- class moscot.problems.spatiotemporal.SpatioTemporalProblem(adata, **kwargs)#
Class for analyzing time series spatial single-cell data.
add_problem(key, problem, *[, overwrite])
Add a subproblem.
align([reference, mode, spatial_key, key_added])
Align the spatial data.
cell_transition(source, target, ...[, ...])
Aggregate the transport matrix.
compute_batch_distances(time, batch_key[, ...])
Compute the average Wasserstein distance between batches for a specific time point.
Compute correlation of push-forward or pull-back distribution with features.
compute_interpolated_distance(source, ...[, ...])
compute_random_distance(source, ...[, ...])
Compute Wasserstein distance between randomly interpolated and intermediate cells.
compute_time_point_distances(source, ...[, ...])
Compute Wasserstein distance between time points.
Load the model from a file.
prepare(time_key[, spatial_key, joint_attr, ...])
Prepare the spatiotemporal problem problem.
pull(source, target[, data, subset, ...])
Pull mass from target to source.
push(source, target[, data, subset, ...])
Push mass from source to target.
Remove a subproblem.
sankey(source, target, source_groups, ...[, ...])
Compute a Sankey diagram between cells across time points.
Save the problem to a file.
Compute gene scores to obtain prior knowledge about proliferation and apoptosis.
solve([alpha, epsilon, tau_a, tau_b, rank, ...])
Solve the spatiotemporal problem.
Annotated data object.
obswhere cell apoptosis is stored.
Batch key in
Cell cost obtained by the first dual potential.
Cell cost obtained by the second dual potential.
Posterior estimate of the source growth rates.
Prior estimate of the source growth rates.
Kind of the underlying problem.
OT subproblems that define the biological problem.
obswhere cell proliferation is stored.
Solutions to the
Spatial key in
Temporal key in