scFates.tl.test_association

scFates.tl.test_association(adata, n_map=1, n_jobs=1, spline_df=5, fdr_cut=0.0001, A_cut=1, st_cut=0.8, reapply_filters=False, plot=False, copy=False, layer=None)

Determine a set of genes significantly associated with the trajectory.

Feature expression is modeled as a function of pseudotime in a branch-specific manner, using cubic spline regression \(g_{i} \sim\ t_{i}\) for each branch independently. This tree-dependent model is then compared with an unconstrained model \(g_{i} \\sim\ 1\) using F-test.

The models are fit using mgcv R package.

Benjamini-Hochberg correction is used to adjust for multiple hypothesis testing.

Parameters:

adata AnnData

Annotated data matrix.

layer Optional[str] (default: None)

adata layer to use for the test.

n_map int (default: 1)

number of cell mappings from which to do the test.

n_jobs int (default: 1)

number of cpu processes used to perform the test.

spline_df int (default: 5)

dimension of the basis used to represent the smooth term.

fdr_cut float (default: 0.0001)

FDR (Benjamini-Hochberg adjustment) cutoff on significance; significance if FDR < fdr_cut.

A_cut int (default: 1)

amplitude is max of predicted value minus min of predicted value by GAM. significance if A > A_cut.

st_cut float (default: 0.8)

cutoff on stability (fraction of mappings with significant (fdr,A) pair) of association; significance, significance if st > st_cut.

reapply_filters bool (default: False)

avoid recomputation and reapply fitlers.

plot bool (default: False)

call scf.pl.test_association after the test.

root

restrain the test to a subset of the tree (in combination with leaves).

leaves

restrain the test to a subset of the tree (in combination with root).

copy bool (default: False)

Return a copy instead of writing to adata.

Returns:

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

.var[‘p_val’]

p-values from statistical test.

.var[‘fdr’]

corrected values from multiple testing.

.var[‘st’]

proportion of mapping in which feature is significant.

.var[‘A’]

amplitue of change of tested feature.

’.var[‘signi’]`

feature is significantly changing along pseuodtime

.uns[‘stat_assoc_list’]

list of fitted features on the tree for all mappings.