API

Tools for preprocessing

tl.preprocessing(adata[, Min_Genes, ...])

The recipe for preprocessing raw count data.

Tools for tree alignment

tl.trajectory_tree(adata[, root_node, ...])	Calculate a trajectory tree.
tl.tree_alignment(adata1, adata2[, ...])	Calculate an alignment of the two trajectory trees.
tl.dpt(aligned_data[, alignment, no_prune])	Calculate pseudotime for the alignments.
tl.dtw(aligned_data, gene[, alignment, ...])	Calculate dynamic time warping for genes.

Calculate gene similarity

tl.genes_similarity_score(aligned_data[, ...])

Calculate similarity scores using dynamic time warping [H.Sakoe78].

Plotting

pl.trajectory_tree(adata[, figsize, ...])	Plot a trajectory tree.
pl.tree_alignment(aligned_data[, figsize, ...])	Plot the alignment of the trees.
pl.dtw(aligned_data, gene[, alignment, ...])	Plot the results of dynamic time warping.
pl.gene_expression_trend(aligned_data, gene)	Plot gene expression trend.

Read

tl.read_capital_data(dirname[, adata1_name, ...])

Reading CAPITAL data.

Dataset

dataset.setty19([fpath])	A preprocessed dataset of Setty2019 used in our work.
dataset.paul15([fpath])	A preprocessed dataset of Paul15 used in our work.
dataset.velten17([fpath])	A preprocessed dataset of Velten2017 used in our work.