diff --git a/omicverse/utils/__init__.py b/omicverse/utils/__init__.py index f4da147b..584446a1 100644 --- a/omicverse/utils/__init__.py +++ b/omicverse/utils/__init__.py @@ -35,3 +35,4 @@ from ._heatmap import * from ._roe import roe from ._paga import cal_paga,plot_paga +from ._cluster import cluster diff --git a/omicverse/utils/_cluster.py b/omicverse/utils/_cluster.py new file mode 100644 index 00000000..6c1a1ce4 --- /dev/null +++ b/omicverse/utils/_cluster.py @@ -0,0 +1,53 @@ +#聚类 +from sklearn.mixture import GaussianMixture +import scanpy as sc +import pandas as pd +import anndata + +#初始化聚类位置，这个很重要 +def get_initial_means(X, n_components,init_params, r): + # Run a GaussianMixture with max_iter=0 to output the initialization means + gmm = GaussianMixture( + n_components=n_components, init_params=init_params, tol=1e-9, max_iter=0, random_state=r + ).fit(X) + return gmm.means_ + + +def cluster(adata:anndata.AnnData,method:str='leiden', + use_rep:str='X_pca',random_state:int=1024, + n_components=None, **kwargs): + + if method=='leiden': + sc.tl.leiden(adata,**kwargs) + elif method=='louvain': + sc.tl.louvain(adata,**kwargs) + elif method=='GMM': + if n_components is None: + print('You need to input the `n_components` when methods is `GMM`') + return + print(f"""running GaussianMixture clustering""") + data=adata.obsm[use_rep].copy() + ini = get_initial_means(data,n_components, 'k-means++', 0) + gmm = GaussianMixture(n_components = n_components,random_state=random_state, + means_init=ini, **kwargs) + gmm.fit(data) + adata.obs['gmm_cluster']=gmm.predict(data) + adata.obs['gmm_cluster']=adata.obs['gmm_cluster'].astype(str) + + #new_num=adata.obs['gmm_cluster'].value_counts()[adata.obs['gmm_cluster'].value_counts()>10].shape[0] + #adata.obs.loc[adata.obs['gmm_cluster'].isin(adata.obs['gmm_cluster'].value_counts()[adata.obs['gmm_cluster'].value_counts()<10].index.tolist()),'gmm_cluster']='-1' + + #adata.obs['gmm_cluster']=adata.obs['gmm_cluster'].astype('category') + #adata.obs['gmm_cluster'].cat.categories=pd.Index(list(range(len(adata.obs['gmm_cluster'].cat.categories)))) + + print(f"""finished: found {n_components} clusters and added + 'gmm_cluster', the cluster labels (adata.obs, categorical)""") + +def filtered(adata:anndata.AnnData, + cluster_key:str, + cluster_minsize:int=10): + new_num=adata.obs[cluster_key].value_counts()[adata.obs[cluster_key].value_counts() v 1.1.10 +
  • @@ -496,7 +505,7 @@