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---
license: cc-by-4.0
library_name: scvi-tools
tags:
- biology
- genomics
- single-cell
- model_cls_name:SCANVI
- scvi_version:0.20.3
- anndata_version:0.8.0
- modality:rna
- tissue:lung
- annotated:True
---
# Description
The single cell lung cancer atlas is a resource integrating more than 1.2 million cells from 309 patients across 29 datasets.
# Model properties
Many model properties are in the model tags. Some more are listed below.
**model_init_params**:
```json
{
"n_hidden": 128,
"n_latent": 10,
"n_layers": 2,
"dropout_rate": 0.2,
"dispersion": "gene",
"gene_likelihood": "zinb",
"latent_distribution": "normal",
"use_layer_norm": "both",
"use_batch_norm": "none",
"encode_covariates": true
}
```
**model_setup_anndata_args**:
```json
{
"labels_key": "cell_type",
"unlabeled_category": "unknown",
"layer": null,
"batch_key": "sample",
"size_factor_key": null,
"categorical_covariate_keys": null,
"continuous_covariate_keys": null
}
```
**model_summary_stats**:
| Summary Stat Key | Value |
|--------------------------|--------|
| n_batch | 505 |
| n_cells | 892296 |
| n_extra_categorical_covs | 0 |
| n_extra_continuous_covs | 0 |
| n_labels | 45 |
| n_latent_qzm | 10 |
| n_latent_qzv | 10 |
| n_vars | 6000 |
**model_data_registry**:
| Registry Key | scvi-tools Location |
|-------------------|----------------------------------------|
| X | adata.X |
| batch | adata.obs['_scvi_batch'] |
| labels | adata.obs['_scvi_labels'] |
| latent_qzm | adata.obsm['_scanvi_latent_qzm'] |
| latent_qzv | adata.obsm['_scanvi_latent_qzv'] |
| minify_type | adata.uns['_scvi_adata_minify_type'] |
| observed_lib_size | adata.obs['_scanvi_observed_lib_size'] |
**model_parent_module**: scvi.model
**data_is_minified**: True
# Training data
This is an optional link to where the training data is stored if it is too large
to host on the huggingface Model hub.
<!-- If your model is not uploaded with any data (e.g., minified data) on the Model Hub, then make
sure to provide this field if you want users to be able to access your training data. See the scvi-tools
documentation for details. -->
Training data url: https://zenodo.org/record/7227571/files/core_atlas_scanvi_model.tar.gz
# Training code
This is an optional link to the code used to train the model.
Training code url: https://github.com/icbi-lab/luca
# References
High-resolution single-cell atlas reveals diversity and plasticity of tissue-resident neutrophils in non-small cell lung cancer. S Salcher, G Sturm, L Horvath, G Untergasser, C Kuempers, G Fotakis, E Panizzolo, A Martowicz, M Trebo, G Pall, G Gamerith, M Sykora, F Augustin, K Schmitz, F Finotello, D Rieder, S Perner, S Sopper, D Wolf, A Pircher, Z Trajanoski. Cancer Cell. 2022; 40 (12): 1503-1520.e8. https: //doi.org/10.1016/j.ccell.2022.10.008 |