scanpy.pp.neighbors¶

scanpy.pp.
neighbors
(adata, n_neighbors=15, n_pcs=None, use_rep=None, knn=True, random_state=0, method='umap', metric='euclidean', metric_kwds={}, copy=False)¶ Compute 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
AnnData
Annotated data matrix.
 n_neighbors :
int
int
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
isTrue
, number of nearest neighbors to be searched. Ifknn
isFalse
, a Gaussian kernel width is set to the distance of then_neighbors
neighbor. n_pcs :
int
orNone
, optional (default:None
) Use this many PCs. If
n_pcs==0
use.X
ifuse_rep is None
. use_rep : {
None
, ‘X’} or any key for.obsm
, optional (default:None
) Use the indicated representation. If
None
, the representation is chosen automatically: for.n_vars
< 50,.X
is used, otherwise ‘X_pca’ is used. If ‘X_pca’ is not present, it’s computed with default parameters. knn :
bool
bool
If
True
, use a hard threshold to restrict the number of neighbors ton_neighbors
, that is, consider a knn graph. Otherwise, use a Gaussian Kernel to assign low weights to neighbors more distant than then_neighbors
nearest neighbor. random_state :
int
,RandomState
,None
Union
[int
,RandomState
,None
] A numpy random seed.
 method : {‘umap’, ‘gauss’,
None
} (default:'umap'
) Use ‘umap’ [McInnes18] or ‘gauss’ (Gauss kernel following [Coifman05] with adaptive width [Haghverdi16]) for computing connectivities.
 metric :
str
,Callable
[[ndarray
,ndarray
],float
]Union
[str
,Callable
[[ndarray
,ndarray
],float
]] A known metric’s name or a callable that returns a distance.
 metric_kwds :
Mapping
Mapping
[str
,Any
] Options for the metric.
 copy :
bool
bool
Return a copy instead of writing to adata.
 adata :
 Return type
AnnData
,None
Optional
[AnnData
] Returns
Depending on
copy
, updates or returnsadata
with the following: connectivitiessparse matrix (
.uns['neighbors']
, dtypefloat32
) Weighted adjacency matrix of the neighborhood graph of data points. Weights should be interpreted as connectivities.
 distancessparse matrix (
.uns['neighbors']
, dtypefloat32
) Instead of decaying weights, this stores distances for each pair of neighbors.
 connectivitiessparse matrix (