Plot graph structures

This functions show how to plot graph structures, such as the transition matrix.

import numpy as np

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 create a forward transition matrix using the high-level pipeline.

cr.tl.transition_matrix(
    adata, show_progress_bar=False, weight_connectivities=0.2, softmax_scale=4
)

Out:

((0.8 * <VelocityKernel>) + (0.2 * <ConnectivityKernel>))

We can now plot the transition matrix. Below we don’t show any arrows, which dramatically speeds up the plotting.

cr.pl.graph(
    adata,
    "T_fwd",
    edge_alpha=0.1,
    node_size=5,
    arrows=False,
    keys="clusters",
    keylocs="obs",
)

To further illustrate the functionalities, let us only consider the ‘Delta’ cluster. We can also filter the edges by their weights, as shown below. Only transitions with probability at least 0.1 are plotted.

ixs = np.where(adata.obs["clusters"] == "Delta")[0]
cr.pl.graph(adata, "T_fwd", ixs=ixs, arrows=True, node_size=200, filter_edges=(0.1, 1))

Lastly, we can visualize different edge aggregations, such as minimum or maximum. Here we take at most 3 outgoing edges restricted to ixs for each node in descending order and color the nodes by the maximum outgoing weights. Aggregated values are always computed before any filtering happens, such as shown above.

Here we also specify edge_reductions_restrict_to_ixs (by default, it is the same as ixs) that computes the statistic between the cells marked with ixs and edge_reductions_restrict_to_ixs.

Below we compare the maximum transition from each of the “Delta” cells to any of the “Beta” cells.

cr.pl.graph(
    adata,
    "T_fwd",
    ixs=ixs,
    edge_alpha=0.5,
    node_size=200,
    keys="outgoing",
    arrows=False,
    top_n_edges=(3, False, "outgoing"),
    title="outgoing to Beta",
    edge_reductions=np.max,
    edge_reductions_restrict_to_ixs=np.where(adata.obs["clusters"] == "Beta")[0],
)

Estimated memory usage: 366 MB