scanpy.pp.neighbors#
- scanpy.pp.neighbors(adata, n_neighbors=15, n_pcs=None, *, use_rep=None, knn=True, method='umap', transformer=None, metric='euclidean', metric_kwds=mappingproxy({}), random_state=0, key_added=None, copy=False)[source]#
Computes the nearest neighbors distance matrix and a neighborhood graph of observations [McInnes18].
The neighbor search efficiency of this heavily relies on UMAP [McInnes18], which also provides a method for estimating connectivities of data points - the connectivity of the manifold (
method=='umap'
). Ifmethod=='gauss'
, connectivities are computed according to [Coifman05], in the adaption of [Haghverdi16].- Parameters:
- adata
AnnData
Annotated data matrix.
- n_neighbors
int
(default:15
) The size of local neighborhood (in terms of number of neighboring data points) used for manifold approximation. Larger values result in more global views of the manifold, while smaller values result in more local data being preserved. In general values should be in the range 2 to 100. If knn is True, number of nearest neighbors to be searched. If knn is False, a Gaussian kernel width is set to the distance of the n_neighbors neighbor.
ignored if ``transformer`` is an instance.
- n_pcs
Optional
[int
] (default:None
) Use this many PCs. If n_pcs==0 use .X if use_rep is None.
- use_rep
Optional
[str
] (default:None
) Use the indicated representation. ‘X’ or any key for .obsm is valid. If None, the representation is chosen automatically: For .n_vars <
N_PCS
(default: 50), .X is used, otherwise ‘X_pca’ is used. If ‘X_pca’ is not present, it’s computed with default parameters or n_pcs if present.- knn
bool
(default:True
) If True, use a hard threshold to restrict the number of neighbors to n_neighbors, that is, consider a knn graph. Otherwise, use a Gaussian Kernel to assign low weights to neighbors more distant than the n_neighbors nearest neighbor.
- method
Literal
['umap'
,'gauss'
] (default:'umap'
) Use ‘umap’ [McInnes18] or ‘gauss’ (Gauss kernel following [Coifman05] with adaptive width [Haghverdi16]) for computing connectivities.
- transformer
Union
[KnnTransformerLike
,Literal
['pynndescent'
,'rapids'
],None
] (default:None
) Approximate kNN search implementation following the API of
KNeighborsTransformer
. Also accepts the following known options:- None (the default)
Behavior depends on data size. For small data, we will calculate exact kNN, otherwise we use
PyNNDescentTransformer
- ’pynndescent’
- ’rapids’
A transformer based on
cuml.neighbors.NearestNeighbors
.Deprecated since version 1.10.0: Use
rapids_singlecell.pp.neighbors()
instead.
- metric
Union
[Literal
['cityblock'
,'cosine'
,'euclidean'
,'l1'
,'l2'
,'manhattan'
],Literal
['braycurtis'
,'canberra'
,'chebyshev'
,'correlation'
,'dice'
,'hamming'
,'jaccard'
,'kulsinski'
,'mahalanobis'
,'minkowski'
,'rogerstanimoto'
,'russellrao'
,'seuclidean'
,'sokalmichener'
,'sokalsneath'
,'sqeuclidean'
,'yule'
],Callable
[[ndarray
,ndarray
],float
]] (default:'euclidean'
) A known metric’s name or a callable that returns a distance.
ignored if ``transformer`` is an instance.
- metric_kwds
Mapping
[str
,Any
] (default:mappingproxy({})
) Options for the metric.
ignored if ``transformer`` is an instance.
- random_state
Union
[None
,int
,RandomState
] (default:0
) A numpy random seed.
ignored if ``transformer`` is an instance.
- key_added
Optional
[str
] (default:None
) If not specified, the neighbors data is stored in .uns[‘neighbors’], distances and connectivities are stored in .obsp[‘distances’] and .obsp[‘connectivities’] respectively. If specified, the neighbors data is added to .uns[key_added], distances are stored in .obsp[key_added+’_distances’] and connectivities in .obsp[key_added+’_connectivities’].
- copy
bool
(default:False
) Return a copy instead of writing to adata.
- adata
- Return type:
- Returns:
Returns None if copy=False, else returns an AnnData object. Sets the following fields:
- adata.obsp[‘distances’ | key_added+’_distances’]
scipy.sparse.csr_matrix
(dtype float) Distance matrix of the nearest neighbors search. Each row (cell) has n_neighbors-1 non-zero entries. These are the distances to their n_neighbors-1 nearest neighbors (excluding the cell itself).
- adata.obsp[‘connectivities’ | key_added+’_connectivities’]
scipy.sparse._csr.csr_matrix
(dtype float) Weighted adjacency matrix of the neighborhood graph of data points. Weights should be interpreted as connectivities.
- adata.uns[‘neighbors’ | key_added]
dict
neighbors parameters.
- adata.obsp[‘distances’ | key_added+’_distances’]
Examples
>>> import scanpy as sc >>> adata = sc.datasets.pbmc68k_reduced() >>> # Basic usage >>> sc.pp.neighbors(adata, 20, metric='cosine') >>> # Provide your own transformer for more control and flexibility >>> from sklearn.neighbors import KNeighborsTransformer >>> transformer = KNeighborsTransformer(n_neighbors=10, metric='manhattan', algorithm='kd_tree') >>> sc.pp.neighbors(adata, transformer=transformer) >>> # now you can e.g. access the index: `transformer._tree`