diff --git a/multimodal/totalVI_reference_mapping.ipynb b/multimodal/totalVI_reference_mapping.ipynb
index 818ed5f..58c4720 100644
--- a/multimodal/totalVI_reference_mapping.ipynb
+++ b/multimodal/totalVI_reference_mapping.ipynb
@@ -29,10 +29,10 @@
"execution_count": 1,
"metadata": {
"execution": {
- "iopub.execute_input": "2024-02-12T17:43:53.325765Z",
- "iopub.status.busy": "2024-02-12T17:43:53.325617Z",
- "iopub.status.idle": "2024-02-12T17:43:56.090439Z",
- "shell.execute_reply": "2024-02-12T17:43:56.089954Z"
+ "iopub.execute_input": "2024-09-22T12:44:44.696435Z",
+ "iopub.status.busy": "2024-09-22T12:44:44.696336Z",
+ "iopub.status.idle": "2024-09-22T12:44:45.744359Z",
+ "shell.execute_reply": "2024-09-22T12:44:45.744037Z"
}
},
"outputs": [
@@ -40,7 +40,7 @@
"name": "stdout",
"output_type": "stream",
"text": [
- "\u001b[33mWARNING: Running pip as the 'root' user can result in broken permissions and conflicting behaviour with the system package manager. It is recommended to use a virtual environment instead: https://pip.pypa.io/warnings/venv\u001b[0m\u001b[33m\r\n",
+ "\u001b[33mWARNING: Running pip as the 'root' user can result in broken permissions and conflicting behaviour with the system package manager, possibly rendering your system unusable.It is recommended to use a virtual environment instead: https://pip.pypa.io/warnings/venv. Use the --root-user-action option if you know what you are doing and want to suppress this warning.\u001b[0m\u001b[33m\r\n",
"\u001b[0m"
]
},
@@ -48,7 +48,7 @@
"name": "stderr",
"output_type": "stream",
"text": [
- "/env/lib/python3.11/site-packages/scvi_colab/_core.py:41: UserWarning: \n",
+ "/usr/local/lib/python3.12/site-packages/scvi_colab/_core.py:41: UserWarning: \n",
" Not currently in Google Colab environment.\n",
"\n",
" Please run with `run_outside_colab=True` to override.\n",
@@ -74,15 +74,38 @@
"base_uri": "https://localhost:8080/"
},
"execution": {
- "iopub.execute_input": "2024-02-12T17:43:56.092344Z",
- "iopub.status.busy": "2024-02-12T17:43:56.092209Z",
- "iopub.status.idle": "2024-02-12T17:44:05.400947Z",
- "shell.execute_reply": "2024-02-12T17:44:05.400420Z"
+ "iopub.execute_input": "2024-09-22T12:44:45.745572Z",
+ "iopub.status.busy": "2024-09-22T12:44:45.745469Z",
+ "iopub.status.idle": "2024-09-22T12:45:01.978641Z",
+ "shell.execute_reply": "2024-09-22T12:45:01.978199Z"
},
"id": "BSRGJ42EguIG",
"outputId": "afc7183c-1818-4954-a3d5-9feaa4e5b077"
},
- "outputs": [],
+ "outputs": [
+ {
+ "name": "stderr",
+ "output_type": "stream",
+ "text": [
+ "/usr/local/lib/python3.12/site-packages/leidenalg/VertexPartition.py:388: SyntaxWarning: invalid escape sequence '\\m'\n",
+ " \"\"\" Implements modularity. This quality function is well-defined only for positive edge weights.\n",
+ "/usr/local/lib/python3.12/site-packages/leidenalg/VertexPartition.py:761: SyntaxWarning: invalid escape sequence '\\m'\n",
+ " \"\"\" Implements Reichardt and Bornholdt's Potts model with a configuration null model.\n",
+ "/usr/local/lib/python3.12/site-packages/leidenalg/Optimiser.py:7: SyntaxWarning: invalid escape sequence '\\g'\n",
