compute_transition_matrix(mode='deterministic', backward_mode='transpose', scheme='correlation', softmax_scale=None, n_samples=1000, seed=None, **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.
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
’sampling’ - sample 1 transition matrix from the velocity distribution.
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.
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 counteract everything tending to orthogonality in high dimensions.
Similarity scheme between cells as described in [Li2020]. Can be one of the following:
Alternatively, any function can be passed as long as it follows the call signature of
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.
Makes available the following fields:
- Return type