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:

{
    "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:

{
    "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.

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