+ " \"\"\" Class for doing community detection using the Leiden algorithm.\n",
+ "/usr/local/lib/python3.12/site-packages/leidenalg/Optimiser.py:305: SyntaxWarning: invalid escape sequence '\\s'\n",
+ " \"\"\" Optimise the given partitions simultaneously.\n"
+ ]
+ },
+ {
+ "name": "stderr",
+ "output_type": "stream",
+ "text": [
+ "/usr/local/lib/python3.12/site-packages/pyro/ops/stats.py:514: SyntaxWarning: invalid escape sequence '\\g'\n",
+ " \"\"\"\n"
+ ]
+ }
+ ],
"source": [
"import os\n",
"import tempfile\n",
@@ -115,10 +138,10 @@
"execution_count": 3,
"metadata": {
"execution": {
- "iopub.execute_input": "2024-02-12T17:44:05.403312Z",
- "iopub.status.busy": "2024-02-12T17:44:05.402966Z",
- "iopub.status.idle": "2024-02-12T17:44:05.406885Z",
- "shell.execute_reply": "2024-02-12T17:44:05.406411Z"
+ "iopub.execute_input": "2024-09-22T12:45:01.980832Z",
+ "iopub.status.busy": "2024-09-22T12:45:01.980438Z",
+ "iopub.status.idle": "2024-09-22T12:45:01.984200Z",
+ "shell.execute_reply": "2024-09-22T12:45:01.983919Z"
}
},
"outputs": [
@@ -133,7 +156,7 @@
"name": "stdout",
"output_type": "stream",
"text": [
- "Last run with scvi-tools version: 1.1.0\n"
+ "Last run with scvi-tools version: 1.1.6\n"
]
}
],
@@ -159,10 +182,10 @@
"base_uri": "https://localhost:8080/"
},
"execution": {
- "iopub.execute_input": "2024-02-12T17:44:05.408517Z",
- "iopub.status.busy": "2024-02-12T17:44:05.408388Z",
- "iopub.status.idle": "2024-02-12T17:44:05.418134Z",
- "shell.execute_reply": "2024-02-12T17:44:05.417693Z"
+ "iopub.execute_input": "2024-09-22T12:45:01.985143Z",
+ "iopub.status.busy": "2024-09-22T12:45:01.985049Z",
+ "iopub.status.idle": "2024-09-22T12:45:01.997513Z",
+ "shell.execute_reply": "2024-09-22T12:45:01.997172Z"
},
"id": "9bnMRRihguIJ",
"outputId": "d71ccbb9-41c7-451a-8b76-2add1396ab28"
@@ -207,10 +230,10 @@
"base_uri": "https://localhost:8080/"
},
"execution": {
- "iopub.execute_input": "2024-02-12T17:44:05.419836Z",
- "iopub.status.busy": "2024-02-12T17:44:05.419692Z",
- "iopub.status.idle": "2024-02-12T17:46:05.952898Z",
- "shell.execute_reply": "2024-02-12T17:46:05.952435Z"
+ "iopub.execute_input": "2024-09-22T12:45:01.999218Z",
+ "iopub.status.busy": "2024-09-22T12:45:01.999124Z",
+ "iopub.status.idle": "2024-09-22T12:46:00.177869Z",
+ "shell.execute_reply": "2024-09-22T12:46:00.177450Z"
},
"id": "dqFHL3TugIPi",
"outputId": "b1471da0-8e2d-4e69-8479-a43985e7dd75"
@@ -220,12441 +243,2353 @@
"name": "stdout",
"output_type": "stream",
"text": [
- "\u001b[34mINFO \u001b[0m Downloading file at \u001b[35m/tmp/tmpkyrakh_v/\u001b[0m\u001b[95mpbmc_seurat_v4.h5ad\u001b[0m \n"
+ "\u001b[34mINFO \u001b[0m Downloading file at \u001b[35m/tmp/tmpt249ad5o/\u001b[0m\u001b[95mpbmc_seurat_v4.h5ad\u001b[0m \n"
]
},
{
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "\r",
- "Downloading...: 0%| | 0/1002579.0 [00:00, ?it/s]"
- ]
+ "data": {
+ "application/vnd.jupyter.widget-view+json": {
+ "model_id": "fde8f99df0234a3aaff2630c116fec60",
+ "version_major": 2,
+ "version_minor": 0
+ },
+ "text/plain": [
+ "Downloading...: 0%| | 0/1002579.0 [00:00, ?it/s]"
