scanpy.Neighbors.compute_eigen

Neighbors.compute_eigen(n_comps=15, sym=None, sort='decrease', random_state=0)

Compute eigen decomposition of transition matrix.

Parameters

n_comps : intint (default: 15)

Number of eigenvalues/vectors to be computed, set n_comps = 0 if you need all eigenvectors.

sym : bool | NoneOptional[bool] (default: None)

Instead of computing the eigendecomposition of the assymetric transition matrix, computed the eigendecomposition of the symmetric Ktilde matrix.

random_state : None | int | RandomStateUnion[None, int, RandomState] (default: 0)

A numpy random seed

Returns

Writes the following attributes.

eigen_valuesndarray

Eigenvalues of transition matrix.

eigen_basisndarray

Matrix of eigenvectors (stored in columns). .eigen_basis is projection of data matrix on right eigenvectors, that is, the projection on the diffusion components. these are simply the components of the right eigenvectors and can directly be used for plotting.