# theislab/scanpy

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 """Performm clustering using PhenoGraph """ from .. import logging as logg def phenograph( data, k=30, directed=False, prune=False, min_cluster_size=10, jaccard=True, primary_metric='euclidean', n_jobs=-1, q_tol=1e-3, louvain_time_limit=2000, nn_method='kdtree'): """ PhenoGraph clustering [Levine15]_. :param 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 :param k: Number of nearest neighbors to use in first step of graph construction :param 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) :param prune: Whether to symmetrize by taking the average (prune=False) or product (prune=True) between the graph and its transpose :param 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 :param jaccard: If True, use Jaccard metric between k-neighborhoods to build graph If False, use a Gaussian kernel :param primary_metric: Distance metric to define nearest neighbors Options include: {'euclidean','manhattan','correlation','cosine'}. Note that performance will be slower for correlation and cosine :param 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 :param q_tol: Tolerance (i.e., precision) for monitoring modularity optimization :param louvain_time_limit: Maximum number of seconds to run modularity optimization. If exceeded the best result so far is returned :param 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 :return communities: numpy integer array of community assignments for each row in data :return graph: numpy sparse array of the graph that was used for clustering :return Q: 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 """ logg.info('PhenoGraph clustering', r=True) try: import phenograph except ImportError: raise ImportError( 'please install phenograph: ' 'pip3 install git+https://github.com/jacoblevine/phenograph.git') communities, graph, Q = phenograph.cluster( data=data, k=k, directed=directed, prune=prune, min_cluster_size=min_cluster_size, jaccard=jaccard, primary_metric=primary_metric, n_jobs=n_jobs, q_tol=q_tol, louvain_time_limit=louvain_time_limit, nn_method=nn_method ) logg.info(' finished', time=True) return communities, graph, Q