+ ]
+ },
+ "metadata": {},
+ "output_type": "display_data"
+ }
+ ],
+ "source": [
+ "adata = scvi.data.pbmc_seurat_v4_cite_seq(\n",
+ " save_path=save_dir.name,\n",
+ " mask_protein_batches=5,\n",
+ ")\n",
+ "adata.layers[\"counts\"] = adata.X.copy()\n",
+ "sc.pp.normalize_total(adata, target_sum=1e4)\n",
+ "sc.pp.log1p(adata)\n",
+ "adata.raw = adata"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 6,
+ "metadata": {
+ "execution": {
+ "iopub.execute_input": "2024-09-22T12:46:00.179624Z",
+ "iopub.status.busy": "2024-09-22T12:46:00.179500Z",
+ "iopub.status.idle": "2024-09-22T12:46:09.759666Z",
+ "shell.execute_reply": "2024-09-22T12:46:09.759276Z"
},
+ "id": "0x-haM2IxV6d"
+ },
+ "outputs": [
{
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "\r",
- "Downloading...: 0%| | 17/1002579.0 [00:00<2:21:51, 117.78it/s]"
- ]
+ "data": {
+ "text/plain": [
+ "AnnData object with n_obs × n_vars = 152094 × 4000\n",
+ " obs: 'nCount_ADT', 'nFeature_ADT', 'nCount_RNA', 'nFeature_RNA', 'orig.ident', 'lane', 'donor', 'time', 'celltype.l1', 'celltype.l2', 'celltype.l3', 'Phase', 'nCount_SCT', 'nFeature_SCT', 'X_index', 'total_counts', 'total_counts_mt', 'pct_counts_mt', 'Protein log library size', 'Number proteins detected', 'RNA log library size'\n",
+ " var: 'mt', 'highly_variable', 'highly_variable_rank', 'means', 'variances', 'variances_norm', 'highly_variable_nbatches'\n",
+ " uns: 'log1p', 'hvg'\n",
+ " obsm: 'protein_counts'\n",
+ " layers: 'counts'"
+ ]
+ },
+ "execution_count": 6,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "sc.pp.highly_variable_genes(\n",
+ " adata,\n",
+ " n_top_genes=4000,\n",
+ " flavor=\"seurat_v3\",\n",
+ " batch_key=\"orig.ident\",\n",
+ " subset=True,\n",
+ " layer=\"counts\",\n",
+ ")\n",
+ "adata"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 7,
+ "metadata": {
+ "colab": {
+ "base_uri": "https://localhost:8080/"
},
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "\r",
- "Downloading...: 0%| | 52/1002579.0 [00:00<1:30:31, 184.57it/s]"
- ]
+ "execution": {
+ "iopub.execute_input": "2024-09-22T12:46:09.760741Z",
+ "iopub.status.busy": "2024-09-22T12:46:09.760620Z",
+ "iopub.status.idle": "2024-09-22T12:46:09.842413Z",
+ "shell.execute_reply": "2024-09-22T12:46:09.842003Z"
},
+ "id": "c3lroWyRxfii",
+ "outputId": "ba9500d5-f649-4530-984a-e3ae422383b9"
+ },
+ "outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
- "\r",
- "Downloading...: 0%| | 87/1002579.0 [00:00<1:21:30, 204.99it/s]"
+ "\u001b[34mINFO \u001b[0m Using column names from columns of adata.obsm\u001b[1m[\u001b[0m\u001b[32m'protein_counts'\u001b[0m\u001b[1m]\u001b[0m \n"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
- "\r",
- "Downloading...: 0%| | 209/1002579.0 [00:00<37:55, 440.58it/s] "
+ "\u001b[34mINFO \u001b[0m Found batches with missing protein expression \n"
]
+ }
+ ],
+ "source": [
+ "TOTALVI.setup_anndata(\n",
+ " adata,\n",
+ " layer=\"counts\",\n",
+ " batch_key=\"orig.ident\",\n",
+ " protein_expression_obsm_key=\"protein_counts\",\n",
+ ")"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {
