README.md

---
library_name: scvi-tools
license: cc-by-4.0
tags:
- biology
- genomics
- single-cell
- model_cls_name:SCVI
- scvi_version:1.2.0
- anndata_version:0.11.1
- modality:rna
- annotated:False
---


ScVI is a variational inference model for single-cell RNA-seq data that can learn an underlying latent space, integrate technical batches and impute dropouts.
The learned low-dimensional latent representation of the data can be used for visualization and clustering.

scVI takes as input a scRNA-seq gene expression matrix with cells and genes.
We provide an extensive [user guide](https://docs.scvi-tools.org/en/1.2.0/user_guide/models/scvi.html).

- See our original manuscript for further details of the model: [scVI manuscript](https://www.nature.com/articles/s41592-018-0229-2).
- See our manuscript on [scvi-hub](https://www.biorxiv.org/content/10.1101/2024.03.01.582887v2) how to leverage pre-trained models.

This model can be used for fine tuning on new data using our Arches framework: [Arches tutorial](https://docs.scvi-tools.org/en/1.0.0/tutorials/notebooks/scarches_scvi_tools.html).


# Model Description

scVI model trained on synthetic IID data and uploaded with the minified data.

# Model Properties

We provide here key parameters used to setup and train the model.

<!-- 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**: N/A

<details>
<summary><strong>Model Parameters</strong></summary>

These provide the settings to setup the original model:
```json
{
    "n_hidden": 128,
    "n_latent": 10,
    "n_layers": 1,
    "dropout_rate": 0.1,
    "dispersion": "gene",
    "gene_likelihood": "zinb",
    "latent_distribution": "normal"
}
```

</details>

<details>
<summary><strong>Setup Data Arguments</strong></summary>
Arguments passed to setup_anndata of the original model:
```json
{
    "layer": null,
    "batch_key": null,
    "labels_key": null,
    "size_factor_key": null,
    "categorical_covariate_keys": null,
    "continuous_covariate_keys": null
}
```

</details>

<details>
<summary><strong>Data Registry</strong></summary>
Registry elements for AnnData manager:
|   Registry Key    |         scvi-tools Location          |
|-------------------|--------------------------------------|
|         X         |               adata.X                |
|       batch       |       adata.obs['_scvi_batch']       |
|      labels       |      adata.obs['_scvi_labels']       |
|    latent_qzm     |       adata.obsm['latent_qzm']       |
|    latent_qzv     |       adata.obsm['latent_qzv']       |
|    minify_type    | adata.uns['_scvi_adata_minify_type'] |
| observed_lib_size |    adata.obs['observed_lib_size']    |

- **Data is Minified**: True

</details>

<details>
<summary><strong>Summary Statistics</strong></summary>
|     Summary Stat Key     | Value |
|--------------------------|-------|
|         n_batch          |   1   |
|         n_cells          |  400  |
| n_extra_categorical_covs |   0   |
| n_extra_continuous_covs  |   0   |
|         n_labels         |   1   |
|       n_latent_qzm       |  10   |
|       n_latent_qzv       |  10   |
|          n_vars          |  100  |

</details>


<details>
<summary><strong>Training</strong></summary>

If provided by the original uploader, for those interested in understanding or replicating the training process, the code is available at the link below.

**Training Code URL**: N/A

</details>

# References

To be added...