scFates.tl.curve

scFates.tl.curve(adata, Nodes=None, use_rep=None, ndims_rep=None, init=None, epg_lambda=0.01, epg_mu=0.1, epg_trimmingradius=inf, epg_extend_leaves=False, epg_verbose=False, device='cpu', plot=False, basis='umap', seed=None, copy=False, **kwargs)

Generate a principal curve.

Learn a curved representation on any space, composed of nodes, approximating the position of the cells on a given space such as gene expression, pca, diffusion maps, … Uses ElpiGraph algorithm.

Parameters:

adata AnnData

Annotated data matrix.

Nodes Optional[int] (default: None)

Number of nodes composing the principial tree, use a range of 10 to 100 for ElPiGraph approach and 100 to 2000 for PPT approach.

use_rep Optional[str] (default: None)

Choose the space to be learned by the principal tree.

ndims_rep Optional[int] (default: None)

Number of dimensions to use for the inference.

epg_lambda Union[float, int, None] (default: 0.01)

Parameter for ElPiGraph, coefficient of ‘stretching’ elasticity [Albergante20].

epg_mu Union[float, int, None] (default: 0.1)

Parameter for ElPiGraph, coefficient of ‘bending’ elasticity [Albergante20].

epg_trimmingradius Optional (default: inf)

Parameter for ElPiGraph, trimming radius for MSE-based data approximation term [Albergante20].

epg_extend_leaves bool (default: False)

Parameter for ElPiGraph, calls elpigraph.ExtendLeaves() after graph learning.

epg_verbose bool (default: False)

show verbose output of epg algorithm

device Literal['cpu', 'gpu'] (default: 'cpu')

Run method on either cpu or on gpu

plot bool (default: False)

Plot the resulting tree.

basis Optional[str] (default: 'umap')

Basis onto which the resulting tree should be projected.

seed Optional[int] (default: None)

A numpy random seed.

copy bool (default: False)

Return a copy instead of writing to adata.

**kwargs

Arguments passsed to elpigraph.computeElasticPrincipalCurve()

Returns:

adata : anndata.AnnData if copy=True it returns or else add fields to adata:

.uns[‘epg’]

dictionnary containing information from elastic principal curve

.obsm[‘X_R’]

soft assignment of cells to principal points

.uns[‘graph’][‘B’]

adjacency matrix of the principal points

.uns[‘graph’][‘F’]

coordinates of principal points in representation space