├── .gitignore ├── LICENSE ├── README.md ├── adjacency_matrices_plots └── adjacency_matrices_plots.ipynb ├── attention_visualisation ├── Darmanis │ ├── CellVGAE │ │ ├── cellvgae_clusters.npy │ │ ├── cellvgae_node_embs.npy │ │ └── cellvgae_umap.npy │ ├── attn_layers_data │ │ ├── darmanis_hidden_layer_1_attention_coeff.pt │ │ ├── darmanis_hidden_layer_1_attention_edge_index.pt │ │ ├── darmanis_hidden_layer_2_attention_coeff.pt │ │ ├── darmanis_hidden_layer_2_attention_edge_index.pt │ │ ├── darmanis_hidden_layer_mean_attention_coeff.pt │ │ ├── darmanis_hidden_layer_mean_attention_edge_index.pt │ │ ├── darmanis_hidden_layer_std_attention_coeff.pt │ │ └── darmanis_hidden_layer_std_attention_edge_index.pt │ └── darmanis_ground_truth_clusters.csv ├── PBMC3k │ ├── CellVGAE │ │ ├── cellvgae_clusters.npy │ │ ├── cellvgae_node_embs.npy │ │ └── cellvgae_umap.npy │ ├── attn_layers_data │ │ ├── pbmc3k_hidden_layer_1_attention_coeff.pt │ │ ├── pbmc3k_hidden_layer_1_attention_edge_index.pt │ │ ├── pbmc3k_hidden_layer_2_attention_coeff.pt │ │ ├── pbmc3k_hidden_layer_2_attention_edge_index.pt │ │ ├── pbmc3k_hidden_layer_mean_attention_coeff.pt │ │ ├── pbmc3k_hidden_layer_mean_attention_edge_index.pt │ │ ├── pbmc3k_hidden_layer_std_attention_coeff.pt │ │ └── pbmc3k_hidden_layer_std_attention_edge_index.pt │ └── seurat_clusters.csv ├── PDFs │ ├── attention_heads │ │ ├── darmanis_umap_layer_1st.pdf │ │ ├── darmanis_umap_layer_1st_DIFF.pdf │ │ ├── darmanis_umap_layer_2nd.pdf │ │ ├── darmanis_umap_layer_2nd_DIFF.pdf │ │ ├── darmanis_umap_layer_mu.pdf │ │ ├── darmanis_umap_layer_mu_DIFF.pdf │ │ ├── darmanis_umap_layer_sigma.pdf │ │ ├── darmanis_umap_layer_sigma_DIFF.pdf │ │ ├── pbmc3k_umap_layer_1st.pdf │ │ ├── pbmc3k_umap_layer_1st_DIFF.pdf │ │ ├── pbmc3k_umap_layer_2nd.pdf │ │ ├── pbmc3k_umap_layer_2nd_DIFF.pdf │ │ ├── pbmc3k_umap_layer_mu.pdf │ │ ├── pbmc3k_umap_layer_mu_DIFF.pdf │ │ ├── pbmc3k_umap_layer_std.pdf │ │ └── pbmc3k_umap_layer_std_DIFF.pdf │ ├── darmanis_umap_layer2_mean.pdf │ └── pbmc3k_umap_layer2_mean.pdf ├── attn_graph_darmanis_igraph.ipynb ├── attn_graph_pbmc_igraph.ipynb ├── attn_heatmap_darmanis_mean_heads.ipynb ├── attn_heatmap_darmanis_per_head.ipynb ├── attn_heatmap_pbmc_all_mean_heads.ipynb ├── attn_heatmap_pbmc_per_head.ipynb ├── darmanis_layer_2_graph_ORIGINAL.pdf ├── latent_space_euclidean_distance.ipynb └── pbmc_layer_2_graph_ORIGINAL.pdf ├── cellvgae ├── __init__.py ├── __main__.py ├── models │ ├── CellVGAE.py │ ├── CellVGAE_Encoder.py │ ├── __init__.py │ └── mmd.py └── utils │ ├── __init__.py │ ├── attn_graph.py │ └── top_genes.py ├── example_data ├── paul15_Faiss_KNN_K3_KHVG2500.txt └── paul15_myeloid_scanpy.h5ad ├── examples ├── cellvgae_example_scripts.txt └── cellvgae_save_and_load.txt ├── figures └── workflow.png ├── misc ├── benchmark_sam.ipynb ├── clean_seger.ipynb ├── clean_wang.ipynb └── curate_baron.ipynb ├── platelet_knn_analysis ├── PBMC3k_HVG_250_KNN_k20.txt ├── PBMC3k_HVG_seurat_250.csv ├── platelet_knn_analysis.ipynb └── seurat_clusters.csv ├── preprocessing ├── generate_knn.ipynb └── hvg_proprocessing.ipynb ├── pyproject.toml ├── saved_embeddings ├── baron1 │ ├── Baron1_ground_truth.csv │ ├── clusters.npy │ ├── labels_comparison.csv │ ├── metadata.csv │ ├── node_embs.npy │ └── umap.npy ├── baron2 │ ├── Baron2_ground_truth.csv │ ├── clusters.npy │ ├── labels_comparison.csv │ ├── metadata.csv │ ├── node_embs.npy │ └── umap.npy ├── baron3 │ ├── Baron3_ground_truth.csv │ ├── clusters.npy │ ├── labels_comparison.csv │ ├── metadata.csv │ ├── node_embs.npy │ └── umap.npy ├── baron4 │ ├── Baron4_ground_truth.csv │ ├── clusters.npy │ ├── labels_comparison.csv │ ├── metadata.csv │ ├── node_embs.npy │ └── umap.npy ├── darmanis │ ├── clusters.npy │ ├── darmanis_ground_truth.csv │ ├── labels_comparison.csv │ ├── metadata.csv │ ├── node_embs.npy │ └── umap.npy ├── inspect_saved.ipynb ├── loh │ ├── clusters.npy │ ├── labels_comparison.csv │ ├── loh_ground_truth.csv │ ├── metadata.csv │ ├── node_embs.npy │ └── umap.npy ├── muraro │ ├── clusters.npy │ ├── labels_comparison.csv │ ├── metadata.csv │ ├── muraro_ground_truth.csv │ ├── node_embs.npy │ └── umap.npy ├── plots_with_sam_embeddings │ ├── all_datasets_plots.ipynb │ └── sam_umaps.npy ├── segerstolpe │ ├── clusters.npy │ ├── labels_comparison.csv │ ├── metadata.csv │ ├── node_embs.npy │ ├── seger_ground_truth.csv │ └── umap.npy └── wang │ ├── clusters.npy │ ├── labels_comparison.csv │ ├── metadata.csv │ ├── node_embs.npy │ ├── umap.npy │ └── wang_ground_truth.csv ├── setup.py └── top_genes ├── schisto_example ├── CellVGAE │ ├── cellvgae_clusters.npy │ ├── cellvgae_node_embs.npy │ └── cellvgae_umap.npy ├── HVG │ └── Schisto_HVG_250.csv └── layer_weights │ ├── schisto_hidden_layer_1_weight.pt │ ├── schisto_hidden_layer_2_weight.pt │ ├── schisto_hidden_layer_mean_weight.pt │ └── schisto_hidden_layer_std_weight.pt └── top_genes.ipynb /.gitignore: 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