+ "id": "BZ7w6shdguIT"
+ },
+ "source": [
+ "### Prepare and run model\n",
+ "\n",
+ "Here we use the scArches-specific totalVI parameters, as shown in the scArches tutorial.\n",
+ "\n",
+ "Rather than training the model here, which would take 1 hour, we instead download a pretrained object."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 8,
+ "metadata": {
+ "execution": {
+ "iopub.execute_input": "2024-09-22T12:46:09.843402Z",
+ "iopub.status.busy": "2024-09-22T12:46:09.843299Z",
+ "iopub.status.idle": "2024-09-22T12:46:09.845031Z",
+ "shell.execute_reply": "2024-09-22T12:46:09.844796Z"
},
+ "id": "BtdA92q3Gggs"
+ },
+ "outputs": [],
+ "source": [
+ "# arches_params = dict(\n",
+ "# use_layer_norm=\"both\",\n",
+ "# use_batch_norm=\"none\",\n",
+ "# n_layers_decoder=2,\n",
+ "# n_layers_encoder=2,\n",
+ "# )\n",
+ "\n",
+ "# model = TOTALVI(adata, **arches_params)\n",
+ "# model.train(max_epochs=250)\n",
+ "\n",
+ "# model_dir = os.path.join(save_dir.name, \"seurat_reference_model\")\n",
+ "# model.save(model_dir, overwrite=True)"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 9,
+ "metadata": {
+ "execution": {
+ "iopub.execute_input": "2024-09-22T12:46:09.846015Z",
+ "iopub.status.busy": "2024-09-22T12:46:09.845821Z",
+ "iopub.status.idle": "2024-09-22T12:46:09.847715Z",
+ "shell.execute_reply": "2024-09-22T12:46:09.847480Z"
+ }
+ },
+ "outputs": [],
+ "source": [
+ "def download_model(save_path: str, fname: str = \"legacy_seurat_reference_model\"):\n",
+ " \"\"\"Download the pre-trained model.\"\"\"\n",
+ " paths = pooch.retrieve(\n",
+ " url=\"https://figshare.com/ndownloader/files/30929902\",\n",
+ " known_hash=\"422706d6af4ec6b3b91f547d7e8c97812b86a548e0d19b1d85d9cfed686a5130\",\n",
+ " fname=fname,\n",
+ " path=save_path,\n",
+ " processor=pooch.Unzip(),\n",
+ " )\n",
+ " paths.sort()\n",
+ " return str(Path(paths[0]).parent)"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 10,
+ "metadata": {
+ "execution": {
+ "iopub.execute_input": "2024-09-22T12:46:09.848523Z",
+ "iopub.status.busy": "2024-09-22T12:46:09.848437Z",
+ "iopub.status.idle": "2024-09-22T12:46:15.811245Z",
+ "shell.execute_reply": "2024-09-22T12:46:15.810858Z"
+ }
+ },
+ "outputs": [
{
- "name": "stdout",
+ "name": "stderr",
"output_type": "stream",
"text": [
- "\r",
- "Downloading...: 0%| | 452/1002579.0 [00:00<19:30, 856.44it/s]"
+ "Downloading data from 'https://figshare.com/ndownloader/files/30929902' to file '/tmp/tmpt249ad5o/legacy_seurat_reference_model'.\n"
]
},
{
- "name": "stdout",
+ "name": "stderr",
"output_type": "stream",
"text": [
- "\r",
- "Downloading...: 0%| | 922/1002579.0 [00:00<10:21, 1612.34it/s]"
+ "Unzipping contents of '/tmp/tmpt249ad5o/legacy_seurat_reference_model' to '/tmp/tmpt249ad5o/legacy_seurat_reference_model.unzip'\n"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
- "\r",
- "Downloading...: 0%| | 1600/1002579.0 [00:01<06:41, 2493.69it/s]"
+ "\u001b[34mINFO \u001b[0m File \u001b[35m/tmp/tmpt249ad5o/seurat_reference_model/\u001b[0m\u001b[95mmodel.pt\u001b[0m already downloaded \n"
]
},
{
- "name": "stdout",
