial_distribution=None, method='krylov', which='LR', alpha=1.0)

Compute Schur decomposition.

Parameters

• n_components (int) – Number of Schur vectors to compute.

• initial_distribution (Optional[ndarray]) – Input distribution over all cells. If None, uniform distribution is used.

• method (Literal[‘krylov’, ‘brandts’]) –

  Method for calculating the Schur vectors. Valid options are:

  • ’krylov’ - an iterative procedure that computes a partial, sorted Schur decomposition for large, sparse matrices.

  • ’brandts’ - full sorted Schur decomposition of a dense matrix.

  For benefits of each method, see pygpcca.GPCCA.

• which (Literal[‘LR’, ‘LM’]) –

  How to sort the eigenvalues. Valid option are:

  • ’LR’ - the largest real part.

  • ’LM’ - the largest magnitude.

• alpha (float) – Used to compute the eigengap. alpha is the weight given to the deviation of an eigenvalue from one.

Returns

Nothing, just updates the following fields:

• schur_vectors - Real Schur vectors of the transition matrix.

• schur_matrix - Schur matrix.

• eigendecomposition - Eigendecomposition of transition_matrix.