Note
Click here to download the full example code
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, 35.64/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"
)

Total running time of the script: ( 0 minutes 17.372 seconds)
Estimated memory usage: 672 MB