Plot initial states

This example shows how to compute and plot the initial states of the cell-state transition.

CellRank can be applied to any cell-state transition, be it differentiation, regeneration, reprogramming or other.

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 initial states. By default, we’re using the cellrank.tl.estimators.GPCCA estimator. The parameter cluster_key tries to associate the names of the initial states with cluster labels, whereas n_cells controls how many cells we take from each initial state as categorical observation - this is only available to the above mentioned estimator. We can show some plots of interest by specifying show_plots=True.

cr.tl.initial_states(
    adata,
    cluster_key="clusters",
    n_cells=30,
    softmax_scale=4,
    n_states=1,
    show_progress_bar=False,
)

We can now plot the initial states. By default, when using cellrank.tl.estimators.GPCCA, we plot continuous membership vectors to visualize individual cells associations with an initial state.

We can also plot membership vectors for different initial states separately if we computed more than one initial state using same_plot=False. As we only have one initial state here, this does not make sense.

cr.pl.initial_states(adata)
from Ngn3 low EP

Lastly, we can discretize the assignment of cells to initial states by showing the cells most likely to belong to the initial state by specifying the discrete parameter.

cr.pl.initial_states(adata, discrete=True)
initial states

To see how to compute and plot the terminal states or the lineages, see Plot terminal states or Plot lineages, respectively.

Total running time of the script: ( 0 minutes 7.815 seconds)

Estimated memory usage: 322 MB

Gallery generated by Sphinx-Gallery