squidpy.gr.sepal

squidpy.gr.sepal(adata, max_neighs, genes=None, n_iter=30000, dt=0.001, thresh=1e-08, connectivity_key='spatial_connectivities', spatial_key='spatial', layer=None, use_raw=False, copy=False, n_jobs=None, show_progress_bar=True, *, table_key=None)

Identify spatially variable genes with Sepal.

Sepal is a method that simulates a diffusion process to quantify spatial structure in tissue. See [Anderson and Lundeberg, 2021] for reference.

Parameters:

• adata (AnnData | SpatialData) – Annotated data object.

• table_key (str | None) – Key in spatialdata.SpatialData.tables where the table is stored. Required when adata is a spatialdata.SpatialData object and ignored otherwise.

• max_neighs (Literal[4, 6]) –

  Maximum number of neighbors of a node in the graph. Valid options are:

  • 4 - for a square-grid (ST, Dbit-seq).

  • 6 - for a hexagonal-grid (Visium).

• genes (str | Sequence[str] | None) –

  List of gene names, as stored in anndata.AnnData.var_names, used to compute sepal score.

  If None, it’s computed anndata.AnnData.var ['highly_variable'], if present. Otherwise, it’s computed for all genes.

• n_iter (int | None) – Maximum number of iterations for the diffusion simulation. If n_iter iterations are reached, the simulation will terminate even though convergence has not been achieved.

• dt (float) – Time step in diffusion simulation.

• thresh (float) – Entropy threshold for convergence of diffusion simulation.

• connectivity_key (str) – Key in anndata.AnnData.obsp where spatial connectivities are stored. Default is: anndata.AnnData.obsp ['spatial_connectivities'].

• spatial_key (str) – Key in anndata.AnnData.obsm where spatial coordinates are stored.

• layer (str | None) – Layer in anndata.AnnData.layers to use. If None, use anndata.AnnData.X.

• use_raw (bool) – Whether to access anndata.AnnData.raw.

• copy (bool) – If True, return the result, otherwise save it to the adata object.

• %(n_jobs)s

• %(show_progress_bar)s

Return type:

DataFrame | None

Returns:

If copy = True, returns a pandas.DataFrame with the sepal scores.

Otherwise, modifies the adata with the following key:

• anndata.AnnData.uns ['sepal_score'] - the sepal scores.

Notes

If some genes in anndata.AnnData.uns ['sepal_score'] are NaN, consider re-running the function with increased n_iter.