{ "cells": [ { "cell_type": "code", "execution_count": 1, "metadata": {}, "outputs": [], "source": [ "import warnings\n", "warnings.simplefilter(action='ignore', category=FutureWarning)\n", "\n", "import scanpy as sc\n", "import pandas as pd" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "# Prep RNA-seq" ] }, { "cell_type": "code", "execution_count": 2, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "AnnData object with n_obs × n_vars = 149926 × 27504\n", " obs: 'nCount_ADT', 'nFeature_ADT', 'nCount_RNA', 'nFeature_RNA', 'orig.ident', 'lane', 'donor', 'time', 'celltype.l1', 'celltype.l2', 'celltype.l3', 'Phase', 'Batch', 'n_genes', 'n_genes_by_counts', 'log1p_n_genes_by_counts', 'total_counts', 'log1p_total_counts', 'total_counts_mt', 'log1p_total_counts_mt', 'pct_counts_mt'\n", " var: 'names', 'n_cells', 'mt', 'n_cells_by_counts', 'mean_counts', 'log1p_mean_counts', 'pct_dropout_by_counts', 'total_counts', 'log1p_total_counts', 'highly_variable', 'means', 'dispersions', 'dispersions_norm'\n", " uns: 'Batch_colors', 'celltype.l1_colors', 'celltype.l2_colors', 'celltype.l3_colors', 'donor_colors', 'neighbors', 'pca', 'umap'\n", " obsm: 'X_pca', 'X_umap'\n", " varm: 'PCs'\n", " layers: 'count'\n", " obsp: 'connectivities', 'distances'" ] }, "execution_count": 2, "metadata": {}, "output_type": "execute_result" } ], "source": [ "scrna1 = sc.read('../data/hao-2020/expressions.h5ad')\n", "scrna1" ] }, { "cell_type": "code", "execution_count": 3, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "View of AnnData object with n_obs × n_vars = 49183 × 27504\n", " obs: 'nCount_ADT', 'nFeature_ADT', 'nCount_RNA', 'nFeature_RNA', 'orig.ident', 'lane', 'donor', 'time', 'celltype.l1', 'celltype.l2', 'celltype.l3', 'Phase', 'Batch', 'n_genes', 'n_genes_by_counts', 'log1p_n_genes_by_counts', 'total_counts', 'log1p_total_counts', 'total_counts_mt', 'log1p_total_counts_mt', 'pct_counts_mt'\n", " var: 'names', 'n_cells', 'mt', 'n_cells_by_counts', 'mean_counts', 'log1p_mean_counts', 'pct_dropout_by_counts', 'total_counts', 'log1p_total_counts', 'highly_variable', 'means', 'dispersions', 'dispersions_norm'\n", " uns: 'Batch_colors', 'celltype.l1_colors', 'celltype.l2_colors', 'celltype.l3_colors', 'donor_colors', 'neighbors', 'pca', 'umap'\n", " obsm: 'X_pca', 'X_umap'\n", " varm: 'PCs'\n", " layers: 'count'\n", " obsp: 'connectivities', 'distances'" ] }, "execution_count": 3, "metadata": {}, "output_type": "execute_result" } ], "source": [ "# take only unperturbed\n", "scrna1 = scrna1[scrna1.obs.time == 0]\n", "scrna1" ] }, { "cell_type": "code", "execution_count": 4, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "AnnData object with n_obs × n_vars = 10025 × 25604\n", " obs: 'cell_type', 'n_genes', 'n_genes_by_counts', 'log1p_n_genes_by_counts', 'total_counts', 'log1p_total_counts', 'total_counts_mt', 'log1p_total_counts_mt', 'pct_counts_mt'\n", " var: 'gene_ids', 'feature_types', 'genome', 'n_cells', 'mt', 'n_cells_by_counts', 'mean_counts', 'log1p_mean_counts', 'pct_dropout_by_counts', 'total_counts', 'log1p_total_counts'\n", " layers: 'count'" ] }, "execution_count": 4, "metadata": {}, "output_type": "execute_result" } ], "source": [ "scrna2 = sc.read('../data/10xpbmc10k-2020/expressions.h5ad')\n", "scrna2" ] }, { "cell_type": "code", "execution_count": 5, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "AnnData object with n_obs × n_vars = 97039 × 24737\n", " obs: 'sample_id', 'n_genes', 'n_genes_by_counts', 'total_counts', 'total_counts_mt', 'pct_counts_mt', 'full_clustering', 'initial_clustering', 'Resample', 'Collection_Day', 'Sex', 'Age_interval', 'Swab_result', 'Status', 'Smoker', 'Status_on_day_collection', 'Status_on_day_collection_summary', 'Days_from_onset', 'Site', 'time_after_LPS', 'Worst_Clinical_Status', 'Outcome', 'patient_id'\n", " var: 'feature_types'\n", " uns: 'full_clustering_colors', 'hvg', 'initial_clustering_colors', 'leiden', 'neighbors', 'pca', 'umap'\n", " obsm: 'X_pca', 'X_pca_harmony', 'X_umap'\n", " layers: 'count'" ] }, "execution_count": 5, "metadata": {}, "output_type": "execute_result" } ], "source": [ "scrna3 = sc.read('../data/haniffa-2021/expressions_healthy.h5ad')\n", "scrna3" ] }, { "cell_type": "code", "execution_count": 6, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "AnnData object with n_obs × n_vars = 6334 × 21819\n", " obs: 'n_genes', 'n_genes_by_counts', 'log1p_n_genes_by_counts', 'total_counts', 'log1p_total_counts', 'total_counts_mt', 'log1p_total_counts_mt', 'pct_counts_mt', 'cell_type', 'batch'\n", " var: 'gene_ids', 'feature_types', 'genome', 'mt', 'n_cells-0', 'n_cells_by_counts-0', 'mean_counts-0', 'log1p_mean_counts-0', 'pct_dropout_by_counts-0', 'total_counts-0', 'log1p_total_counts-0', 'highly_variable-0', 'means-0', 'dispersions-0', 'dispersions_norm-0', 'n_cells-1', 'n_cells_by_counts-1', 'mean_counts-1', 'log1p_mean_counts-1', 'pct_dropout_by_counts-1', 'total_counts-1', 'log1p_total_counts-1', 'highly_variable-1', 'means-1', 'dispersions-1', 'dispersions_norm-1'\n", " obsm: 'X_pca'\n", " layers: 'count'" ] }, "execution_count": 6, "metadata": {}, "output_type": "execute_result" } ], "source": [ "scrna_query1 = sc.read('../data/sanger-2021/expressions.h5ad')\n", "scrna_query1" ] }, { "cell_type": "code", "execution_count": 7, "metadata": {}, "outputs": [], "source": [ "scrna_query1.obs['Batch'] = scrna_query1.obs['batch']" ] }, { "cell_type": "code", "execution_count": 8, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "AnnData object with n_obs × n_vars = 527286 × 24737\n", " obs: 'sample_id', 'n_genes', 'n_genes_by_counts', 'total_counts', 'total_counts_mt', 'pct_counts_mt', 'full_clustering', 'initial_clustering', 'Resample', 'Collection_Day', 'Sex', 'Age_interval', 'Swab_result', 'Status', 'Smoker', 'Status_on_day_collection', 'Status_on_day_collection_summary', 'Days_from_onset', 'Site', 'time_after_LPS', 'Worst_Clinical_Status', 'Outcome', 'patient_id'\n", " var: 'feature_types'\n", " uns: 'full_clustering_colors', 'hvg', 'initial_clustering_colors', 'leiden', 'neighbors', 'pca', 'umap'\n", " obsm: 'X_pca', 'X_pca_harmony', 'X_umap'\n", " layers: 'count'" ] }, "execution_count": 8, "metadata": {}, "output_type": "execute_result" } ], "source": [ "scrna_query2 = sc.read('../data/haniffa-2021/expressions_covid.h5ad')\n", "scrna_query2" ] }, { "cell_type": "code", "execution_count": 9, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "18410" ] }, "execution_count": 9, "metadata": {}, "output_type": "execute_result" } ], "source": [ "common_genes = list(set(scrna1.var_names) & set(scrna2.var_names) & set(scrna3.var_names) & set(scrna_query1.var_names) & set(scrna_query2.var_names))\n", "len(common_genes)" ] }, { "cell_type": "code", "execution_count": 10, "metadata": {}, "outputs": [], "source": [ "scrna1 = scrna1[:, common_genes]\n", "scrna2 = scrna2[:, common_genes]\n", "scrna3 = scrna3[:, common_genes]\n", "scrna_query1 = scrna_query1[:, common_genes]\n", "scrna_query2 = scrna_query2[:, common_genes]" ] }, { "cell_type": "code", "execution_count": 11, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "AnnData object with n_obs × n_vars = 689867 × 18410\n", " obs: 'nCount_ADT', 'nFeature_ADT', 'nCount_RNA', 'nFeature_RNA', 'orig.ident', 'lane', 'donor', 'time', 'celltype.l1', 'celltype.l2', 'celltype.l3', 'Phase', 'Batch', 'n_genes', 'n_genes_by_counts', 'log1p_n_genes_by_counts', 'total_counts', 'log1p_total_counts', 'total_counts_mt', 'log1p_total_counts_mt', 'pct_counts_mt', 'cell_type', 'sample_id', 'full_clustering', 'initial_clustering', 'Resample', 'Collection_Day', 'Sex', 'Age_interval', 'Swab_result', 'Status', 'Smoker', 'Status_on_day_collection', 'Status_on_day_collection_summary', 'Days_from_onset', 'Site', 'time_after_LPS', 'Worst_Clinical_Status', 'Outcome', 'patient_id', 'batch'\n", " var: 'names-0', 'n_cells-0', 'mt-0', 'n_cells_by_counts-0', 'mean_counts-0', 'log1p_mean_counts-0', 'pct_dropout_by_counts-0', 'total_counts-0', 'log1p_total_counts-0', 'highly_variable-0', 'means-0', 'dispersions-0', 'dispersions_norm-0', 'n_cells-1', 'mt-1', 'n_cells_by_counts-1', 'mean_counts-1', 'log1p_mean_counts-1', 'pct_dropout_by_counts-1', 'total_counts-1', 'log1p_total_counts-1', 'gene_ids-1', 'feature_types-1', 'genome-1', 'feature_types-2', 'mt-3', 'gene_ids-3', 'feature_types-3', 'genome-3', 'n_cells-0-3', 'n_cells_by_counts-0-3', 'mean_counts-0-3', 'log1p_mean_counts-0-3', 'pct_dropout_by_counts-0-3', 'total_counts-0-3', 'log1p_total_counts-0-3', 'highly_variable-0-3', 'means-0-3', 'dispersions-0-3', 'dispersions_norm-0-3', 'n_cells-1-3', 'n_cells_by_counts-1-3', 'mean_counts-1-3', 'log1p_mean_counts-1-3', 'pct_dropout_by_counts-1-3', 'total_counts-1-3', 'log1p_total_counts-1-3', 'highly_variable-1-3', 'means-1-3', 'dispersions-1-3', 'dispersions_norm-1-3', 'feature_types-4'\n", " layers: 