+ "name": "stderr",
"output_type": "stream",
"text": [
- "\r",
- "Downloading...: 0%| | 1846/1002579.0 [00:01<07:26, 2243.08it/s]"
+ "/usr/local/lib/python3.12/site-packages/scvi/model/base/_save_load.py:43: FutureWarning: You are using `torch.load` with `weights_only=False` (the current default value), which uses the default pickle module implicitly. It is possible to construct malicious pickle data which will execute arbitrary code during unpickling (See https://github.com/pytorch/pytorch/blob/main/SECURITY.md#untrusted-models for more details). In a future release, the default value for `weights_only` will be flipped to `True`. This limits the functions that could be executed during unpickling. Arbitrary objects will no longer be allowed to be loaded via this mode unless they are explicitly allowlisted by the user via `torch.serialization.add_safe_globals`. We recommend you start setting `weights_only=True` for any use case where you don't have full control of the loaded file. Please open an issue on GitHub for any issues related to this experimental feature.\n",
+ " model_state_dict = torch.load(model_path, map_location=\"cpu\")\n",
+ "/usr/local/lib/python3.12/site-packages/scvi/model/base/_save_load.py:76: FutureWarning: You are using `torch.load` with `weights_only=False` (the current default value), which uses the default pickle module implicitly. It is possible to construct malicious pickle data which will execute arbitrary code during unpickling (See https://github.com/pytorch/pytorch/blob/main/SECURITY.md#untrusted-models for more details). In a future release, the default value for `weights_only` will be flipped to `True`. This limits the functions that could be executed during unpickling. Arbitrary objects will no longer be allowed to be loaded via this mode unless they are explicitly allowlisted by the user via `torch.serialization.add_safe_globals`. We recommend you start setting `weights_only=True` for any use case where you don't have full control of the loaded file. Please open an issue on GitHub for any issues related to this experimental feature.\n",
+ " model = torch.load(model_path, map_location=map_location)\n"
]
},
{
- "name": "stdout",
+ "name": "stderr",
"output_type": "stream",
"text": [
- "\r",
- "Downloading...: 0%| | 3096/1002579.0 [00:01<04:03, 4113.04it/s]"
+ "/usr/local/lib/python3.12/site-packages/scvi/model/base/_base_model.py:708: UserWarning: `var_names` for the loaded `adata` does not match those of the `adata` used to train the model. For valid results, the former should match the latter.\n",
+ " _validate_var_names(adata, var_names)\n"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
- "\r",
- "Downloading...: 0%| | 4194/1002579.0 [00:01<03:17, 5061.58it/s]"
+ "\u001b[34mINFO \u001b[0m Found batches with missing protein expression \n"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
- "\r",
- "Downloading...: 1%| | 5341/1002579.0 [00:01<02:51, 5807.75it/s]"
+ "\u001b[34mINFO \u001b[0m Computing empirical prior initialization for protein background. \n"
]
},
{
- "name": "stdout",
