API

Import scFates as:

import scFates as scf

Some convenient preprocessing functions translated from pagoda2 have been included:

Pre-processing

pp.filter_cells(adata[, device, p_level, …])	Filter cells using on gene/molecule relationship.
pp.batch_correct(adata, batch_key[, layer, …])	batch correction of the count matrix.
pp.find_overdispersed(adata[, gam_k, alpha, …])	Find overdispersed gene, using pagoda2 strategy.
pp.diffusion(adata[, n_components, knn, …])	Wrapper to generate diffusion maps using Palantir.

Tree inference

tl.tree(adata[, Nodes, use_rep, ndims_rep, …])	Generate a principal tree.
tl.curve(adata[, Nodes, use_rep, ndims_rep, …])	Generate a principal curve.
tl.circle(adata[, Nodes, use_rep, …])	Generate a principal circle.
tl.cellrank_to_tree(adata, time, Nodes[, …])	Converts CellRank [Lange21] fate probabilities into a principal tree that can be analysed by scFates.
tl.explore_sigma(adata, Nodes[, use_rep, …])	Explore varisou sigma parameters for best tree fitting.

Tree operations

tl.cleanup(adata[, minbranchlength, leaves, …])	Remove spurious branches from the tree.
tl.subset_tree(adata[, root_milestone, …])	Subset the fitted tree.
tl.attach_tree(adata, adata_branch[, linkage])	Attach a tree to another!
tl.simplify(adata[, n_nodes, copy])	While keeping nodes defining forks and tips (milestones), reduce the number of nodes composing the segments.
tl.convert_to_soft(adata, sigma, lam[, …])	Convert an hard assignment matrix to a soft one, allowing for probabilistic mapping.

Pseudotime analysis

tl.root(adata, root[, tips_only, min_val, …])	Define the root of the trajectory.
tl.roots(adata, roots, meeting[, copy])	Define 2 roots of the tree.
tl.pseudotime(adata[, n_jobs, n_map, seed, copy])	Compute pseudotime.
tl.dendrogram(adata[, crowdedness, n_jobs])	Generate a single-cell dendrogram embedding.
tl.test_association(adata[, n_map, n_jobs, …])	Determine a set of genes significantly associated with the trajectory.
tl.test_association_covariate(adata, group_key)	Separately test for associated features for each covariates on the same trajectory path.
tl.test_association_monocle3(adata[, …])	Determine a set of genes significantly associated with the trajectory.
tl.fit(adata[, features, layer, n_map, …])	Model feature expression levels as a function of tree positions.
tl.cluster(adata[, layer, n_neighbors, …])	Cluster features.
tl.test_covariate(adata, group_key[, …])	Test for branch differential gene expression between covariates on the same trajectory path.
tl.linearity_deviation(adata, …[, …])	Identifies genes that specifically characterize a given transition but not the progenitors neither the progenies.
tl.unroll_circle(adata[, copy])	Unroll circle to get full spectrum of pseudotime values along it.

Bifurcation analysis

Branch specific feature extraction and classification

tl.test_fork(adata, root_milestone, milestones)	Test for branch differential gene expression and differential upregulation from progenitor to terminal state.
tl.branch_specific(adata, root_milestone, …)	Assign genes differentially expressed between two post-bifurcation branches.
tl.activation(adata, root_milestone, milestones)	Identify pseudotime of activation of branch-specififc features.
tl.activation_lm(adata, root_milestone, …)	A more robust version of tl.activation.

Correlation analysis

tl.module_inclusion(adata, root_milestone, …)	Estimates the pseudotime onset of a feature within its fate-specific module.
tl.slide_cells(adata, root_milestone, milestones)	Assign cells in a probabilistic manner to non-intersecting windows along pseudotime.
tl.slide_cors(adata, root_milestone, milestones)	Obtain gene module correlations in the non-intersecting windows along pseudotime.
tl.synchro_path(adata, root_milestone, …)	Estimates pseudotime trends of local intra- and inter-module correlations of fates-specific modules.
tl.synchro_path_multi(adata, root_milestone, …)	Wrappers that call tl.synchro_path on the pairwise combination of all selected branches.

Plot

Trajectory

pl.graph(adata[, basis, size_nodes, …])	Project principal graph onto embedding.
pl.trajectory(adata[, basis, …])	Project trajectory onto embedding.
pl.trajectory_3d(adata[, basis, color, …])	Project trajectory onto 3d embedding.
pl.dendrogram(adata[, root_milestone, …])	Plot the single-cell dendrogram embedding.
pl.milestones(adata[, basis, annotate, …])	Display the milestone graph in PAGA style.

Pseudotime features

pl.test_association(adata[, log_A])	Plot a set of fitted features over pseudotime.
pl.single_trend(adata[, feature, …])	Plot a single feature fit over pseudotime.
pl.trends(adata[, features, cluster, …])	Plot a set of fitted features over pseudotime.
pl.matrix(adata, features[, nbins, layer, …])	Plot a set of features as per-segment matrix plots of binned pseudotimes.
pl.linearity_deviation(adata, …[, …])	Plot the results generated by tl.linearity_deviation.
pl.binned_pseudotime_meta(adata, key[, …])	Plot a dot plot of proportion of cells from a given category over binned sections of pseudotime.

Bifurcation & correlation analysis

pl.modules(adata, root_milestone, milestones)	Plot the mean expression of the early and late modules.
pl.test_fork(adata, root_milestone, milestones)	Plot results generated from tl.test_fork.
pl.slide_cors(adata, root_milestone, milestones)	Plot results generated from tl.slide_cors.
pl.synchro_path(adata, root_milestone, …)	Plot results generated from tl.synchro_path.
pl.module_inclusion(adata, root_milestone, …)	Plot results generated from tl.module_inclusion.

Getting analysed data

get.fork_stats(adata, root_milestone, milestones)	Extract statistics from the fork analysis.
get.modules(adata, root_milestone, milestones)	Extract mean expression of identified early and late modules.
get.slide_cors(adata, root_milestone, …)	Extract statistics from the sliding window correlation analysis.