Plot circular embedding

This example shows how to plot absorption probabilities using circular a posteriori projection [Velten et al., 2017].

import cellrank as cr

adata = cr.datasets.pancreas_preprocessed("../example.h5ad")
adata

Out:

AnnData object with n_obs × n_vars = 2531 × 2000
    obs: 'day', 'proliferation', 'G2M_score', 'S_score', 'phase', 'clusters_coarse', 'clusters', 'clusters_fine', 'louvain_Alpha', 'louvain_Beta', 'initial_size_unspliced', 'initial_size_spliced', 'initial_size', 'n_counts', 'velocity_self_transition', 'dpt_pseudotime'
    var: 'highly_variable_genes', 'gene_count_corr', 'means', 'dispersions', 'dispersions_norm', 'fit_r2', 'fit_alpha', 'fit_beta', 'fit_gamma', 'fit_t_', 'fit_scaling', 'fit_std_u', 'fit_std_s', 'fit_likelihood', 'fit_u0', 'fit_s0', 'fit_pval_steady', 'fit_steady_u', 'fit_steady_s', 'fit_variance', 'fit_alignment_scaling', 'velocity_genes'
    uns: 'clusters_colors', 'clusters_fine_colors', 'diffmap_evals', 'iroot', 'louvain_Alpha_colors', 'louvain_Beta_colors', 'neighbors', 'pca', 'recover_dynamics', 'velocity_graph', 'velocity_graph_neg', 'velocity_params'
    obsm: 'X_diffmap', 'X_pca', 'X_umap', 'velocity_umap'
    varm: 'PCs', 'loss'
    layers: 'Ms', 'Mu', 'fit_t', 'fit_tau', 'fit_tau_', 'spliced', 'unspliced', 'velocity', 'velocity_u'
    obsp: 'connectivities', 'distances'

First, we compute the absorption probabilities.

cr.tl.terminal_states(
    adata,
    cluster_key="clusters",
    weight_connectivities=0.2,
    n_states=3,
    softmax_scale=4,
    show_progress_bar=False,
)
cr.tl.lineages(adata)

Out:

/home/docs/checkouts/readthedocs.org/user_builds/cellrank/checkouts/stable/examples/plotting/plot_circular_embedding.py:15: DeprecationWarning: `cellrank.tl.terminal_states` will be removed in version `2.0`. Please use the `cellrank.kernels` or `cellrank.estimators` interface instead.
  cr.tl.terminal_states(
/home/docs/checkouts/readthedocs.org/user_builds/cellrank/checkouts/stable/cellrank/tl/_init_term_states.py:156: DeprecationWarning: `cellrank.tl.transition_matrix` will be removed in version `2.0`. Please use the `cellrank.kernels` or `cellrank.estimators` interface instead.
  kernel = transition_matrix(
/home/docs/checkouts/readthedocs.org/user_builds/cellrank/checkouts/stable/examples/plotting/plot_circular_embedding.py:23: DeprecationWarning: `cellrank.tl.lineages` will be removed in version `2.0`. Please use the `cellrank.kernels` or `cellrank.estimators` interface instead.
  cr.tl.lineages(adata)

  0%|          | 0/3 [00:00<?, ?/s]
100%|##########| 3/3 [00:00<00:00, 22.43/s]
100%|##########| 3/3 [00:00<00:00, 22.18/s]

We can now visualize the absorption probabilities by projecting them onto a unit circle. The tips of the simplex indicate the probability of 1.0 for the lineages and the midpoints of the edges of the edges mark where the probabilities of the lineages connected by an edge are equal.

cr.pl.circular_projection(
    adata, keys=["clusters", "kl_divergence"], legend_loc="upper right"
)

Estimated memory usage: 668 MB