+ "name": "stderr",
"output_type": "stream",
"text": [
- "\r",
- "Downloading...: 1%| | 6521/1002579.0 [00:01<02:36, 6347.00it/s]"
+ "/usr/local/lib/python3.12/site-packages/scvi/model/base/_save_load.py:136: UserWarning: Some proteins have all 0 counts in some batches. These proteins will be treated as missing measurements; however, this can occur due to experimental design/biology. Reinitialize the model with `override_missing_proteins=True`,to override this behavior.\n",
+ " model = cls(adata, **non_kwargs, **kwargs)\n"
]
- },
+ }
+ ],
+ "source": [
+ "legacy_model_dir = download_model(save_dir.name)\n",
+ "model_dir = os.path.join(save_dir.name, \"seurat_reference_model\")\n",
+ "TOTALVI.convert_legacy_save(legacy_model_dir, model_dir)\n",
+ "\n",
+ "model = TOTALVI.load(model_dir, adata=adata)"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 11,
+ "metadata": {
+ "execution": {
+ "iopub.execute_input": "2024-09-22T12:46:15.812685Z",
+ "iopub.status.busy": "2024-09-22T12:46:15.812574Z",
+ "iopub.status.idle": "2024-09-22T12:46:15.826411Z",
+ "shell.execute_reply": "2024-09-22T12:46:15.826176Z"
+ }
+ },
+ "outputs": [
{
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "\r",
- "Downloading...: 1%| | 7717/1002579.0 [00:01<02:26, 6787.27it/s]"
- ]
+ "data": {
+ "text/html": [
+ "
Anndata setup with scvi-tools version 1.1.6.\n",
+ "
\n"
+ ],
+ "text/plain": [
+ "Anndata setup with scvi-tools version \u001b[1;36m1.1\u001b[0m.\u001b[1;36m6\u001b[0m.\n"
+ ]
+ },
+ "metadata": {},
+ "output_type": "display_data"
},
{
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "\r",
- "Downloading...: 1%| | 8831/1002579.0 [00:02<02:26, 6774.46it/s]"
- ]
+ "data": {
+ "text/html": [
+ "\n",
+ "
\n"
+ ],
+ "text/plain": [
+ "\n"
+ ]
+ },
+ "metadata": {},
+ "output_type": "display_data"
},
{
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "\r",
- "Downloading...: 1%| | 10125/1002579.0 [00:02<02:12, 7480.88it/s]"
- ]
+ "data": {
+ "text/html": [
+ "Setup via `TOTALVI.setup_anndata` with arguments:\n",
+ "
\n"
+ ],
+ "text/plain": [
+ "Setup via `TOTALVI.setup_anndata` with arguments:\n"
+ ]
+ },
+ "metadata": {},
+ "output_type": "display_data"
},
{
- "name": "stdout",
- "output_type": "stream",
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- ]
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+ "│ 'layer': 'counts',\n",
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+ " Summary Statistics \n",
+ "┏━━━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━┓\n",
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+ "│ n_batch │ 24 │\n",
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+ " Data Registry \n",
+ "┏━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┓\n",
+ "┃ Registry Key ┃ scvi-tools Location ┃\n",
+ "┡━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┩\n",
+ "│ X │ adata.layers['counts'] │\n",
+ "│ batch │ adata.obs['_scvi_batch'] │\n",
+ "│ labels │ adata.obs['_scvi_labels'] │\n",
+ "│ proteins │ adata.obsm['protein_counts'] │\n",
+ "└──────────────┴──────────────────────────────┘\n",
