---
language:
- en
metrics:
- accuracy
- AUC ROC
- precision
- recall
tags:
- biology
- chemistry
- therapeutic science
- drug design
- drug development
- therapeutics
license: bsd-2-clause
datasets:
- scvi-tools/DATASET-FOR-UNIT-TESTING-1
---

# scVI
Single-cell variational inference (scVI) is a powerful tool for the probabilistic analysis of single-cell transcriptomics data. It uses deep generative models to address technical noise and batch effects, providing a robust framework for various downstream analysis tasks. 
To load the pre-trained model, use the Files and Versions tab files.

# Code

```python
from tdc_ml.multi_pred.anndata_dataset import DataLoader
from tdc_ml import tdc_hf_interface

adata = DataLoader("scvi_test_dataset",
                   "./data",
                   dataset_names=["scvi_test_dataset"],
                   no_convert=True).adata

scvi = tdc_hf_interface("scVI")
model = scvi.load()
output = model(adata)
```

# TDC_ML.scVI Source Code
* https://github.com/apliko-xyz/PyTDC/blob/main/tdc/model_server/models/scvi.py
* weights extracted from https://cellxgene.cziscience.com/census-models

# PyTDC Citation
```
@inproceedings{
velez-arce2025pytdc,
title={Py{TDC}: A multimodal machine learning training, evaluation, and inference platform for biomedical foundation models},
author={Alejandro Velez-Arce and Marinka Zitnik},
booktitle={Forty-second International Conference on Machine Learning},
year={2025},
url={https://openreview.net/forum?id=HV8vZDDoYc}
}
```

```
@inproceedings{
velez-arce2024signals,
title={Signals in the Cells: Multimodal and Contextualized Machine Learning Foundations for Therapeutics},
author={Alejandro Velez-Arce and Xiang Lin and Kexin Huang and Michelle M Li and Wenhao Gao and Bradley Pentelute and Tianfan Fu and Manolis Kellis and Marinka Zitnik},
booktitle={NeurIPS 2024 Workshop on AI for New Drug Modalities},
year={2024},
url={https://openreview.net/forum?id=kL8dlYp6IM}
}
```

## References
* Lopez, R., Regier, J., Cole, M., Jordan, M. I., & Yosef, N. (2018). Deep Generative Modeling for Single-cell Transcriptomics. Nature Methods, 15, 1053-1058.
* Gayoso, A., Lopez, R., Xing, G., Boyeau, P., Wu, K., Jayasuriya, M., Mehlman, E., Langevin, M., Liu, Y., Samaran, J., Misrachi, G., Nazaret, A., Clivio, O., Xu, C. A., Ashuach, T., Lotfollahi, M., Svensson, V., Beltrame, E., Talavera-López, C., ... Yosef, N. (2021). scvi-tools: a library for deep probabilistic analysis of single-cell omics data. bioRxiv.
* CZ CELLxGENE Discover: A single-cell data platform for scalable exploration, analysis and modeling of aggregated data CZI Single-Cell Biology, et al. bioRxiv 2023.10.30; doi: https://doi.org/10.1101/2023.10.30.563174