# scvelo.tl.umap¶

scvelo.tl.umap(adata, min_dist=0.5, spread=1.0, n_components=2, maxiter=None, alpha=1.0, gamma=1.0, negative_sample_rate=5, init_pos='spectral', random_state=0, a=None, b=None, copy=False, method='umap', neighbors_key=None)

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 : AnnData Annotated data matrix. min_dist : float The effective minimum distance between embedded points. Smaller values will result in a more clustered/clumped embedding where nearby points on the manifold are drawn closer together, while larger values will result on a more even dispersal of points. The value should be set relative to the spread value, which determines the scale at which embedded points will be spread out. The default of in the umap-learn package is 0.1. spread : float The effective scale of embedded points. In combination with min_dist this determines how clustered/clumped the embedded points are. n_components : int The number of dimensions of the embedding. maxiter : int, None The number of iterations (epochs) of the optimization. Called n_epochs in the original UMAP. alpha : float The initial learning rate for the embedding optimization. gamma : float Weighting applied to negative samples in low dimensional embedding optimization. Values higher than one will result in greater weight being given to negative samples. negative_sample_rate : int The number of negative edge/1-simplex samples to use per positive edge/1-simplex sample in optimizing the low dimensional embedding. init_pos : _Genericalias[paga, spectral, random], ndarray, None How to initialize the low dimensional embedding. Called init in the original UMAP. Options are: Any key for adata.obsm. ’paga’: positions from paga(). ’spectral’: use a spectral embedding of the graph. ’random’: assign initial embedding positions at random. A numpy array of initial embedding positions. random_state : None, int, RandomState If int, random_state is the seed used by the random number generator; If RandomState or Generator, random_state is the random number generator; If None, the random number generator is the RandomState instance used by np.random. a : float, None More specific parameters controlling the embedding. If None these values are set automatically as determined by min_dist and spread. b : float, None More specific parameters controlling the embedding. If None these values are set automatically as determined by min_dist and spread. copy : bool Return a copy instead of writing to adata. method : _Genericalias[umap, rapids] Use the original ‘umap’ implementation, or ‘rapids’ (experimental, GPU only) neighbors_key : str, None If not specified, umap looks .uns[‘neighbors’] for neighbors settings and .obsp[‘connectivities’] for connectivities (default storage places for pp.neighbors). If specified, umap looks .uns[neighbors_key] for neighbors settings and .obsp[.uns[neighbors_key][‘connectivities_key’]] for connectivities. AnnData, None Depending on copy, returns or updates adata with the following fields. **X_umap** (adata.obsm field) – UMAP coordinates of data.