stlearn.em.run_umap

stlearn.em.run_umap(adata: AnnData, min_dist: float = 0.5, spread: float = 1.0, n_components: int = 2, maxiter: int | None = None, alpha: float = 1.0, gamma: float = 1.0, negative_sample_rate: int = 5, init_pos: Literal['paga', 'spectral', 'random'] | ndarray | None = 'spectral', random_state: int | RandomState | None = 0, a: float | None = None, b: float | None = None, copy: bool = False, method: Literal['umap', 'rapids'] = 'umap') → AnnData | None[source]

Wrap function scanpy.pp.umap

Embed the neighborhood graph using UMAP [McInnes18].

UMAP (Uniform Manifold Approximation and Projection) is a manifold learning technique suitable for visualizing high-dimensional data. Besides tending to be faster than tSNE, it optimizes the embedding such that it best reflects the topology of the data, which we represent throughout Scanpy using a neighborhood graph. tSNE, by contrast, optimizes the distribution of nearest-neighbor distances in the embedding such that these best match the distribution of distances in the high-dimensional space. We use the implementation of umap-learn [McInnes18]. For a few comparisons of UMAP with tSNE, see this preprint.

Parameters:

  • adata – Annotated data matrix.

  • n_components – The number of dimensions of the embedding.

  • random_state – If int, random_state is the seed used by the random number generator; If RandomState, random_state is the random number generator; If None, the random number generator is the RandomState instance used by np.random.

Returns:

  • Depending on copy, returns or updates adata with the following fields.

  • `X_umap` (numpy.ndarray (adata.obsm)) – Independent Component Analysis representation of data.