'count'" ] }, "execution_count": 11, "metadata": {}, "output_type": "execute_result" } ], "source": [ "adata = scrna1.concatenate(scrna2, scrna3, scrna_query1, scrna_query2)\n", "adata" ] }, { "cell_type": "code", "execution_count": 12, "metadata": {}, "outputs": [], "source": [ "adata.X = adata.layers['count']\n", "sc.pp.normalize_total(adata, target_sum=1e4)\n", "sc.pp.log1p(adata)" ] }, { "cell_type": "code", "execution_count": 13, "metadata": {}, "outputs": [], "source": [ "sc.pp.highly_variable_genes(adata, n_top_genes=4000)" ] }, { "cell_type": "code", "execution_count": 14, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "View of AnnData object with n_obs × n_vars = 689867 × 4000\n", " obs: 'nCount_ADT', 'nFeature_ADT', 'nCount_RNA', 'nFeature_RNA', 'orig.ident', 'lane', 'donor', 'time', 'celltype.l1', 'celltype.l2', 'celltype.l3', 'Phase', 'Batch', 'n_genes', 'n_genes_by_counts', 'log1p_n_genes_by_counts', 'total_counts', 'log1p_total_counts', 'total_counts_mt', 'log1p_total_counts_mt', 'pct_counts_mt', 'cell_type', 'sample_id', 'full_clustering', 'initial_clustering', 'Resample', 'Collection_Day', 'Sex', 'Age_interval', 'Swab_result', 'Status', 'Smoker', 'Status_on_day_collection', 'Status_on_day_collection_summary', 'Days_from_onset', 'Site', 'time_after_LPS', 'Worst_Clinical_Status', 'Outcome', 'patient_id', 'batch'\n", " var: 'names-0', 'n_cells-0', 'mt-0', 'n_cells_by_counts-0', 'mean_counts-0', 'log1p_mean_counts-0', 'pct_dropout_by_counts-0', 'total_counts-0', 'log1p_total_counts-0', 'highly_variable-0', 'means-0', 'dispersions-0', 'dispersions_norm-0', 'n_cells-1', 'mt-1', 'n_cells_by_counts-1', 'mean_counts-1', 'log1p_mean_counts-1', 'pct_dropout_by_counts-1', 'total_counts-1', 'log1p_total_counts-1', 'gene_ids-1', 'feature_types-1', 'genome-1', 'feature_types-2', 'mt-3', 'gene_ids-3', 'feature_types-3', 'genome-3', 'n_cells-0-3', 'n_cells_by_counts-0-3', 'mean_counts-0-3', 'log1p_mean_counts-0-3', 'pct_dropout_by_counts-0-3', 'total_counts-0-3', 'log1p_total_counts-0-3', 'highly_variable-0-3', 'means-0-3', 'dispersions-0-3', 'dispersions_norm-0-3', 'n_cells-1-3', 'n_cells_by_counts-1-3', 'mean_counts-1-3', 'log1p_mean_counts-1-3', 'pct_dropout_by_counts-1-3', 'total_counts-1-3', 'log1p_total_counts-1-3', 'highly_variable-1-3', 'means-1-3', 'dispersions-1-3', 'dispersions_norm-1-3', 'feature_types-4', 'highly_variable', 'means', 'dispersions', 'dispersions_norm'\n", " uns: 'log1p'\n", " layers: 'count'" ] }, "execution_count": 14, "metadata": {}, "output_type": "execute_result" } ], "source": [ "adata = adata[:, adata.var.highly_variable]\n", "adata" ] }, { "cell_type": "code", "execution_count": 15, "metadata": {}, "outputs": [], "source": [ "scrna1 = adata[adata.obs.batch == '0']\n", "scrna2 = adata[adata.obs.batch == '1']\n", "scrna3 = adata[adata.obs.batch == '2']\n", "scrna_query1 = adata[adata.obs.batch == '3']\n", "scrna_query2 = adata[adata.obs.batch == '4']" ] }, { "cell_type": "code", "execution_count": 16, "metadata": {}, "outputs": [], "source": [ "del adata" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "# Prep ADT" ] }, { "cell_type": "code", "execution_count": 17, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "AnnData object with n_obs × n_vars = 149926 × 228\n", " obs: 'nCount_ADT', 'nFeature_ADT', 'nCount_RNA', 'nFeature_RNA', 'orig.ident', 'lane', 'donor', 'time', 'celltype.l1', 'celltype.l2', 'celltype.l3', 'Phase', 'Batch', 'n_genes'\n", " var: 'n_cells'\n", " uns: 'Batch_colors', 'celltype.l1_colors', 'celltype.l2_colors', 'celltype.l3_colors', 'donor_colors', 'neighbors', 'pca', 'umap'\n", " obsm: 'X_pca', 'X_umap'\n", " varm: 'PCs'\n", " layers: 'count'\n", " obsp: 'connectivities', 'distances'" ] }, "execution_count": 17, "metadata": {}, "output_type": "execute_result" } ], "source": [ "cite = sc.read('../data/hao-2020/protein.h5ad')\n", "cite" ] }, { "cell_type": "code", "execution_count": 18, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "View of AnnData object with n_obs × n_vars = 49183 × 228\n", " obs: 'nCount_ADT', 'nFeature_ADT', 'nCount_RNA', 'nFeature_RNA', 'orig.ident', 'lane', 'donor', 'time', 'celltype.l1', 'celltype.l2', 'celltype.l3', 'Phase', 'Batch', 'n_genes'\n", " var: 'n_cells'\n", " uns: 'Batch_colors', 'celltype.l1_colors', 'celltype.l2_colors', 'celltype.l3_colors', 'donor_colors', 'neighbors', 'pca', 'umap'\n", " obsm: 'X_pca', 'X_umap'\n", " varm: 'PCs'\n", " layers: 'count'\n", " obsp: 'connectivities', 'distances'" ] }, "execution_count": 18, "metadata": {}, "output_type": "execute_result" } ], "source": [ "# take only unperturbed\n", "cite = cite[cite.obs.time == 0]\n", "cite" ] }, { "cell_type": "code", "execution_count": 19, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "CD4\n", "CD4\n", "CD3\n", "CD56\n", "CD56\n", "CD11b\n", "CD11b\n", "CD38\n", "CD38\n", "CD44\n", "CD44\n", "CD26\n", "CD26\n", "CD275\n", "CD275\n", "CD45\n", "CD45\n", "CD133\n", "CD133\n", "CD3\n", "CD138\n", "CD138\n" ] }, { "data": { "text/html": [ "
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49183 rows × 217 columns

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" ], "text/plain": [ " CD39 Rat-IgG1-1 CD107a CD62P TCR-2 CD30 CD31 \\\n", "L1_AAACCCATCTCCCTAG 5.0 12.0 9.0 88.0 48.0 3.0 40.0 \n", "L1_AAACCCATCTGCGGAC 5.0 3.0 10.0 66.0 15.0 1.0 39.0 \n", "L1_AAACGAAAGGCCCAAA 34.0 15.0 37.0 357.0 14.0 4.0 360.0 \n", "L1_AAACGAAAGTTACTCG 6.0 5.0 11.0 87.0 15.0 4.0 38.0 \n", "L1_AAACGAACACAGCATT 3.0 21.0 33.0 65.0 8.0 10.0 64.0 \n", "... ... ... ... ... ... ... ... \n", "E2L8_TTTGGTTAGGCCTGCT 3.0 5.0 9.0 67.0 4.0 0.0 13.0 \n", "E2L8_TTTGGTTAGTCAGCGA 36.0 9.0 19.0 317.0 5.0 2.0 296.0 \n", "E2L8_TTTGGTTAGTGCCTCG 42.0 4.0 76.0 1050.0 10.0 1.0 396.0 \n", "E2L8_TTTGGTTTCGTCAACA 1.0 4.0 6.0 80.0 12.0 0.0 28.0 \n", "E2L8_TTTGTTGGTTAGTTCG 24.0 5.0 13.0 78.0 4.0 4.0 37.0 \n", "\n", " CD34 CD35 CD36 ... CD119 CD169 CD28 CD161 \\\n", "L1_AAACCCATCTCCCTAG 6.0 77.0 2.0 ... 1.0 3.0 68.0 1.0 \n", "L1_AAACCCATCTGCGGAC 11.0 54.0 4.0 ... 6.0 1.0 56.0 0.0 \n", "L1_AAACGAAAGGCCCAAA 24.0 722.0 12.0 ... 19.0 5.0 32.0 4.0 \n", "L1_AAACGAAAGTTACTCG 8.0 66.0 3.0 ... 3.0 3.0 108.0 7.0 \n", "L1_AAACGAACACAGCATT 9.0 55.0 2.0 ... 5.0 4.0 51.0 18.0 \n", "... ... ... ... ... ... ... ... ... \n", "E2L8_TTTGGTTAGGCCTGCT 1.0 32.0 1.0 ... 3.0 2.0 37.0 1.0 \n", "E2L8_TTTGGTTAGTCAGCGA 11.0 646.0 19.0 ... 18.0 51.0 20.0 5.0 \n", "E2L8_TTTGGTTAGTGCCTCG 12.0 3084.0 23.0 ... 32.0 52.0 15.0 5.0 \n", "E2L8_TTTGGTTTCGTCAACA 1.0 41.0 0.0 ... 5.0 1.0 33.0 2.0 \n", "E2L8_TTTGTTGGTTAGTTCG 5.0 851.0 4.0 ... 12.0 1.0 14.0 1.0 \n", "\n", " CD163 CD138 CD164 CD144 CD202b CD11c \n", "L1_AAACCCATCTCCCTAG 8.0 16.0 6.0 8.0 15.0 21.0 \n", "L1_AAACCCATCTGCGGAC 4.0 13.0 0.0 10.0 10.0 16.0 \n", "L1_AAACGAAAGGCCCAAA 14.0 27.0 2.0 19.0 23.0 70.0 \n", "L1_AAACGAAAGTTACTCG 3.0 13.0 1.0 9.0 12.0 13.0 \n", "L1_AAACGAACACAGCATT 6.0 52.0 4.0 9.0 45.0 46.0 \n", "... ... ... ... ... ... ... \n", "E2L8_TTTGGTTAGGCCTGCT 2.0 8.0 0.0 2.0 7.0 7.0 \n", "E2L8_TTTGGTTAGTCAGCGA 7.0 18.0 5.0 7.0 4.0 47.0 \n", "E2L8_TTTGGTTAGTGCCTCG 10.0 25.0 9.0 5.0 15.0 109.0 \n", "E2L8_TTTGGTTTCGTCAACA 1.0 8.0 4.0 2.0 6.0 8.0 \n", "E2L8_TTTGTTGGTTAGTTCG 5.0 9.0 23.0 2.0 2.0 15.0 \n", "\n", "[49183 rows x 217 columns]" ] }, "execution_count": 19, "metadata": {}, "output_type": "execute_result" } ], "source": [ "# from https://yoseflab.github.io/scvi-tools-reproducibility/scarches_totalvi_seurat_data/\n", "protein_df = pd.DataFrame(index=cite.obs_names)\n", "ref_proteins = cite.var_names\n", "for p in ref_proteins:\n", " if p.split(\"-\")[-1] == \"1\" or p.split(\"-\")[-1] == \"2\":\n", " root = p.split(\"-\")[0]\n", " if root not in [\"Notch\", \"TCR\"]:\n", " try:\n", " protein_df[root] = (\n", " cite[:, root + \"-1\"].layers['count']\n", " + cite[:, root + \"-2\"].layers['count']\n", " )\n", " print(root)\n", " except:\n", " protein_df[p] = cite[:, p].layers['count']\n", " else:\n", " protein_df[p] = cite[:, p].layers['count']\n", " else:\n", " protein_df[p] = cite[:, p].layers['count']\n", "protein_df" ] }, { "cell_type": "code", "execution_count": 20, "metadata": {}, "outputs": [ { "data": { "text/html": [ "
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CD39Rat-IgG1-1CD107aCD62PTCR-2CD30CD31CD34CD35CD36...CD119CD169CD28CD161CD163CD138CD164CD144CD202bCD11c
L1_AAACCCATCTCCCTAG5.012.09.088.048.03.040.06.077.02.0...1.03.068.01.08.016.06.08.015.021.0
L1_AAACCCATCTGCGGAC5.03.010.066.015.01.039.011.054.04.0...6.01.056.00.04.013.00.010.010.016.0
L1_AAACGAAAGGCCCAAA34.015.037.0357.014.04.0360.024.0722.012.0...19.05.032.04.014.027.02.019.023.070.0
L1_AAACGAAAGTTACTCG6.05.011.087.015.04.038.08.066.03.0...3.03.0108.07.03.013.01.09.012.013.0
L1_AAACGAACACAGCATT3.021.033.065.08.010.064.09.055.02.0...5.04.051.018.06.052.04.09.045.046.0
..................................................................
