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| import omicverse as ov | |
| import scanpy as sc | |
| import scvelo as scv | |
| ov.utils.ov_plot_set() | |
| ov.utils.download_pathway_database() | |
| ov.utils.download_geneid_annotation_pair() | |
| adata = scv.datasets.pancreas() | |
| adata | |
| adata.X.max() | |
| sc.pp.normalize_total(adata, target_sum=1e4) | |
| sc.pp.log1p(adata) | |
| adata.X.max() | |
| pathway_dict=ov.utils.geneset_prepare('genesets/GO_Biological_Process_2021.txt',organism='Mouse') | |
| ##Assest one geneset | |
| geneset_name='response to vitamin (GO:0033273)' | |
| ov.single.geneset_aucell(adata, | |
| geneset_name=geneset_name, | |
| geneset=pathway_dict[geneset_name]) | |
| sc.pl.embedding(adata, | |
| basis='umap', | |
| color=["{}_aucell".format(geneset_name)]) | |
| ##Assest more than one geneset | |
| geneset_names=['response to vitamin (GO:0033273)','response to vitamin D (GO:0033280)'] | |
| ov.single.pathway_aucell(adata, | |
| pathway_names=geneset_names, | |
| pathways_dict=pathway_dict) | |
| sc.pl.embedding(adata, | |
| basis='umap', | |
| color=[i+'_aucell' for i in geneset_names]) | |
| ##Assest test geneset | |
| ov.single.geneset_aucell(adata, | |
| geneset_name='Sox', | |
| geneset=['Sox17', 'Sox4', 'Sox7', 'Sox18', 'Sox5']) | |
| sc.pl.embedding(adata, | |
| basis='umap', | |
| color=["Sox_aucell"]) | |
| ##Assest all pathways | |
| adata_aucs=ov.single.pathway_aucell_enrichment(adata, | |
| pathways_dict=pathway_dict, | |
| num_workers=8) | |
| adata_aucs.obs=adata[adata_aucs.obs.index].obs | |
| adata_aucs.obsm=adata[adata_aucs.obs.index].obsm | |
| adata_aucs.obsp=adata[adata_aucs.obs.index].obsp | |
| adata_aucs | |
| adata_aucs.write_h5ad('data/pancreas_auce.h5ad',compression='gzip') | |
| adata_aucs=sc.read('data/pancreas_auce.h5ad') | |
| sc.pl.embedding(adata_aucs, | |
| basis='umap', | |
| color=geneset_names) | |
| #adata_aucs.uns['log1p']['base']=None | |
| sc.tl.rank_genes_groups(adata_aucs, 'clusters', method='t-test',n_genes=100) | |
| sc.pl.rank_genes_groups_dotplot(adata_aucs,groupby='clusters', | |
| cmap='Spectral_r', | |
| standard_scale='var',n_genes=3) | |
| degs = sc.get.rank_genes_groups_df(adata_aucs, group='Beta', key='rank_genes_groups', log2fc_min=2, | |
| pval_cutoff=0.05)['names'].squeeze() | |
| degs | |
| import matplotlib.pyplot as plt | |
| #fig, axes = plt.subplots(4,3,figsize=(12,9)) | |
| axes=sc.pl.embedding(adata_aucs,ncols=3, | |
| basis='umap',show=False,return_fig=True,wspace=0.55,hspace=0.65, | |
| color=['clusters']+degs.values.tolist(), | |
| title=[ov.utils.plot_text_set(i,3,20)for i in ['clusters']+degs.values.tolist()]) | |
| axes.tight_layout() | |
| adata.uns['log1p']['base']=None | |
| sc.tl.rank_genes_groups(adata, 'clusters', method='t-test',n_genes=100) | |
| res=ov.single.pathway_enrichment(adata,pathways_dict=pathway_dict,organism='Mouse', | |
| group_by='clusters',plot=True) | |
| ax=ov.single.pathway_enrichment_plot(res,plot_title='Enrichment',cmap='Reds', | |
| xticklabels=True,cbar=False,square=True,vmax=10, | |
| yticklabels=True,cbar_kws={'label': '-log10(qvalue)','shrink': 0.5,}) | |