scFates.tl.explore_sigma

scFates.tl.explore_sigma(adata, Nodes, use_rep=None, ndims_rep=None, sigmas=[1000, 100, 10, 1, 0.1, 0.01], nsteps=1, metric='euclidean', seed=None, plot=False, second_round=False, **kwargs)

Explore varisou sigma parameters for best tree fitting. Given that high sigma tend to collapse the principal points into the middle of the whole data (meaning taking in account all the datapoints regardless their locality), it is possible to explore which sigma is best by detecting at which level the tree stops collapsing.

Parameters:

adata

Annotated data matrix.

Nodes

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 default: None

Choose the space to be learned by the principal tree.

ndims_rep default: None

Number of dimensions to use for the inference.

sigmas default: [1000, 100, 10, 1, 0.1, 0.01]

Range of sigma parameters to test.

device

Run method on either cpu or on gpu.

nsteps default: 1

Number of SimplePPT iteration, usually 1 is enough.

metric default: 'euclidean'

Distance metric to use.

seed default: None

A numpy random seed.

plot default: False

Plot the resulting tree.

second_round default: False

Perform a second exploration, on a restricted sigma parameters based on the first estimated sigma.

**kwargs

Arguments passsed to elpigraph.computeElasticPrincipalCircle()

Returns:

sigma : float suggested sigma value