Tools: TL

Embeddings

tl.phate(adata[, n_components, k, a, ...])	PHATE [Moon et al., 2019].
tl.palantir(adata, *[, n_components, knn, ...])	Run Diffusion maps using the adaptive anisotropic kernel [Setty et al., 2019].
tl.trimap(adata[, n_components, n_inliers, ...])	TriMap: Large-scale Dimensionality Reduction Using Triplets [Amid and Warmuth, 2019].
tl.sam(adata, *[, max_iter, num_norm_avg, ...])	Self-Assembling Manifolds single-cell RNA sequencing analysis tool [Tarashansky et al., 2019].

Clustering and trajectory inference

tl.phenograph(data[, clustering_algo, k, ...])	PhenoGraph clustering [Levine et al., 2015].
tl.harmony_timeseries(adata, tp, *[, ...])	Harmony time series for data visualization with augmented affinity matrix at discrete time points [Nowotschin et al., 2019].
tl.wishbone(adata, start_cell, *[, branch, ...])	Wishbone identifies bifurcating developmental trajectories from single-cell data [Setty et al., 2016].
tl.palantir(adata, *[, n_components, knn, ...])	Run Diffusion maps using the adaptive anisotropic kernel [Setty et al., 2019].
tl.palantir_results(adata, early_cell, *[, ...])	Running Palantir

Gene scores, Cell cycle

tl.sandbag(adata[, annotation, fraction, ...])	Calculate marker pairs of genes [Fechtner, 2018, Scialdone et al., 2015].
tl.cyclone(adata[, marker_pairs, ...])	Assigns scores and predicted class to observations [Scialdone et al., 2015] [Fechtner, 2018].