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

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

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)

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),index=range(df.shape) )
>>> adata = sc.AnnData( X=dframe, obs=dframe, var=dframe)
>>> adata.obs['pheno'] = pd.Categorical(result)