# scanpy.external.tl.phenograph¶

scanpy.external.tl.phenograph(data, k=30, directed=False, prune=False, min_cluster_size=10, jaccard=True, primary_metric='euclidean', n_jobs=-1, q_tol=0.001, louvain_time_limit=2000, nn_method='kdtree')

PhenoGraph clustering [Levine15].

Parameters: Return communities: data Numpy ndarray of data to cluster, or sparse matrix of k-nearest neighbor graph If ndarray, n-by-d array of n cells in d dimensions If sparse matrix, n-by-n adjacency matrix k Number of nearest neighbors to use in first step of graph construction directed Whether to use a symmetric (default) or asymmetric (“directed”) graph The graph construction process produces a directed graph, which is symmetrized by one of two methods (see below) prune Whether to symmetrize by taking the average (prune=False) or product (prune=True) between the graph and its transpose min_cluster_size Cells that end up in a cluster smaller than min_cluster_size are considered outliers and are assigned to -1 in the cluster labels jaccard If True, use Jaccard metric between k-neighborhoods to build graph If False, use a Gaussian kernel primary_metric Distance metric to define nearest neighbors Options include: {‘euclidean’,’manhattan’,’correlation’,’cosine’}. Note that performance will be slower for correlation and cosine n_jobs Nearest Neighbors and Jaccard coefficients will be computed in parallel using n_jobs. If n_jobs=-1, the number of jobs is determined automatically q_tol Tolerance (i.e., precision) for monitoring modularity optimization louvain_time_limit Maximum number of seconds to run modularity optimization. If exceeded the best result so far is returned nn_method Whether to use brute force or kdtree for nearest neighbor search. For very large high-dimensional data sets, brute force (with parallel computation) performs faster than kdtree numpy integer array of community assignments for each row in data numpy sparse array of the graph that was used for clustering the modularity score for communities on graph

Example

>>> import scanpy.api as sc
>>> import numpy as np

>>> # Cluster and cluster centrolds
>>> df = np.random.rand(1000,40)
>>> df.shape
(1000, 40)
>>> communities, graph, Q = sc.tl.phenograph(df, k=50)
Finding 50 nearest neighbors using minkowski metric and 'auto' algorithm
Neighbors computed in 0.16141605377197266 seconds
Jaccard graph constructed in 0.7866239547729492 seconds
Wrote graph to binary file in 0.42542195320129395 seconds
Running Louvain modularity optimization
After 1 runs, maximum modularity is Q = 0.223536
After 2 runs, maximum modularity is Q = 0.235874
Louvain completed 22 runs in 1.5609488487243652 seconds
PhenoGraph complete in 2.9466471672058105 seconds