Description
Tabula sapiens. An across organ dataset of cell-types in human tissues.
Model properties
Many model properties are in the model tags. Some more are listed below.
model_init_params:
{
"n_hidden": 128,
"n_latent": 20,
"n_layers": 3,
"dropout_rate": 0.05,
"dispersion": "gene",
"gene_likelihood": "nb",
"latent_distribution": "normal",
"use_batch_norm": "none",
"use_layer_norm": "both",
"encode_covariates": true
}
model_setup_anndata_args:
{
"layer": null,
"batch_key": "donor_assay",
"labels_key": "cell_ontology_class",
"size_factor_key": null,
"categorical_covariate_keys": null,
"continuous_covariate_keys": null
}
model_summary_stats:
| Summary Stat Key | Value |
|---|---|
| n_batch | 6 |
| n_cells | 34766 |
| n_extra_categorical_covs | 0 |
| n_extra_continuous_covs | 0 |
| n_labels | 18 |
| n_latent_qzm | 20 |
| n_latent_qzv | 20 |
| n_vars | 4000 |
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['_scvi_latent_qzm'] |
| latent_qzv | adata.obsm['_scvi_latent_qzv'] |
| minify_type | adata.uns['_scvi_adata_minify_type'] |
| observed_lib_size | adata.obs['_scvi_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/api/files/fd2c61e6-f4cd-4984-ade0-24d26d9adef6/TS_Blood_filtered.h5ad
Training code
This is an optional link to the code used to train the model.
Training code url: https://github.com/scvi-hub-references/tabula_sapiens/main.py
References
The Tabula Sapiens: A multi-organ, single-cell transcriptomic atlas of humans. The Tabula Sapiens Consortium. Science 2022.05.13; doi: https: //doi.org/10.1126/science.abl4896
