non-small-cell-lung-cancer

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:

model_data_registry:

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