E2L8_TTTGGTTAGGCCTGCT3.05.09.067.04.00.013.01.032.01.0...3.02.037.01.02.08.00.02.07.07.0
E2L8_TTTGGTTAGTCAGCGA36.09.019.0317.05.02.0296.011.0646.019.0...18.051.020.05.07.018.05.07.04.047.0
E2L8_TTTGGTTAGTGCCTCG42.04.076.01050.010.01.0396.012.03084.023.0...32.052.015.05.010.025.09.05.015.0109.0
E2L8_TTTGGTTTCGTCAACA1.04.06.080.012.00.028.01.041.00.0...5.01.033.02.01.08.04.02.06.08.0
E2L8_TTTGTTGGTTAGTTCG24.05.013.078.04.04.037.05.0851.04.0...12.01.014.01.05.09.023.02.02.015.0
\n", "

49183 rows × 217 columns

\n", "
" ], "text/plain": [ " CD39 Rat-IgG1-1 CD107a CD62P TCR-2 CD30 CD31 \\\n", "L1_AAACCCATCTCCCTAG 5.0 12.0 9.0 88.0 48.0 3.0 40.0 \n", "L1_AAACCCATCTGCGGAC 5.0 3.0 10.0 66.0 15.0 1.0 39.0 \n", "L1_AAACGAAAGGCCCAAA 34.0 15.0 37.0 357.0 14.0 4.0 360.0 \n", "L1_AAACGAAAGTTACTCG 6.0 5.0 11.0 87.0 15.0 4.0 38.0 \n", "L1_AAACGAACACAGCATT 3.0 21.0 33.0 65.0 8.0 10.0 64.0 \n", "... ... ... ... ... ... ... ... \n", "E2L8_TTTGGTTAGGCCTGCT 3.0 5.0 9.0 67.0 4.0 0.0 13.0 \n", "E2L8_TTTGGTTAGTCAGCGA 36.0 9.0 19.0 317.0 5.0 2.0 296.0 \n", "E2L8_TTTGGTTAGTGCCTCG 42.0 4.0 76.0 1050.0 10.0 1.0 396.0 \n", "E2L8_TTTGGTTTCGTCAACA 1.0 4.0 6.0 80.0 12.0 0.0 28.0 \n", "E2L8_TTTGTTGGTTAGTTCG 24.0 5.0 13.0 78.0 4.0 4.0 37.0 \n", "\n", " CD34 CD35 CD36 ... CD119 CD169 CD28 CD161 \\\n", "L1_AAACCCATCTCCCTAG 6.0 77.0 2.0 ... 1.0 3.0 68.0 1.0 \n", "L1_AAACCCATCTGCGGAC 11.0 54.0 4.0 ... 6.0 1.0 56.0 0.0 \n", "L1_AAACGAAAGGCCCAAA 24.0 722.0 12.0 ... 19.0 5.0 32.0 4.0 \n", "L1_AAACGAAAGTTACTCG 8.0 66.0 3.0 ... 3.0 3.0 108.0 7.0 \n", "L1_AAACGAACACAGCATT 9.0 55.0 2.0 ... 5.0 4.0 51.0 18.0 \n", "... ... ... ... ... ... ... ... ... \n", "E2L8_TTTGGTTAGGCCTGCT 1.0 32.0 1.0 ... 3.0 2.0 37.0 1.0 \n", "E2L8_TTTGGTTAGTCAGCGA 11.0 646.0 19.0 ... 18.0 51.0 20.0 5.0 \n", "E2L8_TTTGGTTAGTGCCTCG 12.0 3084.0 23.0 ... 32.0 52.0 15.0 5.0 \n", "E2L8_TTTGGTTTCGTCAACA 1.0 41.0 0.0 ... 5.0 1.0 33.0 2.0 \n", "E2L8_TTTGTTGGTTAGTTCG 5.0 851.0 4.0 ... 12.0 1.0 14.0 1.0 \n", "\n", " CD163 CD138 CD164 CD144 CD202b CD11c \n", "L1_AAACCCATCTCCCTAG 8.0 16.0 6.0 8.0 15.0 21.0 \n", "L1_AAACCCATCTGCGGAC 4.0 13.0 0.0 10.0 10.0 16.0 \n", "L1_AAACGAAAGGCCCAAA 14.0 27.0 2.0 19.0 23.0 70.0 \n", "L1_AAACGAAAGTTACTCG 3.0 13.0 1.0 9.0 12.0 13.0 \n", "L1_AAACGAACACAGCATT 6.0 52.0 4.0 9.0 45.0 46.0 \n", "... ... ... ... ... ... ... \n", "E2L8_TTTGGTTAGGCCTGCT 2.0 8.0 0.0 2.0 7.0 7.0 \n", "E2L8_TTTGGTTAGTCAGCGA 7.0 18.0 5.0 7.0 4.0 47.0 \n", "E2L8_TTTGGTTAGTGCCTCG 10.0 25.0 9.0 5.0 15.0 109.0 \n", "E2L8_TTTGGTTTCGTCAACA 1.0 8.0 4.0 2.0 6.0 8.0 \n", "E2L8_TTTGTTGGTTAGTTCG 5.0 9.0 23.0 2.0 2.0 15.0 \n", "\n", "[49183 rows x 217 columns]" ] }, "execution_count": 20, "metadata": {}, "output_type": "execute_result" } ], "source": [ "protein_df" ] }, { "cell_type": "code", "execution_count": 21, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "AnnData object with n_obs × n_vars = 49183 × 217\n", " obs: 'nCount_ADT', 'nFeature_ADT', 'nCount_RNA', 'nFeature_RNA', 'orig.ident', 'lane', 'donor', 'time', 'celltype.l1', 'celltype.l2', 'celltype.l3', 'Phase', 'Batch', 'n_genes'\n", " layers: 'count'" ] }, "execution_count": 21, "metadata": {}, "output_type": "execute_result" } ], "source": [ "cite1 = sc.AnnData(protein_df)\n", "cite1.obs = cite.obs.copy()\n", "cite1.layers['count'] = cite1.X.copy()\n", "cite1" ] }, { "cell_type": "code", "execution_count": 22, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "AnnData object with n_obs × n_vars = 97039 × 192\n", " obs: 'sample_id', 'n_genes', 'n_genes_by_counts', 'total_counts', 'total_counts_mt', 'pct_counts_mt', 'full_clustering', 'initial_clustering', 'Resample', 'Collection_Day', 'Sex', 'Age_interval', 'Swab_result', 'Status', 'Smoker', 'Status_on_day_collection', 'Status_on_day_collection_summary', 'Days_from_onset', 'Site', 'time_after_LPS', 'Worst_Clinical_Status', 'Outcome', 'patient_id'\n", " var: 'feature_types'\n", " uns: 'hvg', 'leiden', 'neighbors', 'pca', 'umap'\n", " obsm: 'X_pca', 'X_pca_harmony', 'X_umap'\n", " layers: 'count'" ] }, "execution_count": 22, "metadata": {}, "output_type": "execute_result" } ], "source": [ "cite3 = sc.read('../data/haniffa-2021/protein_healthy.h5ad')\n", "cite3" ] }, { "cell_type": "code", "execution_count": 23, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "AnnData object with n_obs × n_vars = 527286 × 192\n", " obs: 'sample_id', 'n_genes', 'n_genes_by_counts', 'total_counts', 'total_counts_mt', 'pct_counts_mt', 'full_clustering', 'initial_clustering', 'Resample', 'Collection_Day', 'Sex', 'Age_interval', 'Swab_result', 'Status', 'Smoker', 'Status_on_day_collection', 'Status_on_day_collection_summary', 'Days_from_onset', 'Site', 'time_after_LPS', 'Worst_Clinical_Status', 'Outcome', 'patient_id'\n", " var: 'feature_types'\n", " uns: 'hvg', 'leiden', 'neighbors', 'pca', 'umap'\n", " obsm: 'X_pca', 'X_pca_harmony', 'X_umap'\n", " layers: 'count'" ] }, "execution_count": 23, "metadata": {}, "output_type": "execute_result" } ], "source": [ "cite_query2 = sc.read('../data/haniffa-2021/protein_covid.h5ad')\n", "cite_query2" ] }, { "cell_type": "code", "execution_count": 24, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "Index(['CD80', 'CD86', 'CD274', 'PDCD1LG2', 'ICOSLG', 'ITGAM', 'OX40L',\n", " 'TNFSF9', 'PVR', 'NECTIN2',\n", " ...\n", " 'CD101', 'IL21R', 'C5AR1', 'HLA-F', 'NLRP2', 'Podocalyxin', 'GGT1',\n", " 'c-Met', 'LIGHT', 'DR3'],\n", " dtype='object', length=192)" ] }, "execution_count": 24, "metadata": {}, "output_type": "execute_result" } ], "source": [ "cite3.var_names = [name[3:] for name in cite3.var_names]\n", "cite3.var_names" ] }, { "cell_type": "code", "execution_count": 25, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "Index(['CD80', 'CD86', 'CD274', 'PDCD1LG2', 'ICOSLG', 'ITGAM', 'OX40L',\n", " 'TNFSF9', 'PVR', 'NECTIN2',\n", " ...\n", " 'CD101', 'IL21R', 'C5AR1', 'HLA-F', 'NLRP2', 'Podocalyxin', 'GGT1',\n", " 'c-Met', 'LIGHT', 'DR3'],\n", " dtype='object', length=192)" ] }, "execution_count": 25, "metadata": {}, "output_type": "execute_result" } ], "source": [ "cite_query2.var_names = [name[3:] for name in cite_query2.var_names]\n", "cite_query2.var_names" ] }, { "cell_type": "code", "execution_count": 26, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "61" ] }, "execution_count": 26, "metadata": {}, "output_type": "execute_result" } ], "source": [ "common_protein = list(set(cite1.var_names) & set(cite3.var_names) & set(cite_query2.var_names))\n", "len(common_protein)" ] }, { "cell_type": "code", "execution_count": 27, "metadata": { "scrolled": false }, "outputs": [], "source": [ "cite1 = cite1[:, common_protein].copy()\n", "cite3 = cite3[:, common_protein].copy()\n", "cite_query2 = cite_query2[:, common_protein].copy()" ] }, { "cell_type": "code", "execution_count": 28, "metadata": {}, "outputs": [], "source": [ "def clr_normalize_each_cell(adata, inplace=True):\n", " \"\"\"Normalize count vector for each cell, i.e. for each row of .X\"\"\"\n", "\n", " import numpy as np\n", " import scipy\n", "\n", " def seurat_clr(x):\n", " s = np.sum(np.log1p(x[x > 0]))\n", " exp = np.exp(s / len(x))\n", " return np.log1p(x / exp)\n", "\n", " if not inplace:\n", " adata = adata.copy()\n", "\n", " # apply to dense or sparse matrix, along axis. returns dense matrix\n", " adata.X = np.apply_along_axis(\n", " seurat_clr, 1, (adata.X.A if scipy.sparse.issparse(adata.X) else adata.X)\n", " )\n", " return adata" ] }, { "cell_type": "code", "execution_count": 29, "metadata": {}, "outputs": [], "source": [ "cite1 = clr_normalize_each_cell(cite1)" ] }, { "cell_type": "code", "execution_count": 30, "metadata": {}, "outputs": [], "source": [ "cite3.X = cite3.layers['count']\n", "cite_query2.X = cite_query2.layers['count']" ] }, { "cell_type": "code", "execution_count": 31, "metadata": {}, "outputs": [], "source": [ "cite3 = clr_normalize_each_cell(cite3)\n", "cite_query2 = clr_normalize_each_cell(cite_query2)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "# Prep ATAC-seq" ] }, { "cell_type": "code", "execution_count": 32, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "AnnData object with n_obs × n_vars = 10000 × 40086\n", " obs: 'cell_type'\n", " layers: 'binary'" ] }, "execution_count": 32, "metadata": {}, "output_type": "execute_result" } ], "source": [ "atac2 = sc.read('../data/10xpbmc10k-2020/peaks.h5ad')\n", "atac2" ] }, { "cell_type": "code", "execution_count": 33, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "AnnData object with n_obs × n_vars = 6334 × 38313\n", " obs: 'nb_features', 'cell_type', 'batch'\n", " var: 'feature_types', 'genome', 'gene_ids-0', 'n_cells-0', 'commonness-0', 'prop_shared_cells-0', 'variability_score-0', 'gene_ids-1', 'n_cells-1', 'commonness-1', 'prop_shared_cells-1', 'variability_score-1'\n", " layers: 'binary'" ] }, "execution_count": 33, "metadata": {}, "output_type": "execute_result" } ], "source": [ "atac_query1 = sc.read('../data/sanger-2021/peaks.h5ad')\n", "atac_query1" ] }, { "cell_type": "code", "execution_count": 34, "metadata": {}, "outputs": [], "source": [ "atac_query1.obs['Batch'] = atac_query1.obs['batch']" ] }, { "cell_type": "code", "execution_count": 35, "metadata": {}, "outputs": [], "source": [ "def merge(A, B, chrom):\n", " new_names_A = []\n", " new_names_B = []\n", " \n", " old_names_A = []\n", " old_names_B = []\n", " \n", " A.sort(key=lambda x: x[0])\n", " B.sort(key=lambda x: x[0])\n", "\n", " merged = []\n", " i = j = 0\n", " \n", " while i < len(A) and j < len(B):\n", " \n", " if (A[i][0] >= B[j][0] and A[i][0] <= B[j][1]) or (B[j][0] >= A[i][0] and B[j][0] <= A[i][1]):\n", " left = min(A[i][0], B[j][0])\n", " right = max(A[i][1], B[j][1])\n", " merged.append([left, right])\n", " new_names_A.append(f'{chrom}:{left}-{right}')\n", " new_names_B.append(f'{chrom}:{left}-{right}')\n", " old_names_A.append(f'{chrom}:{A[i][0]}-{A[i][1]}')\n", " old_names_B.append(f'{chrom}:{B[j][0]}-{B[j][1]}')\n", " i+=1 \n", " j+=1\n", " elif B[j][1] < A[i][0]:\n", " new_names_B.append('0') # '0' as a placeholder to be dropped later\n", " old_names_B.append(f'{chrom}:{B[j][0]}-{B[j][1]}')\n", " j+=1\n", " else:\n", " new_names_A.append('0')\n", " old_names_A.append(f'{chrom}:{A[i][0]}-{A[i][1]}')\n", " i+=1\n", " return new_names_A, new_names_B, old_names_A, old_names_B" ] }, { "cell_type": "code", "execution_count": 36, "metadata": {}, "outputs": [], "source": [ "def prepare_peaks(adata):\n", " peaks = {}\n", " \n", " for ch in set([name.split(':')[0] for name in adata.var_names]):\n", " peaks[ch] = []\n", " \n", " for name in adata.var_names:\n", " ch = name.split(\":\")[0]\n", " pos = name.split(\":\")[1].split('-')\n", " pos = [int(n) for n in pos]\n", " peaks[ch].append(pos)\n", " \n", " return peaks" ] }, { "cell_type": "code", "execution_count": 37, "metadata": {}, "outputs": [], "source": [ "def merge_and_rename_peaks(adata1, adata2, verbose=False):\n", " # create dict with peaks of format {'chr': [[peak1_start, peak1_end], ..., [peakn_start, peakn_end]], ...}\n", " peaks1 = prepare_peaks(adata1)\n", " peaks2 = prepare_peaks(adata2)\n", " \n", " # find chromosomes present in both adatas\n", " common_chrs = peaks1.keys() & peaks2.keys()\n", " \n", " # find new peak intervals\n", " adata1_new_var_names = []\n", " adata2_new_var_names = []\n", "\n", " adata1_old_var_names = []\n", " adata2_old_var_names = []\n", "\n", " for chrom in common_chrs:\n", " if verbose:\n", " print(f'Processing chromosome {chrom}...')\n", " \n", " new_names1, new_names2, old_names1, old_names2 = merge(peaks1[chrom], peaks2[chrom], chrom)\n", " \n", " adata1_new_var_names.extend(new_names1)\n", " adata2_new_var_names.extend(new_names2)\n", " \n", " adata1_old_var_names.extend(old_names1)\n", " adata2_old_var_names.extend(old_names2)\n", " \n", " # create dataframes with new var names\n", " df1 = pd.DataFrame(list(zip(adata1_old_var_names, adata1_new_var_names)), columns=['old_var_names', 'new_var_names'])\n", " df1 = df1.set_index('old_var_names')\n", " \n", " df2 = pd.DataFrame(list(zip(adata2_old_var_names, adata2_new_var_names)), columns=['old_var_names', 'new_var_names'])\n", " df2 = df2.set_index('old_var_names')\n", " \n", " # fill chromosomes that were not in the common set with '0'\n", " df1 = pd.DataFrame(index=adata1.var_names).join(df1).fillna('0')\n", " df2 = pd.DataFrame(index=adata2.var_names).join(df2).fillna('0')\n", "\n", " # join new var names with adata.var\n", " adata1.var = adata1.var.join(df1[['new_var_names']])\n", " adata2.var = adata2.var.join(df2[['new_var_names']])\n", " \n", " # drop peaks with '0'\n", " adata1 = adata1[:, adata1.var['new_var_names'] != '0']\n", " adata2 = adata2[:, adata2.var['new_var_names'] != '0']\n", " \n", " # rename the peaks\n", " adata1.var_names = adata1.var['new_var_names']\n", " adata2.var_names = adata2.var['new_var_names']\n", " \n", " del adata1.var['new_var_names']\n", " del adata2.var['new_var_names']\n", " \n", " return adata1, adata2" ] }, { "cell_type": "code", "execution_count": 38, "metadata": {}, "outputs": [], "source": [ "atac2, atac_query1 = merge_and_rename_peaks(atac2, atac_query1)" ] }, { "cell_type": "code", "execution_count": 39, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "AnnData object with n_obs × n_vars = 10000 × 32640\n", " obs: 'cell_type'\n", " layers: 'binary'" ] }, "execution_count": 39, "metadata": {}, "output_type": "execute_result" } ], "source": [ "atac2" ] }, { "cell_type": "code", "execution_count": 40, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "AnnData object with n_obs × n_vars = 6334 × 32640\n", " obs: 'nb_features', 'cell_type', 'batch', 'Batch'\n", " var: 'feature_types', 'genome', 'gene_ids-0', 'n_cells-0', 'commonness-0', 'prop_shared_cells-0', 'variability_score-0', 'gene_ids-1', 'n_cells-1', 'commonness-1', 'prop_shared_cells-1', 'variability_score-1'\n", " layers: 'binary'" ] }, "execution_count": 40, "metadata": {}, "output_type": "execute_result" } ], "source": [ "atac_query1" ] }, { "cell_type": "code", "execution_count": 41, "metadata": {}, "outputs": [], "source": [ "atac2.X = atac2.layers['binary']\n", "sc.pp.normalize_total(atac2, target_sum=1e4)\n", "sc.pp.log1p(atac2)" ] }, { "cell_type": "code", "execution_count": 42, "metadata": {}, "outputs": [], "source": [ "atac_query1.X = atac_query1.layers['binary']\n", "sc.pp.normalize_total(atac_query1, target_sum=1e4)\n", "sc.pp.log1p(atac_query1)" ] }, { "cell_type": "code", "execution_count": 43, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "AAACAGCCAAGGAATC-1 CD4 naive\n", "AAACAGCCAATCCCTT-1 CD4 memory T\n", "AAACAGCCAATGCGCT-1 CD4 naive\n", "AAACAGCCAGTAGGTG-1 CD4 naive\n", "AAACAGCCAGTTTACG-1 CD4 memory T\n", " ... \n", "TTTGTTGGTGACATGC-1 CD8 naive\n", "TTTGTTGGTGTTAAAC-1 CD8 naive\n", "TTTGTTGGTTAGGATT-1 NK\n", "TTTGTTGGTTGGTTAG-1 CD4 memory T\n", "TTTGTTGGTTTGCAGA-1 CD8 memory T\n", "Name: cell_type, Length: 10000, dtype: category\n", "Categories (11, object): ['B', 'CD4 memory T', 'CD4 naive', 'CD8 memory T', ..., 'NK', 'Non-classical monocytes', 'Unconventional T cells', 'pDC']" ] }, "execution_count": 43, "metadata": {}, "output_type": "execute_result" } ], "source": [ "atac2.obs.cell_type" ] }, { "cell_type": "code", "execution_count": 44, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "AAACAGCCAAGGAATC-1-1 naive CD4 T cells\n", "AAACAGCCAATCCCTT-1-1 memory CD4 T cells\n", "AAACAGCCAATGCGCT-1-1 naive CD4 T cells\n", "AAACAGCCAGTAGGTG-1-1 naive CD4 T cells\n", "AAACAGCCAGTTTACG-1-1 memory CD4 T cells\n", " ... \n", "TTTGTTGGTGACATGC-1-1 naive CD8 T cells\n", "TTTGTTGGTGTTAAAC-1-1 naive CD8 T cells\n", "TTTGTTGGTTAGGATT-1-1 CD56 (bright) NK cells\n", "TTTGTTGGTTGGTTAG-1-1 memory CD4 T cells\n", "TTTGTTGGTTTGCAGA-1-1 effector CD8 T cells\n", "Name: cell_type, Length: 10025, dtype: object" ] }, "execution_count": 44, "metadata": {}, "output_type": "execute_result" } ], "source": [ "scrna2.obs.cell_type" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "# Clean up" ] }, { "cell_type": "code", "execution_count": 45, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "Index(['B', 'CD4 T', 'CD8 T', 'DC', 'Mono', 'NK', 'other', 'other T'], dtype='object')" ] }, "execution_count": 45, "metadata": {}, "output_type": "execute_result" } ], "source": [ "scrna1.obs['celltype.l1'].astype('category').cat.categories" ] }, { "cell_type": "code", "execution_count": 46, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "Index(['ASDC', 'B intermediate', 'B memory', 'B naive', 'CD14 Mono',\n", " 'CD16 Mono', 'CD4 CTL', 'CD4 Naive', 'CD4 Proliferating', 'CD4 TCM',\n", " 'CD4 TEM', 'CD8 Naive', 'CD8 Proliferating', 'CD8 TCM', 'CD8 TEM',\n", " 'Doublet', 'Eryth', 'HSPC', 'ILC', 'MAIT', 'NK', 'NK Proliferating',\n", " 'NK_CD56bright', 'Plasmablast', 'Platelet', 'Treg', 'cDC1', 'cDC2',\n", " 'dnT', 'gdT', 'pDC'],\n", " dtype='object')" ] }, "execution_count": 46, "metadata": {}, "output_type": "execute_result" } ], "source": [ "scrna1.obs['celltype.l2'].astype('category').cat.categories" ] }, { "cell_type": "code", "execution_count": 47, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "Index(['CD56 (bright) NK cells', 'CD56 (dim) NK cells', 'MAIT T cells',\n", " 'classical monocytes', 'effector CD8 T cells', 'intermediate monocytes',\n", " 'memory B cells', 'memory CD4 T cells', 'myeloid DC', 'naive B cells',\n", " 'naive CD4 T cells', 'naive CD8 T cells', 'non-classical monocytes',\n", " 'plasmacytoid DC'],\n", " dtype='object')" ] }, "execution_count": 47, "metadata": {}, "output_type": "execute_result" } ], "source": [ "scrna2.obs['cell_type'].astype('category').cat.categories" ] }, { "cell_type": "code", "execution_count": 48, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "Index(['ASDC', 'B_exhausted', 'B_immature', 'B_naive', 'B_non-switched_memory',\n", " 'B_switched_memory', 'C1_CD16_mono', 'CD14_mono', 'CD16_mono', 'CD4.CM',\n", " 'CD4.EM', 'CD4.IL22', 'CD4.Naive', 'CD4.Prolif', 'CD4.Tfh', 'CD4.Th1',\n", " 'CD4.Th17', 'CD4.Th2', 'CD8.EM', 'CD8.Naive', 'CD8.Prolif', 'CD8.TE',\n", " 'CD83_CD14_mono', 'DC1', 'DC2', 'DC3', 'DC_prolif', 'HSC_CD38neg',\n", " 'HSC_CD38pos', 'HSC_erythroid', 'HSC_myeloid', 'HSC_prolif', 'ILC1_3',\n", " 'ILC2', 'MAIT', 'Mono_prolif', 'NKT', 'NK_16hi', 'NK_56hi', 'NK_prolif',\n", " 'Plasma_cell_IgA', 'Plasma_cell_IgG', 'Plasma_cell_IgM', 'Plasmablast',\n", " 'Platelets', 'RBC', 'Treg', 'gdT', 'pDC'],\n", " dtype='object')" ] }, "execution_count": 48, "metadata": {}, "output_type": "execute_result" } ], "source": [ "scrna3.obs['full_clustering'].astype('category').cat.categories" ] }, { "cell_type": "code", "execution_count": 49, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "Index(['B_cell', 'CD14', 'CD16', 'CD4', 'CD8', 'DCs', 'HSC', 'Lymph_prolif',\n", " 'MAIT', 'Mono_prolif', 'NK_16hi', 'NK_56hi', 'Plasmablast', 'Platelets',\n", " 'RBC', 'Treg', 