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metadata
license: cc-by-4.0
library_name: scvi-tools
tags:
  - biology
  - genomics
  - single-cell
  - model_cls_name:SCVI
  - scvi_version:0.20.0
  - anndata_version:0.8.0
  - modality:rna
  - tissue:Large_Intestine
  - 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:

{
    "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 2
n_cells 11505
n_extra_categorical_covs 0
n_extra_continuous_covs 0
n_labels 6
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.

Training data url: https://zenodo.org/api/files/fd2c61e6-f4cd-4984-ade0-24d26d9adef6/TS_Large_Intestine_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