scanpy.external.pp.bbknn¶

scanpy.external.pp.
bbknn
(adata, batch_key='batch', copy=False, **kwargs)¶ Batch balanced kNN [Park18].
Batch balanced kNN alters the kNN procedure to identify each cell’s top neighbours in each batch separately instead of the entire cell pool with no accounting for batch. Aligns batches in a quick and lightweight manner.
For use in the scanpy workflow as an alternative to
scanpi.pp.neighbors()
.Note
This is just a wrapper of
bbknn.bbknn()
: up to date docstring, more information and bug reports here. Parameters
 adata :
AnnData
Needs the PCA computed and stored in
adata.obsm["X_pca"]
. batch_key :
str
, optional (default: “batch”) adata.obs
column name discriminating between your batches. neighbors_within_batch :
int
, optional (default: 3) How many top neighbours to report for each batch; total number of neighbours will be this number times the number of batches.
 n_pcs :
int
, optional (default: 50) How many principal components to use in the analysis.
 trim :
int
orNone
, optional (default:None
) Trim the neighbours of each cell to these many top connectivities. May help with population independence and improve the tidiness of clustering. The lower the value the more independent the individual populations, at the cost of more conserved batch effect. If
None
, sets the parameter value automatically to 10 times the total number of neighbours for each cell. Set to 0 to skip. approx :
bool
, optional (default:True
) If
True
, use annoy’s approximate neighbour finding. This results in a quicker run time for large datasets while also potentially increasing the degree of batch correction. n_trees :
int
, optional (default: 10) Only used when
approx=True
. The number of trees to construct in the annoy forest. More trees give higher precision when querying, at the cost of increased run time and resource intensity. use_faiss :
bool
, optional (default:True
) If
approx=False
and the metric is “euclidean”, use the faiss package to compute nearest neighbours if installed. This improves performance at a minor cost to numerical precision as faiss operates on float32. metric :
str
orsklearn.neighbors.DistanceMetric
, optional (default: “angular”) What distance metric to use. If using
approx=True
, the options are “angular”, “euclidean”, “manhattan” and “hamming”. Otherwise, the options are “euclidean”, a member of thesklearn.neighbors.KDTree.valid_metrics
list, or parameterisedsklearn.neighbors.DistanceMetric
objects:>>> from sklearn import neighbors >>> neighbors.KDTree.valid_metrics ['p', 'chebyshev', 'cityblock', 'minkowski', 'infinity', 'l2', 'euclidean', 'manhattan', 'l1'] >>> pass_this_as_metric = neighbors.DistanceMetric.get_metric('minkowski',p=3)
 set_op_mix_ratio :
float
, optional (default: 1) UMAP connectivity computation parameter, float between 0 and 1, controlling the blend between a connectivity matrix formed exclusively from mutual nearest neighbour pairs (0) and a union of all observed neighbour relationships with the mutual pairs emphasised (1)
 local_connectivity :
int
, optional (default: 1) UMAP connectivity computation parameter, how many nearest neighbors of each cell are assumed to be fully connected (and given a connectivity value of 1)
 copy :
bool
, optional (default:False
) If
True
, return a copy instead of writing to the supplied adata.
 adata :
 Returns
The
adata
with the batchcorrected graph.