'gdT', 'pDC'],\n", " dtype='object')" ] }, "execution_count": 49, "metadata": {}, "output_type": "execute_result" } ], "source": [ "scrna3.obs['initial_clustering'].astype('category').cat.categories" ] }, { "cell_type": "code", "execution_count": 50, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "Index(['Bcell Memory', 'Bcell Naive', 'CD14 IL1B Monocyte', 'CD14 Monocyte',\n", " 'CD16 Monocyte', 'CD4', 'CD4 Naive', 'CD8 Effector', 'CD8 Naive',\n", " 'Cycling', 'GD Tcell', 'ILC', 'MAIT', 'NK', 'Plasmablasts', 'Treg',\n", " 'cDC', 'pDC'],\n", " dtype='object')" ] }, "execution_count": 50, "metadata": {}, "output_type": "execute_result" } ], "source": [ "scrna_query1.obs['cell_type'].astype('category').cat.categories" ] }, { "cell_type": "code", "execution_count": 51, "metadata": {}, "outputs": [], "source": [ "new_cell_types1 = {\n", " 'ASDC': 'ASDC', \n", " 'B intermediate': 'B cell', \n", " 'B memory': 'B cell', \n", " 'B naive': 'B cell', \n", " 'CD14 Mono': 'CD14 Monocyte',\n", " 'CD16 Mono': 'CD16 Monocyte', \n", " 'CD4 CTL': 'T CD4 memory', \n", " 'CD4 Naive': 'T CD4 naive',\n", " 'CD4 Proliferating': 'T CD4 memory', \n", " 'CD4 TCM': 'T CD4 memory',\n", " 'CD4 TEM': 'T CD4 memory', \n", " 'CD8 Naive': 'T CD8 naive', \n", " 'CD8 Proliferating': 'T CD8 memory', \n", " 'CD8 TCM': 'T CD8 memory', \n", " 'CD8 TEM': 'T CD8 memory',\n", " 'Doublet': 'other', \n", " 'Eryth': 'RBC', \n", " 'HSPC': 'HSPC', \n", " 'ILC': 'ILC', \n", " 'MAIT': 'MAIT', \n", " 'NK': 'NK', \n", " 'NK Proliferating': 'NK',\n", " 'NK_CD56bright': 'NK', \n", " 'Plasmablast': 'Plasmablast', \n", " 'Platelet': 'Platelet', \n", " 'Treg': 'Treg', \n", " 'cDC1': 'cDC', \n", " 'cDC2': 'cDC',\n", " 'dnT': 'other T',\n", " 'gdT': 'gdT', \n", " 'pDC': 'pDC'\n", "}" ] }, { "cell_type": "code", "execution_count": 52, "metadata": {}, "outputs": [], "source": [ "new_cell_types2 = {\n", " 'CD56 (bright) NK cells': 'NK', \n", " 'CD56 (dim) NK cells': 'NK', \n", " 'MAIT T cells': 'MAIT',\n", " 'classical monocytes': 'CD14 Monocyte', \n", " 'effector CD8 T cells': 'T CD8 memory', \n", " 'intermediate monocytes': 'CD14 Monocyte',\n", " 'memory B cells': 'B cell', \n", " 'memory CD4 T cells': 'T CD4 memory', \n", " 'myeloid DC': 'cDC', \n", " 'naive B cells': 'B cell',\n", " 'naive CD4 T cells': 'T CD4 naive', \n", " 'naive CD8 T cells': 'T CD8 naive', \n", " 'non-classical monocytes': 'CD16 Monocyte',\n", " 'plasmacytoid DC': 'pDC'\n", "}" ] }, { "cell_type": "code", "execution_count": 53, "metadata": {}, "outputs": [], "source": [ "new_cell_types3 = {\n", " 'ASDC': 'ASDC', \n", " 'B_exhausted': 'B cell', \n", " 'B_immature': 'B cell', \n", " 'B_naive': 'B cell', \n", " 'B_non-switched_memory': 'B cell',\n", " 'B_switched_memory' : 'B cell', \n", " 'C1_CD16_mono': 'CD16 Monocyte', \n", " 'CD14_mono': 'CD14 Monocyte', \n", " 'CD16_mono': 'CD16 Monocyte', \n", " 'CD4.CM': 'T CD4 memory',\n", " 'CD4.EM': 'T CD4 memory', \n", " 'CD4.IL22': 'T CD4 memory', \n", " 'CD4.Naive': 'T CD4 naive', \n", " 'CD4.Prolif': 'T CD4 memory', \n", " 'CD4.Tfh': 'T CD4 memory', \n", " 'CD4.Th1': 'T CD4 memory',\n", " 'CD4.Th17': 'T CD4 memory', \n", " 'CD4.Th2': 'T CD4 memory', \n", " 'CD8.EM': 'T CD8 memory', \n", " 'CD8.Naive': 'T CD8 naive',\n", " 'CD8.Prolif': 'T CD8 memory', \n", " 'CD8.TE': 'T CD8 memory',\n", " 'CD83_CD14_mono': 'CD14 Monocyte', \n", " 'DC1': 'cDC', \n", " 'DC2': 'cDC', \n", " 'DC3': 'cDC', \n", " 'DC_prolif': 'cDC', \n", " 'HSC_CD38neg': 'HSPC',\n", " 'HSC_CD38pos': 'HSPC', \n", " 'HSC_erythroid': 'HSPC', \n", " 'HSC_myeloid': 'HSPC', \n", " 'HSC_prolif': 'HSPC', \n", " 'ILC1_3': 'ILC',\n", " 'ILC2': 'ILC', \n", " 'MAIT': 'MAIT', \n", " 'Mono_prolif': 'other', \n", " 'NKT': 'other T', \n", " 'NK_16hi': 'NK', \n", " 'NK_56hi': 'NK', \n", " 'NK_prolif': 'NK',\n", " 'Plasma_cell_IgA': 'B cell', \n", " 'Plasma_cell_IgG': 'B cell', \n", " 'Plasma_cell_IgM': 'B cell', \n", " 'Plasmablast': 'Plasmablast',\n", " 'Platelets': 'Platelet', \n", " 'RBC': 'RBC', \n", " 'Treg': 'Treg', \n", " 'gdT': 'gdT', \n", " 'pDC': 'pDC'\n", "}" ] }, { "cell_type": "code", "execution_count": 54, "metadata": {}, "outputs": [], "source": [ "new_cell_types_query1 = {\n", " 'Bcell Memory': 'B cell', \n", " 'Bcell Naive': 'B cell', \n", " 'CD4': 'T CD4 memory', \n", " 'CD4 Naive': 'T CD4 naive', \n", " 'CD8 Effector': 'T CD8 memory',\n", " 'CD8 Naive': 'T CD8 naive', \n", " 'CD14 IL1B Monocyte': 'CD14 Monocyte', \n", " 'CD14 Monocyte': 'CD14 Monocyte', \n", " 'CD16 Monocyte': 'CD16 Monocyte',\n", " 'Cycling': 'other', \n", " 'GD Tcell': 'gdT', \n", " 'ILC': 'ILC', \n", " 'MAIT': 'MAIT', \n", " 'NK': 'NK', \n", " 'Plasmablasts': 'Plasmablast', \n", " 'Treg': 'Treg',\n", " 'cDC': 'cDC',\n", " 'pDC': 'pDC'\n", "}" ] }, { "cell_type": "code", "execution_count": 55, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "{'B_malignant', 'HSC_MK'}" ] }, "execution_count": 55, "metadata": {}, "output_type": "execute_result" } ], "source": [ "set(scrna_query2.obs['full_clustering'].astype('category').cat.categories) - set(scrna3.obs['full_clustering'].astype('category').cat.categories)" ] }, { "cell_type": "code", "execution_count": 56, "metadata": {}, "outputs": [], "source": [ "new_cell_types_query2 = new_cell_types3.copy()\n", "new_cell_types_query2['B_malignant'] = 'B cell'\n", "new_cell_types_query2['HSC_MK'] = 'HSPC'" ] }, { "cell_type": "code", "execution_count": 57, "metadata": {}, "outputs": [], "source": [ "scrna2.obs_names = [name[:-2] for name in scrna2.obs_names]" ] }, { "cell_type": "code", "execution_count": 58, "metadata": {}, "outputs": [], "source": [ "scrna2 = scrna2[atac2.obs_names]\n", "atac2.obs['cell_type'] = scrna2.obs['cell_type']" ] }, { "cell_type": "code", "execution_count": 59, "metadata": {}, "outputs": [], "source": [ "new_cell_types_scrna1 = [new_cell_types1[name] for name in scrna1.obs['celltype.l2']]\n", "new_cell_types_cite1 = [new_cell_types1[name] for name in cite1.obs['celltype.l2']]" ] }, { "cell_type": "code", "execution_count": 60, "metadata": {}, "outputs": [], "source": [ "new_cell_types_scrna2 = [new_cell_types2[name] for name in scrna2.obs['cell_type']]\n", "new_cell_types_atac2 = [new_cell_types2[name] for name in atac2.obs['cell_type']]" ] }, { "cell_type": "code", "execution_count": 61, "metadata": {}, "outputs": [], "source": [ "new_cell_types_scrna3 = [new_cell_types3[name] for name in scrna3.obs['full_clustering']]\n", "new_cell_types_cite3 = [new_cell_types3[name] for name in cite3.obs['full_clustering']]" ] }, { "cell_type": "code", "execution_count": 62, "metadata": {}, "outputs": [], "source": [ "new_cell_types_scrna_query2 = [new_cell_types_query2[name] for name in scrna_query2.obs['full_clustering']]\n", "new_cell_types_cite_query2 = [new_cell_types_query2[name] for name in cite_query2.obs['full_clustering']]" ] }, { "cell_type": "code", "execution_count": 63, "metadata": {}, "outputs": [], "source": [ "new_cell_types_scrna_query1 = [new_cell_types_query1[name] for name in scrna_query1.obs['cell_type']]\n", "new_cell_types_atac_query1 = [new_cell_types_query1[name] for name in atac_query1.obs['cell_type']]" ] }, { "cell_type": "code", "execution_count": 64, "metadata": {}, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ "Trying to set attribute `.obs` of view, copying.\n" ] }, { "data": { "text/plain": [ "Index(['ASDC', 'B cell', 'CD14 Monocyte', 'CD16 Monocyte', 'HSPC', 'ILC',\n", " 'MAIT', 'NK', 'Plasmablast', 'Platelet', 'RBC', 'T CD4 memory',\n", " 'T CD4 naive', 'T CD8 memory', 'T CD8 naive', 'Treg', 'cDC', 'gdT',\n", " 'other', 'other T', 'pDC'],\n", " dtype='object')" ] }, "execution_count": 64, "metadata": {}, "output_type": "execute_result" } ], "source": [ "scrna1.obs['cell_type'] = new_cell_types_scrna1\n", "cite1.obs['cell_type'] = new_cell_types_cite1\n", "scrna1.obs['cell_type'] = scrna1.obs['cell_type'].astype('category')\n", "cite1.obs['cell_type'] = cite1.obs['cell_type'].astype('category')\n", "cite1.obs['cell_type'].cat.categories" ] }, { "cell_type": "code", "execution_count": 65, "metadata": {}, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ "Trying to set attribute `.obs` of view, copying.\n" ] }, { "data": { "text/plain": [ "Index(['ASDC', 'B cell', 'CD14 Monocyte', 'CD16 Monocyte', 'HSPC', 'ILC',\n", " 'MAIT', 'NK', 'Plasmablast', 'Platelet', 'RBC', 'T CD4 memory',\n", " 'T CD4 naive', 'T CD8 memory', 'T CD8 naive', 'Treg', 'cDC', 'gdT',\n", " 'other', 'other T', 'pDC'],\n", " dtype='object')" ] }, "execution_count": 65, "metadata": {}, "output_type": "execute_result" } ], "source": [ "scrna3.obs['cell_type'] = new_cell_types_scrna3\n", "cite3.obs['cell_type'] = new_cell_types_cite3\n", "scrna3.obs['cell_type'] = scrna3.obs['cell_type'].astype('category')\n", "cite3.obs['cell_type'] = cite3.obs['cell_type'].astype('category')\n", "cite3.obs['cell_type'].cat.categories" ] }, { "cell_type": "code", "execution_count": 66, "metadata": {}, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ "Trying to set attribute `.obs` of view, copying.