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+ "┃\u001b[1m \u001b[0m\u001b[1mRegistry Key\u001b[0m\u001b[1m \u001b[0m┃\u001b[1m \u001b[0m\u001b[1m scvi-tools Location \u001b[0m\u001b[1m \u001b[0m┃\n",
+ "┡━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┩\n",
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+ "└──────────────┴──────────────────────────────┘\n"
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+ " labels State Registry \n",
+ "┏━━━━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━━━━━┓\n",
+ "┃ Source Location ┃ Categories ┃ scvi-tools Encoding ┃\n",
+ "┡━━━━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━━━┩\n",
+ "│ adata.obs['_scvi_labels'] │ 0 │ 0 │\n",
+ "└───────────────────────────┴────────────┴─────────────────────┘\n",
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+ "┡━━━━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━━━┩\n",
+ "│\u001b[38;5;33m \u001b[0m\u001b[38;5;33madata.obs['_scvi_labels']\u001b[0m\u001b[38;5;33m \u001b[0m│\u001b[32m \u001b[0m\u001b[32m 0 \u001b[0m\u001b[32m \u001b[0m│\u001b[38;5;128m \u001b[0m\u001b[38;5;128m 0 \u001b[0m\u001b[38;5;128m \u001b[0m│\n",
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+ " batch State Registry \n",
+ "┏━━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━━━━━┓\n",
+ "┃ Source Location ┃ Categories ┃ scvi-tools Encoding ┃\n",
+ "┡━━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━━━┩\n",
+ "│ adata.obs['orig.ident'] │ P1_0 │ 0 │\n",
+ "│ │ P1_3 │ 1 │\n",
+ "│ │ P1_7 │ 2 │\n",
+ "│ │ P2_0 │ 3 │\n",
+ "│ │ P2_3 │ 4 │\n",
+ "│ │ P2_7 │ 5 │\n",
+ "│ │ P3_0 │ 6 │\n",
+ "│ │ P3_3 │ 7 │\n",
+ "│ │ P3_7 │ 8 │\n",
+ "│ │ P4_0 │ 9 │\n",
+ "│ │ P4_3 │ 10 │\n",
+ "│ │ P4_7 │ 11 │\n",
+ "│ │ P5_0 │ 12 │\n",
+ "│ │ P5_3 │ 13 │\n",
+ "│ │ P5_7 │ 14 │\n",
+ "│ │ P6_0 │ 15 │\n",
+ "│ │ P6_3 │ 16 │\n",
+ "│ │ P6_7 │ 17 │\n",
+ "│ │ P7_0 │ 18 │\n",
+ "│ │ P7_3 │ 19 │\n",
+ "│ │ P7_7 │ 20 │\n",
+ "│ │ P8_0 │ 21 │\n",
+ "│ │ P8_3 │ 22 │\n",
+ "│ │ P8_7 │ 23 │\n",
+ "└─────────────────────────┴────────────┴─────────────────────┘\n",
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+ "│\u001b[38;5;33m \u001b[0m\u001b[38;5;33m \u001b[0m\u001b[38;5;33m \u001b[0m│\u001b[32m \u001b[0m\u001b[32m P4_0 \u001b[0m\u001b[32m \u001b[0m│\u001b[38;5;128m \u001b[0m\u001b[38;5;128m 9 \u001b[0m\u001b[38;5;128m \u001b[0m│\n",
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+ "│\u001b[38;5;33m \u001b[0m\u001b[38;5;33m \u001b[0m\u001b[38;5;33m \u001b[0m│\u001b[32m \u001b[0m\u001b[32m P5_0 \u001b[0m\u001b[32m \u001b[0m│\u001b[38;5;128m \u001b[0m\u001b[38;5;128m 12 \u001b[0m\u001b[38;5;128m \u001b[0m│\n",
+ "│\u001b[38;5;33m \u001b[0m\u001b[38;5;33m \u001b[0m\u001b[38;5;33m \u001b[0m│\u001b[32m \u001b[0m\u001b[32m P5_3 \u001b[0m\u001b[32m \u001b[0m│\u001b[38;5;128m \u001b[0m\u001b[38;5;128m 13 \u001b[0m\u001b[38;5;128m \u001b[0m│\n",
+ "│\u001b[38;5;33m \u001b[0m\u001b[38;5;33m \u001b[0m\u001b[38;5;33m \u001b[0m│\u001b[32m \u001b[0m\u001b[32m P5_7 \u001b[0m\u001b[32m \u001b[0m│\u001b[38;5;128m \u001b[0m\u001b[38;5;128m 14 \u001b[0m\u001b[38;5;128m \u001b[0m│\n",
