scgen_path = 'scgen/scgen_mcl01.h5ad' adata_davae = sc.read_h5ad(base_path + davae_path) adata_scan = sc.read_h5ad(base_path + scan_path) adata_orig = sc.read_h5ad(base_path + orig_path) adata_seurat = sc.read_h5ad(base_path + seurat_path) adata_scgen = sc.read_h5ad(base_path + scgen_path) adata_desc = sc.read_h5ad(base_path + desc_path) sc.pp.filter_genes(adata_orig, min_cells=10) print(adata_orig) # sc.pp.neighbors(adata_orig) # sc.tl.umap(adata_orig) # adata_orig.write_h5ad(base_path+orig_path) orig_label = adata_orig.obs['label'] orig_label = list(map(int, tl.text_label_to_number(orig_label))) orig_batches = adata_orig.obs['batch'].values # sc.pp.neighbors(adata_orig) # sc.tl.umap(adata_orig) # sc.pl.umap(adata_orig, color=['batch', 'celltype']) # sc.pp.neighbors(adata_davae) # sc.tl.umap(adata_davae) # # adata_davae.obs['batch']=adata_orig.obs['batch'] # adata_davae.obs['celltype']=adata_orig.obs['celltype'] # adata_davae.write_h5ad(base_path+'dann_vae/pbmc/293t_save04_label.h5ad') # # sc.pp.neighbors(adata_scan, use_rep='X_scanorama') # sc.tl.umap(adata_scan) # adata_scan.obs['batch']=adata_orig.obs['batch']
seurat_path = 'seurat_result/results/seurat_mouse.h5ad' desc_path = 'dann_vae/mouse/results/desc_mouse.h5ad' scgen_path = 'dann_vae/mouse/results/scgen_mouse.h5ad' adata_davae = sc.read_h5ad(base_path + davae_path) adata_scan = sc.read_h5ad(base_path + scan_path) adata_orig = sc.read_h5ad(base_path + orig_path) # adata_seurat = sc.read_h5ad(base_path+seurat_path) adata_scgen = sc.read_h5ad(base_path + scgen_path) adata_desc = sc.read_h5ad(base_path + desc_path) orig_batches = adata_davae.obs['batch'].values orig_batches = list(map(int, orig_batches)) label = adata_davae.obs['class'] orig_label = tl.text_label_to_number(label) print(collections.Counter(orig_label)) # print(adata_seurat) # sc.pp.neighbors(adata_seurat, use_rep='X_pca') # sc.tl.umap(adata_seurat) # adata_seurat.write_h5ad(base_path+seurat_path) # --------------------------------------- data_scgen_emb = adata_scgen.obsm['X_umap'] data_orig_emb = adata_orig.obsm['X_umap'] data_davae_emb = adata_davae.obsm['X_umap'] # data_seurat_emb = adata_seurat.obsm['X_umap'] data_scan_emb = adata_scan.obsm['X_umap'] data_desc_emb = adata_desc.obsm['X_umap0.8']
print(adata_orig) print(adata_desc) sc.pp.neighbors(adata_orig) sc.tl.umap(adata_orig) # adata_orig.write_h5ad(base_path+orig_path) import collections print(adata_orig.obs['label']) print(adata_orig.obs['celltype']) orig_label = adata_orig.obs['label'] # sc.pl.umap(adata_orig, color=['celltype'], cmap='tab20b') # orig_label = list(map(int, tl.text_label_to_number(orig_label))) orig_batches = adata_orig.obs['batch'].values davae_label = adata_davae.obs['label'] davae_label = list(map(int, tl.text_label_to_number(davae_label))) davae_batches = adata_davae.obs['batch_label'].values # orig_label=davae_label # sc.pp.neighbors(adata_davae) # sc.tl.umap(adata_davae) # adata_davae.write_h5ad(base_path+'dann_vae/benchmark1/davae_save01_uamp.h5ad') # sc.pp.neighbors(adata_scan) # sc.tl.umap(adata_scan) # adata_scan.write_h5ad(base_path+scan_path) # # sc.pp.neighbors(adata_desc, use_rep='X_Embeded_z0.8') sc.tl.umap(adata_desc) # adata_desc.write_h5ad(base_path+desc_path) # # # print(adata_seurat)