U md!@sdZddlmZmZmZmZmZddlZddl Z ddl m Z ddl mZddlmZddlmZdd lmZdee ejefeedeeeeeedeeeedeeeeeeeeeeeeejeeefdddZdS)z$Perform clustering using PhenoGraph )UnionTupleOptionalTypeAnyN)AnnData)spmatrix)Literal)MutableVertexPartition)logginglouvainF T euclideanMbP?kdtreer Zleiden)rZ manhattanZ correlationZcosine)rZbrute)adataclustering_algokdirectedprunemin_cluster_sizejaccardprimary_metricn_jobsq_tollouvain_time_limit nn_methodpartition_typeresolution_parameter n_iterations use_weightsseedcopykargsreturncKsNtd}zddl}|jdks"tWn"tttfk rFtdYnXt|trz|j d}Wqt k r|t dYqXn|}d}|d krd |nd }d |rd nd}d |rd nd}|j f|||||||||| | | | | |||d|\}}}tjd|d|r|||fS||j |<|r:t||j|<|rJ||j|<dS)a: PhenoGraph clustering [Levine15]_. **PhenoGraph** is a clustering method designed for high-dimensional single-cell data. It works by creating a graph ("network") representing phenotypic similarities between cells and then identifying communities in this graph. It supports both Louvain_ and Leiden_ algorithms for community detection. .. _Louvain: https://louvain-igraph.readthedocs.io/en/latest/ .. _Leiden: https://leidenalg.readthedocs.io/en/latest/reference.html .. note:: More information and bug reports `here `__. Parameters ---------- adata AnnData, or Array 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. clustering_algo Choose between `'Louvain'` or `'Leiden'` algorithm for clustering. 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 `prune` below). prune `prune=False`, symmetrize by taking the average between the graph and its transpose. `prune=True`, symmetrize by taking the product 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. 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 1 is given, no parallelism is used. If set to -1, all CPUs are used. For n_jobs below -1, `n_cpus + 1 + n_jobs` are used. 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. partition_type Defaults to :class:`~leidenalg.RBConfigurationVertexPartition`. For the available options, consult the documentation for :func:`~leidenalg.find_partition`. resolution_parameter A parameter value controlling the coarseness of the clustering in Leiden. Higher values lead to more clusters. Set to `None` if overriding `partition_type` to one that does not accept a `resolution_parameter`. n_iterations Number of iterations to run the Leiden algorithm. If the number of iterations is negative, the Leiden algorithm is run until an iteration in which there was no improvement. use_weights Use vertices in the Leiden computation. seed Leiden initialization of the optimization. copy Return a copy or write to `adata`. kargs Additional arguments passed to :func:`~leidenalg.find_partition` and the constructor of the `partition_type`. Returns ------- Depending on `copy`, returns or updates `adata` with the following fields: **communities** - :class:`~numpy.ndarray` (:attr:`~anndata.AnnData.obs`, dtype `int`) integer array of community assignments for each row in data. **graph** - :class:`~scipy.sparse.spmatrix` (:attr:`~anndata.AnnData.obsp`, dtype `float`) the graph that was used for clustering. **Q** - `float` (:attr:`~anndata.AnnData.uns`, dtype `float`) the modularity score for communities on graph. Example ------- >>> from anndata import AnnData >>> import scanpy as sc >>> import scanpy.external as sce >>> import numpy as np >>> import pandas as pd With annotated data as input: >>> adata = sc.datasets.pbmc3k() >>> sc.pp.normalize_per_cell(adata) Then do PCA: >>> sc.tl.pca(adata, n_comps=100) Compute phenograph clusters: **Louvain** community detection >>> sce.tl.phenograph(adata, clustering_algo="louvain", k=30) **Leiden** community detection >>> sce.tl.phenograph(adata, clustering_algo="leiden", k=30) Return only `Graph` object >>> sce.tl.phenograph(adata, clustering_algo=None, k=30) 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_louvain", "pheno_leiden"], 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) >>> dframe = pd.DataFrame(df) >>> dframe.index, dframe.columns = (map(str, dframe.index), map(str, dframe.columns)) >>> adata = AnnData(dframe) >>> sc.tl.pca(adata, n_comps=20) >>> sce.tl.phenograph(adata, clustering_algo="leiden", k=50) >>> sc.tl.tsne(adata, random_state=1) >>> sc.pl.tsne( ... adata, color=['pheno_leiden'], s=100, ... palette=sc.pl.palettes.vega_20_scanpy, legend_fontsize=10 ... ) zPhenoGraph clusteringrNz1.5.3zKplease install the latest release of phenograph: pip install -U PhenoGraphZX_pcaz0Please run `sc.tl.pca` on `adata` and try again!Trzpheno_{}z pheno_{}_igrZgaussianz pheno_{}_q)datarrrrrrrrr r!r"r#r$r%r&r'z finished)time)logginfo phenograph __version__AssertionError ImportErrorAttributeError isinstancerZobsmKeyErrorformatZclusterZobsppdZ CategoricalZobsZuns)rrrrrrrrrr r!r"r#r$r%r&r'r(r)startr0r,Zcomm_keyZig_keyZq_keyZ communitiesgraphQr<W/home/sam/Atlas/atlas_env/lib/python3.8/site-packages/scanpy/external/tl/_phenograph.pyr0sf0       r0)r rFFrTrrrrrNrrTNF)__doc__typingrrrrrnumpynpZpandasr8ZanndatarZ scipy.sparserZ_compatr Z tools._leidenr r+r r.Zndarrayintboolfloatr0r<r<r<r=sb