#安裝軟件pip install infercnvpy -i https://pypi.tuna./simple some-package

import scanpy as scimport infercnvpy as cnvimport matplotlib.pyplot as pltimport warnings
warnings.simplefilter("ignore")
sc.settings.set_figure_params(figsize=(5, 5))

#library(sceasy)#library(reticulate)#use_condaenv('sceasy')#loompy <- reticulate::import('loompy')# sceasy::convertFormat(uterus, from="seurat", to="anndata", outFile='sce_uterus.h5ad')

import anndataadata = anndata.read_h5ad('sce_uterus.h5ad')adata# AnnData object with n_obs × n_vars = 27914 × 27001# obs: 'orig.ident', 'nCount_RNA', 'nFeature_RNA', 'S.Score', 'G2M.Score', 'Phase', 'percent.mt', 'percent.rb', 'percent.hb', 'integrated_snn_res.0.1', 'integrated_snn_res.0.2', 'integrated_snn_res.0.3', 'integrated_snn_res.0.4', 'integrated_snn_res.0.5', 'integrated_snn_res.0.6', 'integrated_snn_res.0.7', 'integrated_snn_res.0.8', 'integrated_snn_res.0.9', 'integrated_snn_res.1', 'integrated_snn_res.1.1', 'integrated_snn_res.1.2', 'seurat_clusters', 'celltype', 'cell_type', 'group_cell'# var: 'name'# obsm: 'X_pca', 'X_tsne'

sc.pl.tsne(adata, color="cell_type")

gene_annotations = pd.read_csv('geneannotation.csv')

# 將染色體位置信息添加到 adata.varadata.var['chromosome'] = ''  # 初始化染色體列adata.var['start'] = 0   # 初始化起始位置列adata.var['end'] = 0     # 初始化結(jié)束位置列

# 匹配基因ID并添加染色體位置信息for gene_id in adata.var_names:    if gene_id in gene_annotations['gene_symbol'].values:        # 找到匹配的基因并獲取其染色體位置信息        chromosome, start, end = gene_annotations.loc[gene_annotations['gene_symbol'] == gene_id, ['chromosome', 'start', 'end']].values[0]        adata.var.loc[gene_id, 'chromosome'] = chromosome        adata.var.loc[gene_id, 'start'] = start        adata.var.loc[gene_id, 'end'] = end

#去掉NAadata = adata[: , adata.var.chromosome.notna()]

#run infercnvpycnv.tl.infercnv(    adata,    reference_key="group_cell",    reference_cat=[        "HC_Ly",        "HC_Endo",        "HC_Uepi",        "HC_Cepi",        "HC_Mac",        "HC_SMC",        "HC_Sfib",    ],    window_size=250,)

cnv.pl.chromosome_heatmap(adata, groupby="group_cell")

#Clustering by CNV profiles and identifying tumor cellscnv.tl.pca(adata)cnv.pp.neighbors(adata)cnv.tl.leiden(adata)
cnv.pl.chromosome_heatmap(adata, groupby="cnv_leiden", dendrogram=True)

#我這里按照cnv_score進(jìn)行區(qū)分adata.obs["cnv_status"] = "normal"adata.obs.loc[adata.obs["cnv_score"]>0.008, "cnv_status"] = (    "tumor")

sc.pl.tsne(adata, color="cnv_status")

cnv.pl.chromosome_heatmap(adata[adata.obs["cnv_status"] == "normal", :])