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--- |
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language: |
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- en |
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metrics: |
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- accuracy |
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- AUC ROC |
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- precision |
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- recall |
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tags: |
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- biology |
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- chemistry |
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- therapeutic science |
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- drug design |
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- drug development |
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- therapeutics |
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license: bsd-2-clause |
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datasets: |
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- scvi-tools/DATASET-FOR-UNIT-TESTING-1 |
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--- |
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# scVI |
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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. |
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To load the pre-trained model, use the Files and Versions tab files. |
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# Code |
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```python |
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from tdc_ml.multi_pred.anndata_dataset import DataLoader |
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from tdc_ml import tdc_hf_interface |
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adata = DataLoader("scvi_test_dataset", |
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"./data", |
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dataset_names=["scvi_test_dataset"], |
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no_convert=True).adata |
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scvi = tdc_hf_interface("scVI") |
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model = scvi.load() |
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output = model(adata) |
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``` |
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# TDC_ML.scVI Source Code |
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* https://github.com/apliko-xyz/PyTDC/blob/main/tdc/model_server/models/scvi.py |
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* weights extracted from https://cellxgene.cziscience.com/census-models |
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# PyTDC Citation |
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``` |
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@inproceedings{ |
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velez-arce2025pytdc, |
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title={Py{TDC}: A multimodal machine learning training, evaluation, and inference platform for biomedical foundation models}, |
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author={Alejandro Velez-Arce and Marinka Zitnik}, |
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booktitle={Forty-second International Conference on Machine Learning}, |
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year={2025}, |
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url={https://openreview.net/forum?id=HV8vZDDoYc} |
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} |
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``` |
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``` |
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@inproceedings{ |
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velez-arce2024signals, |
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title={Signals in the Cells: Multimodal and Contextualized Machine Learning Foundations for Therapeutics}, |
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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}, |
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booktitle={NeurIPS 2024 Workshop on AI for New Drug Modalities}, |
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year={2024}, |
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url={https://openreview.net/forum?id=kL8dlYp6IM} |
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} |
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``` |
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## References |
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* Lopez, R., Regier, J., Cole, M., Jordan, M. I., & Yosef, N. (2018). Deep Generative Modeling for Single-cell Transcriptomics. Nature Methods, 15, 1053-1058. |
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* 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. |
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* 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 |
