Note
Click here to download the full example code
Plot potential lineage drivers¶
This example shows how to compute and plot expression trends for genes which may be involved in lineage decisions.
We identify these by correlating gene expression with absorption probabilities towards a specific terminal state.
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 need to compute the terminal states and the absorption probabilities towards them.
cr.tl.terminal_states(
adata,
cluster_key="clusters",
n_cells=30,
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_lineage_drivers.py:17: 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_lineage_drivers.py:25: 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, 44.37/s]
Once the lineages have been computed, we can compute the potential driver genes for each of them. It is also
possible to restrict this computation to just a few clusters, defined by cluster_key and clusters.
By default we are computing the driver genes for all lineages.
drivers = cr.tl.lineage_drivers(adata, lineages="Alpha")
drivers
Out:
/home/docs/checkouts/readthedocs.org/user_builds/cellrank/checkouts/stable/examples/plotting/plot_lineage_drivers.py:32: DeprecationWarning: `cellrank.tl.lineage_drivers` will be removed in version `2.0`. Please use the `cellrank.kernels` or `cellrank.estimators` interface instead.
drivers = cr.tl.lineage_drivers(adata, lineages="Alpha")
Finally, we can plot the potential drivers. Below we plot top 3 driver genes for the ‘Alpha’ lineage.
cr.pl.lineage_drivers(adata, lineage="Alpha", n_genes=3)
Total running time of the script: ( 0 minutes 20.129 seconds)
Estimated memory usage: 681 MB