scFates.tl.module_inclusion¶
- scFates.tl.module_inclusion(adata, root_milestone, milestones, w=300, step=30, pseudotime_offset=0, module='early', n_perm=10, n_map=1, map_cutoff=0.8, n_jobs=1, alp=10, autocor_cut=0.95, iterations=15, parallel_mode='window', identify_early_features=False, layer=None, perm=False, winp=10, copy=False)¶
Estimates the pseudotime onset of a feature within its fate-specific module.
- Parameters
- adata
Annotated data matrix.
- root_milestone
tip defining progenitor branch.
- milestones
tips defining the progenies branches.
- w :
int(default:300) local window, in number of cells, to estimate correlations.
- step :
int(default:30) steps, in number of cells, between local windows.
- pseudotime_offset :
all|floatUnion[all,float] (default:0) restrict the cell selection up to a pseudotime offset after the fork
- module : {‘all’, ‘early’}
Literal[‘all’, ‘early’] (default:'early') restrict the gene selection to already classified early genes.
- n_perm :
int(default:10) number of permutations used to estimate the background local correlations.
- n_map :
int(default:1) number of probabilistic cells projection to use for estimates.
- map_cutoff :
float(default:0.8) proportion of mapping in which inclusion pseudotimne was found for a given feature to keep it.
- n_jobs :
int(default:1) number of cpu processes to perform estimates.
- alp :
int(default:10) parameter regulating stringency of inclusion event.
- autocor_cut :
float(default:0.95) cutoff on correlation of inclusion times between sequential iterations of the algorithm to stop it.
- iterations :
int(default:15) maximum number of iterations of the algorithm.
- parallel_mode : {‘window’, ‘mappings’}
Literal[‘window’, ‘mappings’] (default:'window') whether to run in parallel over the windows of cells or the mappings.
- identify_early_features :
bool(default:False) classify a feature as early if its inclusion pseudotime is before the bifurcation
- layer
adata layer to use for estimates.
- perm :
bool(default:False) do local estimates for locally permuted expression matrix.
- winp :
int(default:10) window of permutation in cells.
- copy :
bool(default:False) Return a copy instead of writing to adata.
- Returns
adata – if copy=True it returns subsetted or else subset (keeping only significant features) and add fields to adata:
- .uns[‘root_milestone->milestoneA<>milestoneB’][‘module_inclusion’]
Dataframes ontaining inclusion timing for each gene (rows) in each probabilistic cells projection (columns).
- .uns[‘root_milestone->milestoneA<>milestoneB’][‘fork’]
Updated with ‘inclusion’ pseudotime column and ‘module column if identify_early_features=True’
- Return type