Description

The first integrated, universal transcriptomic reference of the human lung on the single-cell level. For more details, see https: //github.com/LungCellAtlas/HLCA.

Model properties

Many model properties are in the model tags. Some more are listed below.

model_init_params:

{
    "n_hidden": 128,
    "n_latent": 30,
    "n_layers": 2,
    "dropout_rate": 0.1,
    "dispersion": "gene",
    "gene_likelihood": "nb",
    "encode_covariates": true,
    "deeply_inject_covariates": false,
    "use_layer_norm": "both",
    "use_batch_norm": "none"
}

model_setup_anndata_args:

{
    "labels_key": "scanvi_label",
    "unlabeled_category": "unlabeled",
    "layer": null,
    "batch_key": "dataset",
    "size_factor_key": null,
    "categorical_covariate_keys": null,
    "continuous_covariate_keys": null
}

model_summary_stats:

Summary Stat Key	Value
n_batch	14
n_cells	584884
n_extra_categorical_covs	0
n_extra_continuous_covs	0
n_labels	29
n_latent_qzm	30
n_latent_qzv	30
n_vars	2000

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://cellxgene.cziscience.com/e/066943a2-fdac-4b29-b348-40cede398e4e.cxg/

Training code

This is an optional link to the code used to train the model.

Training code url: https://github.com/LungCellAtlas/HLCA_reproducibility

References

An integrated cell atlas of the human lung in health and disease. L Sikkema, D Strobl, L Zappia, E Madissoon, NS Markov, L Zaragosi, M Ansari, M Arguel, L Apperloo, C Bécavin, M Berg, E Chichelnitskiy, M Chung, A Collin, ACA Gay, B Hooshiar Kashani, M Jain, T Kapellos, TM Kole, C Mayr, M von Papen, L Peter, C Ramírez-Suástegui, J Schniering, C Taylor, T Walzthoeni, C Xu, LT Bui, C de Donno, L Dony, M Guo, AJ Gutierrez, L Heumos, N Huang, I Ibarra, N Jackson, P Kadur Lakshminarasimha Murthy, M Lotfollahi, T Tabib, C Talavera-Lopez, K Travaglini, A Wilbrey-Clark, KB Worlock, M Yoshida, Lung Biological Network Consortium, T Desai, O Eickelberg, C Falk, N Kaminski, M Krasnow, R Lafyatis, M Nikolíc, J Powell, J Rajagopal, O Rozenblatt-Rosen, MA Seibold, D Sheppard, D Shepherd, SA Teichmann, A Tsankov, J Whitsett, Y Xu, NE Banovich, P Barbry, TE Duong, KB Meyer, JA Kropski, D Pe’er, HB Schiller, PR Tata, JL Schultze, AV Misharin, MC Nawijn, MD Luecken, F Theis. bioRxiv 2022.03.10.483747; doi: https: //doi.org/10.1101/2022.03.10.483747