Minimal pipeline example

import anndata
import lineageot
import numpy as np

rng = np.random.default_rng()

Creating data

First we make a minimal fake AnnData object to run LineageOT on.

t1 = 5;
t2 = 10;

n_cells_1 = 5;
n_cells_2 = 10;
n_cells = n_cells_1 + n_cells_2;

n_genes = 5;

barcode_length = 10;

adata = anndata.AnnData(X = np.random.rand(n_cells, n_genes),
                        obs = {"time" : np.concatenate([t1*np.ones(n_cells_1), t2*np.ones(n_cells_2)])},
                        obsm = {"barcodes" : rng.integers(low = -1, high = 10, size = (n_cells, barcode_length))}

Fitting a lineage tree

Before running LineageOT, we need to build a lineage tree from the observed barcodes. This step is not optimized. We provide an implementation of a heuristic algorithm called neighbor joining. Feel free to use your own preferred tree construction algorithm. You can import a tree saved in Newick format with lineageot.read_newick.

The tree should be formatted as a NetworkX DiGraph in the same way as the output of lineageot.fit_tree() Each node is annotated with 'time' (which indicates either the time of sampling (for observed cells) or the time of division (for unobserved ancestors). Edges are directed from parent to child and are annotated with 'time' equal to the child node’s 'time_to_parent'. Observed node indices correspond to their row in adata[adata.obs['time'] == t2].

lineage_tree_t2 = lineageot.fit_tree(adata[adata.obs['time'] == t2], t2)

Running LineageOT

Once we have a lineage tree annotated with time, we can compute a LineageOT coupling.

coupling = lineageot.fit_lineage_coupling(adata, t1, t2, lineage_tree_t2)


The LineageOT package does not include functionality for downstream analysis and plotting. We recommend transitioning to other packages, like Waddington-OT, after computing a coupling. This saves the fitted coupling in a format Waddington-OT can import.

lineageot.save_coupling_as_tmap(coupling, t1, t2, './tmaps/example')

Total running time of the script: ( 0 minutes 0.000 seconds)

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