bbknn.bbknn
- bbknn.bbknn(adata, batch_key='batch', use_rep='X_pca', key_added=None, copy=False, **kwargs)
Batch balanced KNN, altering 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. The nearest neighbours for each batch are then merged to create a final list of neighbours for the cell. Aligns batches in a quick and lightweight manner. For use in the scanpy workflow as an alternative to
scanpy.pp.neighbors()
.Input
- adata
AnnData
Needs your dimensionality reduction of choice computed and stored in
.obsm
.- 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 in the initial k-nearest-neighbours computation will be this number times the number of batches. This then serves as the basis for the construction of a symmetrical matrix of connectivities.
- use_rep
str
, optional (default: “X_pca”) The dimensionality reduction in
.obsm
to use for neighbour detection. Defaults to PCA.- n_pcs
int
, optional (default: 50) How many dimensions (in case of PCA, 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 timesneighbors_within_batch
times the number of batches. Set to 0 to skip.- computation
str
, optional (default: “annoy”) Which KNN algorithm to use. BBKNN supports the approximate neighbour search of “annoy” and “pynndescent”, and the exact neighbour search of “faiss”, “cKDTree” and “KDTree”. Available metric choices depend on the package used here.
- annoy_n_trees
int
, optional (default: 10) Only used with annoy neighbour identification. 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.
- pynndescent_n_neighbors
int
, optional (default: 30) Only used with pyNNDescent neighbour identification. The number of neighbours to include in the approximate neighbour graph. More neighbours give higher precision when querying, at the cost of increased run time and resource intensity.
- pynndescent_random_state
int
, optional (default: 0) Only used with pyNNDescent neighbour identification. The RNG seed to use when creating the graph.
- metric
str
orsklearn.neighbors.DistanceMetric
ortypes.FunctionType
, optional (default: “euclidean”) What distance metric to use. The options depend on the choice of neighbour algorithm.
“euclidean”, the default, is always available.
Annoy supports “angular”, “manhattan” and “hamming”.
PyNNDescent supports metrics listed in
pynndescent.distances.named_distances
and custom functions, including compiled Numba code.>>> pynndescent.distances.named_distances.keys() dict_keys(['euclidean', 'l2', 'sqeuclidean', 'manhattan', 'taxicab', 'l1', 'chebyshev', 'linfinity', 'linfty', 'linf', 'minkowski', 'seuclidean', 'standardised_euclidean', 'wminkowski', 'weighted_minkowski', 'mahalanobis', 'canberra', 'cosine', 'dot', 'correlation', 'hellinger', 'haversine', 'braycurtis', 'spearmanr', 'kantorovich', 'wasserstein', 'tsss', 'true_angular', 'hamming', 'jaccard', 'dice', 'matching', 'kulsinski', 'rogerstanimoto', 'russellrao', 'sokalsneath', 'sokalmichener', 'yule'])
KDTree supports members of the
sklearn.neighbors.KDTree.valid_metrics()
list, or parameterisedsklearn.metrics.DistanceMetric
objects:>>> sklearn.neighbors.KDTree.valid_metrics() ['euclidean', 'l2', 'minkowski', 'p', 'manhattan', 'cityblock', 'l1', 'chebyshev', 'infinity']
- 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