omicverse.single.Velo#
- class omicverse.single.Velo(adata)[source]#
RNA velocity analysis wrapper for directional cell-state transition inference.
- Parameters:
adata (AnnData) – AnnData containing spliced/unspliced layers (or backend-compatible count layers) and low-dimensional embeddings.
- Returns:
Initializes velocity workflow state.
- Return type:
None
Examples
>>> velo_obj = ov.single.Velo(adata)
Methods
__init__(adata)cal_velocity([method, batch_key, ...])Estimate RNA velocity vectors and write them into AnnData.
cell_fate_perturbation(perturbed[, ...])Summarize perturbation effects on terminal cell fates.
cellrank_fate([velocity_key, xkey, ...])Run a CellRank fate-analysis step from OmicVerse velocity output.
dynamics([backend])Fit transcriptional dynamics parameters for velocity inference.
filter_genes([min_shared_counts])Filter genes for velocity modeling using scVelo shared-count criteria.
graphvelo([xkey, vkey, n_jobs, basis_keys, ...])Refine velocity vectors with GraphVelo and project to selected embeddings.
moments([backend, n_pcs, n_neighbors])Compute neighborhood moments required by RNA velocity models.
perturbation_effect(perturbed_adata, ...[, ...])Write single-cell perturbation effects back to
adata.obs.prepare_regvelo(prior_grn[, regulators, ...])Prepare AnnData for
cal_velocity(method='regvelo').preprocess([recipe, n_neighbors, n_pcs])Preprocess expression data before velocity estimation.
regvelo_perturb(tf[, model, adata, effects, ...])Run RegVelo's native in-silico TF regulon blockade from a Velo object.
run()Print a quick diagnostic summary for velocity input data.
velocity_effect(perturbed_adata[, ...])Compute per-cell velocity direction change after perturbation.
velocity_embedding([basis, vkey])Project velocity vectors onto a low-dimensional embedding.
velocity_graph([basis, vkey])Build a velocity transition graph from precomputed velocity vectors.
velocity_streamplot([basis, velocity_key, ...])Plot cells and velocity streamlines with OmicVerse plotting helpers.