scFates.tl.activation

scFates.tl.activation(adata, root_milestone, milestones, deriv_cut=0.15, pseudotime_offset=0, nwin=20, steps=5, n_map=1, copy=False, n_jobs=- 1, layer=None)

Identify pseudotime of activation of branch-specififc features.

This aims in classifying the genes according to their their activation timing compared to the pseudotime of the bifurcation. Any feature activated before the bifurcation is considered as ‘early’, others are considered ‘late’.

This is done by separating the path into bins of equal pseudotime, and then identifying successive bins having a change in expression higher than the parameter deriv_cut.

Parameters

adata : AnnData

Annotated data matrix.

root_milestone

tip defining progenitor branch.

milestones

tips defining the progenies branches.

deriv_cut : float (default: 0.15)

a first passage of derivative at this cutoff (in proportion to the full dynamic range of the fitted feature) is considered as activation timing

pseudotime_offset : float (default: 0)

consider a feature as early if it gets activated before: pseudotime at bifurcation-pseudotime_offset.

nwin : int (default: 20)

windows of pseudotime to use for assessing activation timimg

steps : int (default: 5)

number of steps dividing a window for that will slide along the pseudotime

n_map : int (default: 1)

number of cell mappings from which to do the test.

n_jobs

number of cpu processes used to perform the test.

copy : bool (default: False)

Return a copy instead of writing to adata.

Returns

adata – if copy=True it returns or else add fields to adata:

.uns[‘root_milestone->milestoneA<>milestoneB’][‘fork’][‘module’]

classify feature as ‘early’ or ‘late’.

.uns[‘root_milestone->milestoneA<>milestoneB’][‘fork’][‘activation’]

pseudotime of activationh.

Return type

anndata.AnnData