\n" ] }, { "data": { "text/plain": [ "Index(['B cell', 'CD14 Monocyte', 'CD16 Monocyte', 'MAIT', 'NK',\n", " 'T CD4 memory', 'T CD4 naive', 'T CD8 memory', 'T CD8 naive', 'cDC',\n", " 'pDC'],\n", " dtype='object')" ] }, "execution_count": 66, "metadata": {}, "output_type": "execute_result" } ], "source": [ "scrna2.obs['old_cell_type'] = scrna2.obs['cell_type'].copy()\n", "atac2.obs['old_cell_type'] = atac2.obs['cell_type'].copy()\n", "scrna2.obs['cell_type'] = new_cell_types_scrna2\n", "atac2.obs['cell_type'] = new_cell_types_atac2\n", "scrna2.obs['cell_type'] = scrna2.obs['cell_type'].astype('category')\n", "atac2.obs['cell_type'] = atac2.obs['cell_type'].astype('category')\n", "atac2.obs['cell_type'].cat.categories" ] }, { "cell_type": "code", "execution_count": 67, "metadata": {}, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ "Trying to set attribute `.obs` of view, copying.\n" ] }, { "data": { "text/plain": [ "Index(['B cell', 'CD14 Monocyte', 'CD16 Monocyte', 'ILC', 'MAIT', 'NK',\n", " 'Plasmablast', 'T CD4 memory', 'T CD4 naive', 'T CD8 memory',\n", " 'T CD8 naive', 'Treg', 'cDC', 'gdT', 'other', 'pDC'],\n", " dtype='object')" ] }, "execution_count": 67, "metadata": {}, "output_type": "execute_result" } ], "source": [ "scrna_query1.obs['old_cell_type'] = scrna_query1.obs['cell_type'].copy()\n", "atac_query1.obs['old_cell_type'] = atac_query1.obs['cell_type'].copy()\n", "scrna_query1.obs['cell_type'] = new_cell_types_scrna_query1\n", "atac_query1.obs['cell_type'] = new_cell_types_atac_query1\n", "scrna_query1.obs['cell_type'] = scrna_query1.obs['cell_type'].astype('category')\n", "atac_query1.obs['cell_type'] = atac_query1.obs['cell_type'].astype('category')\n", "atac_query1.obs['cell_type'].cat.categories" ] }, { "cell_type": "code", "execution_count": 68, "metadata": {}, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ "Trying to set attribute `.obs` of view, copying.\n" ] }, { "data": { "text/plain": [ "Index(['ASDC', 'B cell', 'CD14 Monocyte', 'CD16 Monocyte', 'HSPC', 'ILC',\n", " 'MAIT', 'NK', 'Plasmablast', 'Platelet', 'RBC', 'T CD4 memory',\n", " 'T CD4 naive', 'T CD8 memory', 'T CD8 naive', 'Treg', 'cDC', 'gdT',\n", " 'other', 'other T', 'pDC'],\n", " dtype='object')" ] }, "execution_count": 68, "metadata": {}, "output_type": "execute_result" } ], "source": [ "scrna_query2.obs['cell_type'] = new_cell_types_scrna_query2\n", "cite_query2.obs['cell_type'] = new_cell_types_cite_query2\n", "scrna_query2.obs['cell_type'] = scrna_query2.obs['cell_type'].astype('category')\n", "cite_query2.obs['cell_type'] = cite_query2.obs['cell_type'].astype('category')\n", "cite_query2.obs['cell_type'].cat.categories" ] }, { "cell_type": "code", "execution_count": 69, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "(View of AnnData object with n_obs × n_vars = 21358 × 4000\n", " obs: 'nCount_ADT', 'nFeature_ADT', 'nCount_RNA', 'nFeature_RNA', 'orig.ident', 'lane', 'donor', 'time', 'celltype.l1', 'celltype.l2', 'celltype.l3', 'Phase', 'Batch', 'n_genes', 'n_genes_by_counts', 'log1p_n_genes_by_counts', 'total_counts', 'log1p_total_counts', 'total_counts_mt', 'log1p_total_counts_mt', 'pct_counts_mt', 'cell_type', 'sample_id', 'full_clustering', 'initial_clustering', 'Resample', 'Collection_Day', 'Sex', 'Age_interval', 'Swab_result', 'Status', 'Smoker', 'Status_on_day_collection', 'Status_on_day_collection_summary', 'Days_from_onset', 'Site', 'time_after_LPS', 'Worst_Clinical_Status', 'Outcome', 'patient_id', 'batch'\n", " var: 'names-0', 'n_cells-0', 'mt-0', 'n_cells_by_counts-0', 'mean_counts-0', 'log1p_mean_counts-0', 'pct_dropout_by_counts-0', 'total_counts-0', 'log1p_total_counts-0', 'highly_variable-0', 'means-0', 'dispersions-0', 'dispersions_norm-0', 'n_cells-1', 'mt-1', 'n_cells_by_counts-1', 'mean_counts-1', 'log1p_mean_counts-1', 'pct_dropout_by_counts-1', 'total_counts-1', 'log1p_total_counts-1', 'gene_ids-1', 'feature_types-1', 'genome-1', 'feature_types-2', 'mt-3', 'gene_ids-3', 'feature_types-3', 'genome-3', 'n_cells-0-3', 'n_cells_by_counts-0-3', 'mean_counts-0-3', 'log1p_mean_counts-0-3', 'pct_dropout_by_counts-0-3', 'total_counts-0-3', 'log1p_total_counts-0-3', 'highly_variable-0-3', 'means-0-3', 'dispersions-0-3', 'dispersions_norm-0-3', 'n_cells-1-3', 'n_cells_by_counts-1-3', 'mean_counts-1-3', 'log1p_mean_counts-1-3', 'pct_dropout_by_counts-1-3', 'total_counts-1-3', 'log1p_total_counts-1-3', 'highly_variable-1-3', 'means-1-3', 'dispersions-1-3', 'dispersions_norm-1-3', 'feature_types-4', 'highly_variable', 'means', 'dispersions', 'dispersions_norm'\n", " uns: 'log1p'\n", " layers: 'count',\n", " View of AnnData object with n_obs × n_vars = 27825 × 4000\n", " obs: 'nCount_ADT', 'nFeature_ADT', 'nCount_RNA', 'nFeature_RNA', 'orig.ident', 'lane', 'donor', 'time', 'celltype.l1', 'celltype.l2', 'celltype.l3', 'Phase', 'Batch', 'n_genes', 'n_genes_by_counts', 'log1p_n_genes_by_counts', 'total_counts', 'log1p_total_counts', 'total_counts_mt', 'log1p_total_counts_mt', 'pct_counts_mt', 'cell_type', 'sample_id', 'full_clustering', 'initial_clustering', 'Resample', 'Collection_Day', 'Sex', 'Age_interval', 'Swab_result', 'Status', 'Smoker', 'Status_on_day_collection', 'Status_on_day_collection_summary', 'Days_from_onset', 'Site', 'time_after_LPS', 'Worst_Clinical_Status', 'Outcome', 'patient_id', 'batch'\n", " var: 'names-0', 'n_cells-0', 'mt-0', 'n_cells_by_counts-0', 'mean_counts-0', 'log1p_mean_counts-0', 'pct_dropout_by_counts-0', 'total_counts-0', 'log1p_total_counts-0', 'highly_variable-0', 'means-0', 'dispersions-0', 'dispersions_norm-0', 'n_cells-1', 'mt-1', 'n_cells_by_counts-1', 'mean_counts-1', 'log1p_mean_counts-1', 'pct_dropout_by_counts-1', 'total_counts-1', 'log1p_total_counts-1', 'gene_ids-1', 'feature_types-1', 'genome-1', 'feature_types-2', 'mt-3', 'gene_ids-3', 'feature_types-3', 'genome-3', 'n_cells-0-3', 'n_cells_by_counts-0-3', 'mean_counts-0-3', 'log1p_mean_counts-0-3', 'pct_dropout_by_counts-0-3', 'total_counts-0-3', 'log1p_total_counts-0-3', 'highly_variable-0-3', 'means-0-3', 'dispersions-0-3', 'dispersions_norm-0-3', 'n_cells-1-3', 'n_cells_by_counts-1-3', 'mean_counts-1-3', 'log1p_mean_counts-1-3', 'pct_dropout_by_counts-1-3', 'total_counts-1-3', 'log1p_total_counts-1-3', 'highly_variable-1-3', 'means-1-3', 'dispersions-1-3', 'dispersions_norm-1-3', 'feature_types-4', 'highly_variable', 'means', 'dispersions', 'dispersions_norm'\n", " uns: 'log1p'\n", " layers: 'count')" ] }, "execution_count": 69, "metadata": {}, "output_type": "execute_result" } ], "source": [ "# divide hao in batches\n", "scrna1_1 = scrna1[scrna1.obs.Batch == 'Batch1']\n", "scrna1_2 = scrna1[scrna1.obs.Batch == 'Batch2']\n", "scrna1_1, scrna1_2" ] }, { "cell_type": "code", "execution_count": 70, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "(View of AnnData object with n_obs × n_vars = 21358 × 61\n", " obs: 'nCount_ADT', 'nFeature_ADT', 'nCount_RNA', 'nFeature_RNA', 'orig.ident', 'lane', 'donor', 'time', 'celltype.l1', 'celltype.l2', 'celltype.l3', 'Phase', 'Batch', 'n_genes', 'cell_type'\n", " layers: 'count',\n", " View of AnnData object with n_obs × n_vars = 27825 × 61\n", " obs: 'nCount_ADT', 'nFeature_ADT', 'nCount_RNA', 'nFeature_RNA', 'orig.ident', 'lane', 'donor', 'time', 'celltype.l1', 'celltype.l2', 'celltype.l3', 'Phase', 'Batch', 'n_genes', 'cell_type'\n", " layers: 'count')" ] }, "execution_count": 70, "metadata": {}, "output_type": "execute_result" } ], "source": [ "cite1_1 = cite1[cite1.obs.Batch == 'Batch1']\n", "cite1_2 = cite1[cite1.obs.Batch == 'Batch2']\n", "cite1_1, cite1_2" ] }, { "cell_type": "code", "execution_count": 71, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "(View of AnnData object with n_obs × n_vars = 2859 × 4000\n", " obs: 'nCount_ADT', 'nFeature_ADT', 'nCount_RNA', 'nFeature_RNA', 'orig.ident', 'lane', 'donor', 'time', 'celltype.l1', 'celltype.l2', 'celltype.l3', 'Phase', 'Batch', 'n_genes', 'n_genes_by_counts', 'log1p_n_genes_by_counts', 'total_counts', 'log1p_total_counts', 'total_counts_mt', 'log1p_total_counts_mt', 'pct_counts_mt', 'cell_type', 'sample_id', 'full_clustering', 'initial_clustering', 'Resample', 'Collection_Day', 'Sex', 'Age_interval', 'Swab_result', 'Status', 'Smoker', 'Status_on_day_collection', 'Status_on_day_collection_summary', 'Days_from_onset', 'Site', 'time_after_LPS', 'Worst_Clinical_Status', 'Outcome', 'patient_id', 'batch', 'old_cell_type'\n", " var: 'names-0', 'n_cells-0', 'mt-0', 'n_cells_by_counts-0', 'mean_counts-0', 'log1p_mean_counts-0', 'pct_dropout_by_counts-0', 'total_counts-0', 'log1p_total_counts-0', 'highly_variable-0', 'means-0', 'dispersions-0', 'dispersions_norm-0', 'n_cells-1', 'mt-1', 'n_cells_by_counts-1', 'mean_counts-1', 'log1p_mean_counts-1', 'pct_dropout_by_counts-1', 'total_counts-1', 'log1p_total_counts-1', 'gene_ids-1', 'feature_types-1', 'genome-1', 'feature_types-2', 'mt-3', 'gene_ids-3', 'feature_types-3', 'genome-3', 'n_cells-0-3', 'n_cells_by_counts-0-3', 'mean_counts-0-3', 'log1p_mean_counts-0-3', 'pct_dropout_by_counts-0-3', 'total_counts-0-3', 'log1p_total_counts-0-3', 'highly_variable-0-3', 'means-0-3', 'dispersions-0-3', 'dispersions_norm-0-3', 'n_cells-1-3', 'n_cells_by_counts-1-3', 'mean_counts-1-3', 'log1p_mean_counts-1-3', 'pct_dropout_by_counts-1-3', 'total_counts-1-3', 'log1p_total_counts-1-3', 'highly_variable-1-3', 'means-1-3', 'dispersions-1-3', 'dispersions_norm-1-3', 'feature_types-4', 'highly_variable', 'means', 'dispersions', 'dispersions_norm'\n", " uns: 'log1p'\n", " layers: 'count',\n", " View of AnnData object with n_obs × n_vars = 3475 × 4000\n", " obs: 'nCount_ADT', 'nFeature_ADT', 'nCount_RNA', 'nFeature_RNA', 'orig.ident', 'lane', 'donor', 'time', 'celltype.l1', 'celltype.l2', 'celltype.l3', 'Phase', 'Batch', 'n_genes', 'n_genes_by_counts', 'log1p_n_genes_by_counts', 