license: cc-by-4.0
library_name: scvi-tools
tags:
- biology
- genomics
- single-cell
- model_cls_name:SCANVI
- scvi_version:0.20.0
- anndata_version:0.8.0
- modality:rna
- tissue:Muscle
- annotated:True
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:
{
"labels_key": "cell_ontology_class",
"unlabeled_category": "unknown",
"layer": null,
"batch_key": "donor_assay",
"size_factor_key": null,
"categorical_covariate_keys": null,
"continuous_covariate_keys": null
}
model_summary_stats:
| Summary Stat Key | Value |
|---|---|
| n_batch | 6 |
| n_cells | 30746 |
| n_extra_categorical_covs | 0 |
| n_extra_continuous_covs | 0 |
| n_labels | 20 |
| 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['_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/api/files/c676cd14-97a7-4ea6-ba70-2c7faa1d61d8/Muscle_training_data.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