gedi2py.tools.umap

gedi2py.tools.umap(adata, *, n_neighbors=15, min_dist=0.1, n_components=2, metric='euclidean', input_key='pca', key='gedi', key_added=None, random_state=None, copy=False)[source]

Compute UMAP embedding from GEDI results.

Parameters:

  • adata (AnnData) – Annotated data matrix with GEDI results.

  • n_neighbors (int, default: 15) – Size of local neighborhood for UMAP.

  • min_dist (float, default: 0.1) – Minimum distance between points in the embedding.

  • n_components (int, default: 2) – Dimensionality of the UMAP embedding.

  • metric (str, default: 'euclidean') – Distance metric for neighbor search.

  • input_key (Literal['pca', 'db', 'zdb'], default: 'pca') – Which GEDI representation to use as input: - "pca": PCA coordinates (default) - "db": DB latent factor embedding - "zdb": ZDB shared manifold projection

  • key (str, default: 'gedi') – Key in adata.uns where GEDI results are stored.

  • key_added (str | None, default: None) – Key to store UMAP in adata.obsm. Defaults to X_{key}_umap.

  • random_state (int | None, default: None) – Random seed for reproducibility. If None, uses settings.random_state.

  • copy (bool, default: False) – If True, return UMAP coordinates instead of storing in adata.

Return type:

AnnData | ndarray | None

Returns:

  • If ``copy=True``, returns UMAP coordinates as numpy array (n_cells, n_components).

  • Otherwise, stores in ``adata.obsm[key_added]`` and returns ``None`.`

Examples

>>> import gedi2py as gd
>>> gd.tl.gedi(adata, batch_key="sample", n_latent=10)
>>> gd.tl.umap(adata, n_neighbors=30)
>>> gd.pl.embedding(adata, basis="X_gedi_umap", color="cell_type")