'total_counts', 'log1p_total_counts', 'total_counts_mt', 'log1p_total_counts_mt', 'pct_counts_mt', 'cell_type', 'sample_id', 'full_clustering', 'initial_clustering', 'Resample', 'Collection_Day', 'Sex', 'Age_interval', 'Swab_result', 'Status', 'Smoker', 'Status_on_day_collection', 'Status_on_day_collection_summary', 'Days_from_onset', 'Site', 'time_after_LPS', 'Worst_Clinical_Status', 'Outcome', 'patient_id', 'batch', 'old_cell_type'\n", " var: 'names-0', 'n_cells-0', 'mt-0', 'n_cells_by_counts-0', 'mean_counts-0', 'log1p_mean_counts-0', 'pct_dropout_by_counts-0', 'total_counts-0', 'log1p_total_counts-0', 'highly_variable-0', 'means-0', 'dispersions-0', 'dispersions_norm-0', 'n_cells-1', 'mt-1', 'n_cells_by_counts-1', 'mean_counts-1', 'log1p_mean_counts-1', 'pct_dropout_by_counts-1', 'total_counts-1', 'log1p_total_counts-1', 'gene_ids-1', 'feature_types-1', 'genome-1', 'feature_types-2', 'mt-3', 'gene_ids-3', 'feature_types-3', 'genome-3', 'n_cells-0-3', 'n_cells_by_counts-0-3', 'mean_counts-0-3', 'log1p_mean_counts-0-3', 'pct_dropout_by_counts-0-3', 'total_counts-0-3', 'log1p_total_counts-0-3', 'highly_variable-0-3', 'means-0-3', 'dispersions-0-3', 'dispersions_norm-0-3', 'n_cells-1-3', 'n_cells_by_counts-1-3', 'mean_counts-1-3', 'log1p_mean_counts-1-3', 'pct_dropout_by_counts-1-3', 'total_counts-1-3', 'log1p_total_counts-1-3', 'highly_variable-1-3', 'means-1-3', 'dispersions-1-3', 'dispersions_norm-1-3', 'feature_types-4', 'highly_variable', 'means', 'dispersions', 'dispersions_norm'\n", " uns: 'log1p'\n", " layers: 'count')" ] }, "execution_count": 71, "metadata": {}, "output_type": "execute_result" } ], "source": [ "# divide sanger in batches\n", "scrna_query1_1 = scrna_query1[scrna_query1.obs.Batch == '0']\n", "scrna_query1_2 = scrna_query1[scrna_query1.obs.Batch == '1']\n", "scrna_query1_1, scrna_query1_2" ] }, { "cell_type": "code", "execution_count": 72, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "(View of AnnData object with n_obs × n_vars = 2859 × 32640\n", " obs: 'nb_features', 'cell_type', 'batch', 'Batch', 'old_cell_type'\n", " var: 'feature_types', 'genome', 'gene_ids-0', 'n_cells-0', 'commonness-0', 'prop_shared_cells-0', 'variability_score-0', 'gene_ids-1', 'n_cells-1', 'commonness-1', 'prop_shared_cells-1', 'variability_score-1'\n", " uns: 'log1p'\n", " layers: 'binary',\n", " View of AnnData object with n_obs × n_vars = 3475 × 32640\n", " obs: 'nb_features', 'cell_type', 'batch', 'Batch', 'old_cell_type'\n", " var: 'feature_types', 'genome', 'gene_ids-0', 'n_cells-0', 'commonness-0', 'prop_shared_cells-0', 'variability_score-0', 'gene_ids-1', 'n_cells-1', 'commonness-1', 'prop_shared_cells-1', 'variability_score-1'\n", " uns: 'log1p'\n", " layers: 'binary')" ] }, "execution_count": 72, "metadata": {}, "output_type": "execute_result" } ], "source": [ "atac_query1_1 = atac_query1[atac_query1.obs.Batch == '0']\n", "atac_query1_2 = atac_query1[atac_query1.obs.Batch == '1']\n", "atac_query1_1, atac_query1_2" ] }, { "cell_type": "code", "execution_count": 73, "metadata": {}, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ "/home/icb/litinetskaya/miniconda3/envs/multigrate/lib/python3.7/site-packages/anndata/_core/anndata.py:1210: ImplicitModificationWarning: Initializing view as actual.\n", " \"Initializing view as actual.\", ImplicitModificationWarning\n", "Trying to set attribute `.obs` of view, copying.\n", "... storing 'orig.ident' as categorical\n", "Trying to set attribute `.obs` of view, copying.\n", "... storing 'lane' as categorical\n", "Trying to set attribute `.obs` of view, copying.\n", "... storing 'donor' as categorical\n", "Trying to set attribute `.obs` of view, copying.\n", "... storing 'celltype.l1' as categorical\n", "Trying to set attribute `.obs` of view, copying.\n", "... storing 'celltype.l2' as categorical\n", "Trying to set attribute `.obs` of view, copying.\n", "... storing 'celltype.l3' as categorical\n", "Trying to set attribute `.obs` of view, copying.\n", "... storing 'Phase' as categorical\n", "Trying to set attribute `.obs` of view, copying.\n", "... storing 'Batch' as categorical\n", "Trying to set attribute `.obs` of view, copying.\n", "... storing 'sample_id' as categorical\n", "Trying to set attribute `.obs` of view, copying.\n", "... storing 'full_clustering' as categorical\n", "Trying to set attribute `.obs` of view, copying.\n", "... storing 'initial_clustering' as categorical\n", "Trying to set attribute `.obs` of view, copying.\n", "... storing 'Resample' as categorical\n", "Trying to set attribute `.obs` of view, copying.\n", "... storing 'Collection_Day' as categorical\n", "Trying to set attribute `.obs` of view, copying.\n", "... storing 'Sex' as categorical\n", "Trying to set attribute `.obs` of view, copying.\n", "... storing 'Age_interval' as categorical\n", "Trying to set attribute `.obs` of view, copying.\n", "... storing 'Swab_result' as categorical\n", "Trying to set attribute `.obs` of view, copying.\n", "... storing 'Status' as categorical\n", "Trying to set attribute `.obs` of view, copying.\n", "... storing 'Smoker' as categorical\n", "Trying to set attribute `.obs` of view, copying.\n", "... storing 'Status_on_day_collection' as categorical\n", "Trying to set attribute `.obs` of view, copying.\n", "... storing 'Status_on_day_collection_summary' as categorical\n", "Trying to set attribute `.obs` of view, copying.\n", "... storing 'Days_from_onset' as categorical\n", "Trying to set attribute `.obs` of view, copying.\n", "... storing 'Site' as categorical\n", "Trying to set attribute `.obs` of view, copying.\n", "... storing 'time_after_LPS' as categorical\n", "Trying to set attribute `.obs` of view, copying.\n", "... storing 'Worst_Clinical_Status' as categorical\n", "Trying to set attribute `.obs` of view, copying.\n", "... storing 'Outcome' as categorical\n", "Trying to set attribute `.obs` of view, copying.\n", "... storing 'patient_id' as categorical\n", "Trying to set attribute `.obs` of view, copying.\n", "... storing 'orig.ident' as categorical\n", "Trying to set attribute `.obs` of view, copying.\n", "... storing 'lane' as categorical\n", "Trying to set attribute `.obs` of view, copying.\n", "... storing 'donor' as categorical\n", "Trying to set attribute `.obs` of view, copying.\n", "... storing 'celltype.l1' as categorical\n", "Trying to set attribute `.obs` of view, copying.\n", "... storing 'celltype.l2' as categorical\n", "Trying to set attribute `.obs` of view, copying.\n", "... storing 'celltype.l3' as categorical\n", "Trying to set attribute `.obs` of view, copying.\n", "... storing 'Phase' as categorical\n", "Trying to set attribute `.obs` of view, copying.\n", "... storing 'Batch' as categorical\n", "Trying to set attribute `.obs` of view, copying.\n", "... storing 'sample_id' as categorical\n", "Trying to set attribute `.obs` of view, copying.\n", "... storing 'full_clustering' as categorical\n", "Trying to set attribute `.obs` of view, copying.\n", "... storing 'initial_clustering' as categorical\n", "Trying to set attribute `.obs` of view, copying.\n", "... storing 'Resample' as categorical\n", "Trying to set attribute `.obs` of view, copying.\n", "... storing 'Collection_Day' as categorical\n", "Trying to set attribute `.obs` of view, copying.\n", "... storing 'Sex' as categorical\n", "Trying to set attribute `.obs` of view, copying.\n", "... storing 'Age_interval' as categorical\n", "Trying to set attribute `.obs` of view, copying.\n", "... storing 'Swab_result' as categorical\n", "Trying to set attribute `.obs` of view, copying.\n", "... storing 'Status' as categorical\n", "Trying to set attribute `.obs` of view, copying.\n", "... storing 'Smoker' as categorical\n", "Trying to set attribute `.obs` of view, copying.\n", "... storing 'Status_on_day_collection' as categorical\n", "Trying to set attribute `.obs` of view, copying.\n", "... storing 'Status_on_day_collection_summary' as categorical\n", "Trying to set attribute `.obs` of view, copying.\n", "... storing 'Days_from_onset' as categorical\n", "Trying to set attribute `.obs` of view, copying.\n", "... storing 'Site' as categorical\n", "Trying to set attribute `.obs` of view, copying.\n", "... storing 'time_after_LPS' as categorical\n", "Trying to set attribute `.obs` of view, copying.\n", "... storing 'Worst_Clinical_Status' as categorical\n", "Trying to set attribute `.obs` of view, copying.\n", "... storing 'Outcome' as categorical\n", "Trying to set attribute `.obs` of view, copying.