cellrank.tl.transition_matrix

cellrank.tl.transition_matrix(adata, backward=False, vkey='velocity', xkey='Ms', conn_key='connectivities', gene_subset=None, model=VelocityModel.DETERMINISTIC, backward_mode=BackwardMode.TRANSPOSE, similarity=Similarity.CORRELATION, softmax_scale=None, weight_connectivities=0.2, density_normalize=True, key=None, **kwargs)[source]

Compute a transition matrix based on a combination of RNA Velocity and transcriptomic or spatial similarity.

To learn more about the way in which the transition matrices are computed, see cellrank.tl.kernels.VelocityKernel for the velocity-based transition matrix and cellrank.tl.kernels.ConnectivityKernel for the similarity-based transition matrix.

Parameters

adata (anndata.AnnData) – Annotated data object.
backward (bool) – Direction of the process.
vkey (str) – Key from adata.layers to access the velocities.
xkey (str) – Key in adata.layers where expected gene expression counts are stored.
conn_key (str) – Key in anndata.AnnData.obsp to obtain the connectivity matrix, describing cell-cell similarity.
gene_subset (Optional[Iterable]) – List of genes to be used to compute transition probabilities. By default, genes from adata.var['velocity_genes'] are used.
mode –
How to compute transition probabilities. Valid options are:
- ’deterministic’ - deterministic computation that doesn’t propagate uncertainty.
- ’monte_carlo’ - Monte Carlo average of randomly sampled velocity vectors.
- ’stochastic’ - second order approximation, only available when jax is installed.
backward_mode (Literal[‘transpose’, ‘negate’]) –
How to compute the backward transitions. Valid options are:
- ’transpose’ - compute transitions from neighboring cells \(j\) to cell \(i\).
- ’negate’ - negate the velocity vector.
scheme –
Similarity measure between cells as described in [Li et al., 2021]. Can be one of the following:
- ’correlation’ - cellrank.tl.kernels.utils.Correlation.
- ’cosine’ - cellrank.tl.kernels.utils.Cosine.
- ’dot_product’ - cellrank.tl.kernels.utils.DotProduct.
Alternatively, any function can be passed as long as it follows the signature of cellrank.tl.kernels.utils.SimilarityABC.__call__().
softmax_scale (Optional[float]) – Scaling parameter for the softmax. If None, it will be estimated using 1 / median(correlations). The idea behind this is to scale the softmax to counter everything tending to orthogonality in high dimensions.
weight_connectivities (float) – Weight given to similarities as opposed to velocities. Must be in [0, 1].
density_normalize (bool) – Whether to use density correction when computing the transition probabilities based on similarities. Density correction is done as by [Haghverdi et al., 2016].
key (Optional[str]) – Key used when writing transition matrix to adata. If None, the key will be determined automatically.
kwargs – Keyword arguments for cellrank.tl.kernels.VelocityKernel.compute_transition_matrix().

Return type