scanpy.external.tl.phenograph¶

scanpy.external.tl.
phenograph
(adata, 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
 adata
Numpy ndarray of data to cluster, or sparse matrix of knearest neighbor graph If ndarray, nbyd array of n cells in d dimensions If sparse matrix, nbyn 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 kneighborhoods 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 highdimensional 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.external as sce >>> import scanpy.api as sc >>> import numpy as np >>> import pandas as pd
Assume adata is your annotated data which has the normalized data.
Then do PCA:
>>> sc.tl.pca(adata, n_comps = 100)
Compute phenograph clusters:
>>> result = sce.tl.phenograph(adata.obsm['X_pca'], k = 30)
Embed the phenograph result into adata as a categorical variable (this helps in plotting):
>>> adata.obs['pheno'] = pd.Categorical(result[0])
Check by typing “adata” and you should see under obs a key called ‘pheno’.
Now to show phenograph on tSNE (for example):
Compute tSNE:
>>> sc.tl.tsne(adata, random_state = 7)
Plot phenograph clusters on tSNE:
>>> sc.pl.tsne(adata, color = ['pheno'], s = 100, palette = sc.pl.palettes.vega_20_scanpy, legend_fontsize = 10)
Cluster and cluster centroids for input Numpy ndarray
>>> df = np.random.rand(1000,40) >>> df.shape (1000, 40) >>> result = sce.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
New results can be pushed into adata object:
>>> dframe = pd.DataFrame(data=df, columns=range(df.shape[1]),index=range(df.shape[0]) ) >>> adata = sc.AnnData( X=dframe, obs=dframe, var=dframe) >>> adata.obs['pheno'] = pd.Categorical(result[0])