\n", "... storing 'patient_id' as categorical\n", "... storing 'orig.ident' as categorical\n", "... storing 'lane' as categorical\n", "... storing 'donor' as categorical\n", "... storing 'celltype.l1' as categorical\n", "... storing 'celltype.l2' as categorical\n", "... storing 'celltype.l3' as categorical\n", "... storing 'Phase' as categorical\n", "... storing 'Batch' as categorical\n", "... storing 'sample_id' as categorical\n", "... storing 'full_clustering' as categorical\n", "... storing 'initial_clustering' as categorical\n", "... storing 'Resample' as categorical\n", "... storing 'Collection_Day' as categorical\n", "... storing 'Sex' as categorical\n", "... storing 'Age_interval' as categorical\n", "... storing 'Swab_result' as categorical\n", "... storing 'Status' as categorical\n", "... storing 'Smoker' as categorical\n", "... storing 'Status_on_day_collection' as categorical\n", "... storing 'Status_on_day_collection_summary' as categorical\n", "... storing 'Days_from_onset' as categorical\n", "... storing 'Site' as categorical\n", "... storing 'time_after_LPS' as categorical\n", "... storing 'Worst_Clinical_Status' as categorical\n", "... storing 'Outcome' as categorical\n", "... storing 'patient_id' as categorical\n", "... storing 'old_cell_type' as categorical\n", "... storing 'old_cell_type' as categorical\n", "... storing 'orig.ident' as categorical\n", "... storing 'lane' as categorical\n", "... storing 'donor' as categorical\n", "... storing 'celltype.l1' as categorical\n", "... storing 'celltype.l2' as categorical\n", "... storing 'celltype.l3' as categorical\n", "... storing 'Phase' as categorical\n", "... storing 'Batch' as categorical\n", "... storing 'sample_id' as categorical\n", "... storing 'full_clustering' as categorical\n", "... storing 'initial_clustering' as categorical\n", "... storing 'Resample' as categorical\n", "... storing 'Collection_Day' as categorical\n", "... storing 'Sex' as categorical\n", "... storing 'Age_interval' as categorical\n", "... storing 'Swab_result' as categorical\n", "... storing 'Status' as categorical\n", "... storing 'Smoker' as categorical\n", "... storing 'Status_on_day_collection' as categorical\n", "... storing 'Status_on_day_collection_summary' as categorical\n", "... storing 'Days_from_onset' as categorical\n", "... storing 'Site' as categorical\n", "... storing 'time_after_LPS' as categorical\n", "... storing 'Worst_Clinical_Status' as categorical\n", "... storing 'Outcome' as categorical\n", "... storing 'patient_id' as categorical\n", "Trying to set attribute `.obs` of view, copying.\n", "... storing 'orig.ident' as categorical\n", "Trying to set attribute `.obs` of view, copying.\n", "... storing 'lane' as categorical\n", "Trying to set attribute `.obs` of view, copying.\n", "... storing 'donor' as categorical\n", "Trying to set attribute `.obs` of view, copying.\n", "... storing 'celltype.l1' as categorical\n", "Trying to set attribute `.obs` of view, copying.\n", "... storing 'celltype.l2' as categorical\n", "Trying to set attribute `.obs` of view, copying.\n", "... storing 'celltype.l3' as categorical\n", "Trying to set attribute `.obs` of view, copying.\n", "... storing 'Phase' as categorical\n", "Trying to set attribute `.obs` of view, copying.\n", "... storing 'Batch' as categorical\n", "Trying to set attribute `.obs` of view, copying.\n", "... storing 'sample_id' as categorical\n", "Trying to set attribute `.obs` of view, copying.\n", "... storing 'full_clustering' as categorical\n", "Trying to set attribute `.obs` of view, copying.\n", "... storing 'initial_clustering' as categorical\n", "Trying to set attribute `.obs` of view, copying.\n", "... storing 'Resample' as categorical\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "Trying to set attribute `.obs` of view, copying.\n", "... storing 'Collection_Day' as categorical\n", "Trying to set attribute `.obs` of view, copying.\n", "... storing 'Sex' as categorical\n", "Trying to set attribute `.obs` of view, copying.\n", "... storing 'Age_interval' as categorical\n", "Trying to set attribute `.obs` of view, copying.\n", "... storing 'Swab_result' as categorical\n", "Trying to set attribute `.obs` of view, copying.\n", "... storing 'Status' as categorical\n", "Trying to set attribute `.obs` of view, copying.\n", "... storing 'Smoker' as categorical\n", "Trying to set attribute `.obs` of view, copying.\n", "... storing 'Status_on_day_collection' as categorical\n", "Trying to set attribute `.obs` of view, copying.\n", "... storing 'Status_on_day_collection_summary' as categorical\n", "Trying to set attribute `.obs` of view, copying.\n", "... storing 'Days_from_onset' as categorical\n", "Trying to set attribute `.obs` of view, copying.\n", "... storing 'Site' as categorical\n", "Trying to set attribute `.obs` of view, copying.\n", "... storing 'time_after_LPS' as categorical\n", "Trying to set attribute `.obs` of view, copying.\n", "... storing 'Worst_Clinical_Status' as categorical\n", "Trying to set attribute `.obs` of view, copying.\n", "... storing 'Outcome' as categorical\n", "Trying to set attribute `.obs` of view, copying.\n", "... storing 'patient_id' as categorical\n", "Trying to set attribute `.obs` of view, copying.\n", "... storing 'old_cell_type' as categorical\n", "Trying to set attribute `.obs` of view, copying.\n", "... storing 'orig.ident' as categorical\n", "Trying to set attribute `.obs` of view, copying.\n", "... storing 'lane' as categorical\n", "Trying to set attribute `.obs` of view, copying.\n", "... storing 'donor' as categorical\n", "Trying to set attribute `.obs` of view, copying.\n", "... storing 'celltype.l1' as categorical\n", "Trying to set attribute `.obs` of view, copying.\n", "... storing 'celltype.l2' as categorical\n", "Trying to set attribute `.obs` of view, copying.\n", "... storing 'celltype.l3' as categorical\n", "Trying to set attribute `.obs` of view, copying.\n", "... storing 'Phase' as categorical\n", "Trying to set attribute `.obs` of view, copying.\n", "... storing 'Batch' as categorical\n", "Trying to set attribute `.obs` of view, copying.\n", "... storing 'sample_id' as categorical\n", "Trying to set attribute `.obs` of view, copying.\n", "... storing 'full_clustering' as categorical\n", "Trying to set attribute `.obs` of view, copying.\n", "... storing 'initial_clustering' as categorical\n", "Trying to set attribute `.obs` of view, copying.\n", "... storing 'Resample' as categorical\n", "Trying to set attribute `.obs` of view, copying.\n", "... storing 'Collection_Day' as categorical\n", "Trying to set attribute `.obs` of view, copying.\n", "... storing 'Sex' as categorical\n", "Trying to set attribute `.obs` of view, copying.\n", "... storing 'Age_interval' as categorical\n", "Trying to set attribute `.obs` of view, copying.\n", "... storing 'Swab_result' as categorical\n", "Trying to set attribute `.obs` of view, copying.\n", "... storing 'Status' as categorical\n", "Trying to set attribute `.obs` of view, copying.\n", "... storing 'Smoker' as categorical\n", "Trying to set attribute `.obs` of view, copying.\n", "... storing 'Status_on_day_collection' as categorical\n", "Trying to set attribute `.obs` of view, copying.\n", "... storing 'Status_on_day_collection_summary' as categorical\n", "Trying to set attribute `.obs` of view, copying.\n", "... storing 'Days_from_onset' as categorical\n", "Trying to set attribute `.obs` of view, copying.\n", "... storing 'Site' as categorical\n", "Trying to set attribute `.obs` of view, copying.\n", "... storing 'time_after_LPS' as categorical\n", "Trying to set attribute `.obs` of view, copying.\n", "... storing 'Worst_Clinical_Status' as categorical\n", "Trying to set attribute `.obs` of view, copying.\n", "... storing 'Outcome' as categorical\n", "Trying to set attribute `.obs` of view, copying.\n", "... storing 'patient_id' as categorical\n", "Trying to set attribute `.obs` of view, copying.\n", "... storing 'old_cell_type' as categorical\n", "... storing 'orig.ident' as categorical\n", "... storing 'lane' as categorical\n", "... storing 'donor' as categorical\n", "... storing 'celltype.l1' as categorical\n", "... storing 'celltype.l2' as categorical\n", "... storing 'celltype.l3' as categorical\n", "... storing 'Phase' as categorical\n", "... storing 'Batch' as categorical\n", "... storing 'sample_id' as categorical\n", "... storing 'full_clustering' as categorical\n", "... storing 'initial_clustering' as categorical\n", "... storing 'Resample' as categorical\n", "... storing 'Collection_Day' as categorical\n", "... storing 'Sex' as categorical\n", "... storing 'Age_interval' as categorical\n", "... storing 'Swab_result' as categorical\n", "... storing 'Status' as categorical\n", "... storing 'Smoker' as categorical\n", "... storing 'Status_on_day_collection' as categorical\n", "... storing 'Status_on_day_collection_summary' as categorical\n", "... storing 'Days_from_onset' as categorical\n", "... storing 'Site' as categorical\n", "... storing 'time_after_LPS' as categorical\n", "... storing 'Worst_Clinical_Status' as categorical\n", "... storing 'Outcome' as categorical\n", "... storing 'patient_id' as categorical\n" ] } ], "source": [ "scrna1_1.write('../data/q_to_r/scrna1.h5ad')\n", "scrna1_2.write('../data/q_to_r/scrna2.h5ad')\n", "cite1_1.write('../data/q_to_r/cite1.h5ad')\n", "cite1_2.write('../data/q_to_r/cite2.h5ad')\n", "scrna2.write('../data/q_to_r/scrna3.h5ad')\n", "atac2.write('../data/q_to_r/atac3.h5ad')\n", "scrna3.write('../data/q_to_r/scrna4.h5ad')\n", "cite3.write('../data/q_to_r/cite4.h5ad')\n", "scrna_query1_1.write('../data/q_to_r/scrna_q1.h5ad')\n", "scrna_query1_2.write('../data/q_to_r/scrna_q2.h5ad')\n", "atac_query1_1.write('../data/q_to_r/atac_q1.h5ad')\n", "atac_query1_2.write('../data/q_to_r/atac_q2.h5ad')\n", "scrna_query2.write('../data/q_to_r/scrna_q3.h5ad')\n", "cite_query2.write('../data/q_to_r/cite_q3.h5ad')" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [] } ], "metadata": { "kernelspec": { "display_name": "Python 3", "language": "python", "name": "python3" }, "language_info": { "codemirror_mode": { "name": "ipython", "version": 3 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", "version": "3.7.9" } }, "nbformat": 4, "nbformat_minor": 5 }