cellrank.tl.kernels.VelocityKernel.compute_transition_matrix

VelocityKernel.compute_transition_matrix(mode=VelocityMode.DETERMINISTIC, backward_mode=BackwardMode.TRANSPOSE, scheme=Scheme.CORRELATION, softmax_scale=None, n_samples=1000, seed=None, check_irreducibility=False, **kwargs)

Compute transition matrix based on velocity directions on the local manifold.

For each cell, infer transition probabilities based on the cell’s velocity-extrapolated cell state and the cell states of its K nearest neighbors.

Parameters

• mode (Literal[‘deterministic’, ‘stochastic’, ‘sampling’, ‘monte_carlo’]) –

  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.

  • ’sampling’ - sample 1 transition matrix from the velocity distribution.

• backward_mode (Literal[‘transpose’, ‘negate’]) –

  Only matters if initialized as backward = True. Valid options are:

  • ’transpose’ - compute transitions from neighboring cells \(j\) to cell \(i\).

  • ’negate’ - negate the velocity vector.

• 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.

• scheme (Union[Literal[‘dot_product’, ‘cosine’, ‘correlation’], Callable]) –

  Similarity scheme between cells as described in [Li et al., 2021]. Can be one of the following:

  • ’dot_product’ - cellrank.tl.kernels.DotProductScheme.

  • ’cosine’ - cellrank.tl.kernels.CosineScheme.

  • ’correlation’ - cellrank.tl.kernels.CorrelationScheme.

  Alternatively, any function can be passed as long as it follows the signature of cellrank.tl.kernels.SimilaritySchemeABC.__call__().

• n_samples (int) – Number of bootstrap samples when mode = 'monte_carlo'.

• seed (Optional[int]) – Set the seed for random state when the method requires n_samples.

• check_irreducibility (bool) – Optional check for irreducibility of the final transition matrix.

• show_progress_bar – Whether to show a progress bar. Disabling it may slightly improve performance.

• n_jobs – Number of parallel jobs. If -1, use all available cores. If None or 1, the execution is sequential.

• backend – Which backend to use for parallelization. See joblib.Parallel for valid options.

Return type

VelocityKernel

Returns

Self and updates the following fields:

• transition_matrix.

• logits.