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 highdimensional 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 nearestneighbor distances in the embedding such that these best match the distribution of distances in the highdimensional space. We use the implementation of umaplearn [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 umaplearn 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/1simplex samples to use per positive edge/1simplex sample in optimizing the low dimensional embedding.
 init_pos :
_Literalgenericalias
[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 :
_Literalgenericalias
[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.
 adata :
 Return type
AnnData
,None
 Returns
Depending on copy, returns or updates adata with the following fields.
**X_umap** (adata.obsm field) – UMAP coordinates of data.