# scanpy.external.pp.bbknn¶

scanpy.external.pp.bbknn(adata, batch_key='batch', approx=True, metric='angular', copy=False, *, n_pcs=50, trim=None, n_trees=10, use_faiss=True, set_op_mix_ratio=1.0, local_connectivity=1, **kwargs)

Batch balanced kNN [Polanski19].

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 neighbors().

Note

This is just a wrapper of bbknn.bbknn(): up to date docstring, more information and bug reports there.

Parameters

Needs the PCA computed and stored in adata.obsm["X_pca"].

batch_key

adata.obs column name discriminating between your batches.

approx

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.

metric

What distance metric to use. If using approx=True, the options are 'angular', 'euclidean', 'manhattan', and 'hamming'. Otherwise, the options are "euclidean", an element of sklearn.neighbors.KDTree’s valid_metrics, or parameterised sklearn.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)


copy

If True, return a copy instead of writing to the supplied adata.

neighbors_within_batch

How many top neighbours to report for each batch; total number of neighbours will be this number times the number of batches.

n_pcs

How many principal components to use in the analysis.

trim

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.

n_trees

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

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 32 bit floats.

set_op_mix_ratio

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

UMAP connectivity computation parameter, how many nearest neighbors per cell are assumed to be fully connected (and given a connectivity value of 1)

Returns

The adata with the batch-corrected graph.