from collections.abc import Callable, Mapping
from types import MappingProxyType
from typing import Any, Literal
import numpy as np
import scanpy
from anndata import AnnData
from numpy.random import RandomState
_Method = Literal["umap", "gauss"]
_MetricFn = Callable[[np.ndarray, np.ndarray], float]
# from sklearn.metrics.pairwise_distances.__doc__:
_MetricSparseCapable = Literal[
"cityblock", "cosine", "euclidean", "l1", "l2", "manhattan"
]
_MetricScipySpatial = Literal[
"braycurtis",
"canberra",
"chebyshev",
"correlation",
"dice",
"hamming",
"jaccard",
"kulsinski",
"mahalanobis",
"minkowski",
"rogerstanimoto",
"russellrao",
"seuclidean",
"sokalmichener",
"sokalsneath",
"sqeuclidean",
"yule",
]
_Metric = _MetricSparseCapable | _MetricScipySpatial
[docs]
def neighbors(
adata: AnnData,
n_neighbors: int = 15,
n_pcs: int | None = None,
use_rep: str | None = None,
knn: bool = True,
random_state: int | RandomState | None = 0,
method: _Method = "umap",
metric: _Metric | _MetricFn = "euclidean",
metric_kwds: Mapping[str, Any] = MappingProxyType({}),
copy: bool = False,
) -> AnnData | None:
"""\
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'`). If `method=='gauss'`,
connectivities are computed according to [Coifman05]_, in the adaption of
[Haghverdi16]_.
Parameters
----------
adata:
Annotated data matrix.
n_neighbors:
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.
{n_pcs}
{use_rep}
knn:
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.
random_state:
A numpy random seed.
method:
Use 'umap' [McInnes18]_ or 'gauss' (Gauss kernel following [Coifman05]_
with adaptive width [Haghverdi16]_) for computing connectivities.
metric:
A known metric’s name or a callable that returns a distance.
metric_kwds:
Options for the metric.
copy:
Return a copy instead of writing to adata.
Returns
-------
Depending on `copy`, updates or returns `adata` with the following:
**connectivities** : sparse matrix (`.uns['neighbors']`, dtype `float32`)
Weighted adjacency matrix of the neighborhood graph of data
points. Weights should be interpreted as connectivities.
**distances** : sparse matrix (`.uns['neighbors']`, dtype `float32`)
Instead of decaying weights, this stores distances for each pair of
neighbors.
"""
adata = scanpy.pp.neighbors(
adata,
n_neighbors=n_neighbors,
n_pcs=n_pcs,
use_rep=use_rep,
knn=knn,
random_state=random_state,
method=method,
metric=metric,
metric_kwds=metric_kwds,
copy=copy,
)
print("Created k-Nearest-Neighbor graph in adata.uns['neighbors'] ")
return adata if copy else None