cellrank.external.kernels.WOTKernel¶

class cellrank.external.kernels.WOTKernel(adata, backward=False, time_key='exp_time', compute_cond_num=False)[source]¶

Waddington optimal transport kernel from [Schiebinger et al., 2019].

This class requires the wot package, which can be installed as pip install git+https://github.com/broadinstitute/wot.

Parameters

adata (anndata.AnnData) – Annotated data object.
backward (bool) – Direction of the process.
time_key (str) – Key in adata .obs where experimental time is stored. The experimental time can be of either of a numeric or an ordered categorical type.
compute_cond_num (bool) – Whether to compute condition number of the transition matrix. Note that this might be costly, since it does not use sparse implementation.

Examples

Workflow:

# import packages, load data
import scanpy as sc
import cellrank as cr
adata = cr.datasets.lung()

# filter, normalize and annotate highly variable genes
sc.pp.filter_genes(adata, min_cells=10)
sc.pp.normalize_total(adata)
sc.pp.log1p(adata)
sc.pp.highly_variable_genes(adata)

# estimate proliferation and apoptosis from gene sets (see. e.g. WOT tutorial for example lists)
proliferation_genes = ...
apoptosis_genes = ...
sc.tl.score_genes(adata, gene_list=proliferation_genes, score_name='proliferation')
sc.tl.score_genes(adata, gene_list=apoptosis_genes, score_name='apoptosis')

# initialize kernel, estimate initial growth rate based on scores from above
from cellrank.external.kernels import WOTKernel
ot = WOTKernel(adata, time_key='day')
ot.compute_initial_growth_rates(proliferation_key='proliferation',
                                apoptosis_key='apoptosis',
                                key_added='initial_growth_rates')

# compute transport maps, aggregate into one single transition matrix
ot.compute_transition_matrix(growth_rate_key='initial_growth_rates', growth_iters=3)

Attributes

`adata`	Annotated data object.
`backward`	Direction of the process.
`condition_number`	Condition number of the transition matrix.
`growth_rates`	Estimated cell growth rates for each growth rate iteration.
`kernels`	Get the kernels of the kernel expression, except for constants.
`params`	Parameters which are used to compute the transition matrix.
`transition_matrix`	Return row-normalized transition matrix.

Methods

`compute_initial_growth_rates`([…])	Estimate initial growth rates using a birth-death process as described in [Schiebinger et al., 2019].
`compute_projection`([basis, key_added, copy])	Compute a projection of the transition matrix in the embedding.
`compute_transition_matrix`([cost_matrices, …])	Compute transition matrix using Waddington OT [Schiebinger et al., 2019].
`copy`()	Return a copy of itself.
`plot_random_walks`(n_sims[, max_iter, seed, …])	Plot random walks in an embedding.
`plot_single_flow`(cluster, cluster_key, time_key)	Visualize outgoing flow from a cluster of cells [Mittnenzweig et al., 2021].
`read`(fname)	Deserialize self from a file.
`write`(fname[, ext])	Serialize self to a file.
`write_to_adata`([key])	Write the transition matrix and parameters used for computation to the underlying `adata` object.