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+ "│\u001b[38;5;33m \u001b[0m\u001b[38;5;33m \u001b[0m\u001b[38;5;33m \u001b[0m│\u001b[32m \u001b[0m\u001b[32m P6_3 \u001b[0m\u001b[32m \u001b[0m│\u001b[38;5;128m \u001b[0m\u001b[38;5;128m 16 \u001b[0m\u001b[38;5;128m \u001b[0m│\n",
+ "│\u001b[38;5;33m \u001b[0m\u001b[38;5;33m \u001b[0m\u001b[38;5;33m \u001b[0m│\u001b[32m \u001b[0m\u001b[32m P6_7 \u001b[0m\u001b[32m \u001b[0m│\u001b[38;5;128m \u001b[0m\u001b[38;5;128m 17 \u001b[0m\u001b[38;5;128m \u001b[0m│\n",
+ "│\u001b[38;5;33m \u001b[0m\u001b[38;5;33m \u001b[0m\u001b[38;5;33m \u001b[0m│\u001b[32m \u001b[0m\u001b[32m P7_0 \u001b[0m\u001b[32m \u001b[0m│\u001b[38;5;128m \u001b[0m\u001b[38;5;128m 18 \u001b[0m\u001b[38;5;128m \u001b[0m│\n",
+ "│\u001b[38;5;33m \u001b[0m\u001b[38;5;33m \u001b[0m\u001b[38;5;33m \u001b[0m│\u001b[32m \u001b[0m\u001b[32m P7_3 \u001b[0m\u001b[32m \u001b[0m│\u001b[38;5;128m \u001b[0m\u001b[38;5;128m 19 \u001b[0m\u001b[38;5;128m \u001b[0m│\n",
+ "│\u001b[38;5;33m \u001b[0m\u001b[38;5;33m \u001b[0m\u001b[38;5;33m \u001b[0m│\u001b[32m \u001b[0m\u001b[32m P7_7 \u001b[0m\u001b[32m \u001b[0m│\u001b[38;5;128m \u001b[0m\u001b[38;5;128m 20 \u001b[0m\u001b[38;5;128m \u001b[0m│\n",
+ "│\u001b[38;5;33m \u001b[0m\u001b[38;5;33m \u001b[0m\u001b[38;5;33m \u001b[0m│\u001b[32m \u001b[0m\u001b[32m P8_0 \u001b[0m\u001b[32m \u001b[0m│\u001b[38;5;128m \u001b[0m\u001b[38;5;128m 21 \u001b[0m\u001b[38;5;128m \u001b[0m│\n",
+ "│\u001b[38;5;33m \u001b[0m\u001b[38;5;33m \u001b[0m\u001b[38;5;33m \u001b[0m│\u001b[32m \u001b[0m\u001b[32m P8_3 \u001b[0m\u001b[32m \u001b[0m│\u001b[38;5;128m \u001b[0m\u001b[38;5;128m 22 \u001b[0m\u001b[38;5;128m \u001b[0m│\n",
+ "│\u001b[38;5;33m \u001b[0m\u001b[38;5;33m \u001b[0m\u001b[38;5;33m \u001b[0m│\u001b[32m \u001b[0m\u001b[32m P8_7 \u001b[0m\u001b[32m \u001b[0m│\u001b[38;5;128m \u001b[0m\u001b[38;5;128m 23 \u001b[0m\u001b[38;5;128m \u001b[0m│\n",
+ "└─────────────────────────┴────────────┴─────────────────────┘\n"
+ ]
+ },
+ "metadata": {},
+ "output_type": "display_data"
+ }
+ ],
+ "source": [
+ "model.view_anndata_setup()"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 12,
+ "metadata": {
+ "colab": {
+ "base_uri": "https://localhost:8080/",
+ "height": 334
},
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+ "execution": {
+ "iopub.execute_input": "2024-09-22T12:46:15.827424Z",
+ "iopub.status.busy": "2024-09-22T12:46:15.827220Z",
+ "iopub.status.idle": "2024-09-22T12:46:15.945678Z",
+ "shell.execute_reply": "2024-09-22T12:46:15.945446Z"
},
+ "id": "shhdA2iKguIZ",
+ "outputId": "9940ff38-d8e8-4887-8b3e-0f60e4a02463"
+ },
+ "outputs": [
{
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- "text": [
- "\r",
- "Downloading...: 2%|▏ | 19317/1002579.0 [00:03<02:37, 6252.31it/s]"
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+ "data": {
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+ "execution_count": 12,
+ "metadata": {},
+ "output_type": "execute_result"
},
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",
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+ "