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---
license: cc-by-4.0
library_name: scvi-tools
tags:
- biology
- genomics
- single-cell
- model_cls_name:SCANVI
- modality:rna
- annotated:True
---
# Description
Mouse preimplantation development model spanning early stages of development. The
model was trained utilizing single‐cell ANnotation using Variational Inference
(scANVI, [Xu et al., 2021]) implemented in [scvi-tools]. In short, scANVI raw
single-cell RNA sequencing (scRNA-seq) count matrix - cell by gene, where values
represent gene expression measured by counting number of transcribed RNA.
# Model Training
- [raw dataset](https://zenodo.org/records/13749348/files/01_mouse_reprocessed.h5ad)
- [notebook analysis](https://github.com/brickmanlab/proks-salehin-et-al/blob/master/notebooks/15_mouse_scANVI_fix.ipynb)
# Metrics
Cell type (`ct`) prediction
| Metric | Score |
|-------------------|---------------------|
| Accuracy score | 0.9126746506986028 |
| Balanced accuracy | 0.9572872718187365 |
| F1 (micro) | 0.9126746506986028 |
| F1 (macro) | 0.9201654923575322 |
# Model parameters
Below we provide settings for scANVI setup
`lvae.init_params_["non_kwargs"]`
```json
{
"n_hidden": 128,
"n_latent": 10,
"n_layers": 2,
"dropout_rate": 0.1,
"dispersion": "gene",
"gene_likelihood": "nb",
"linear_classifier": false
}
```
`lvae.adata_manager.registry['setup_args']`
```json
{
"labels_key": "ct",
"unlabeled_category": "Unknown",
"layer": "counts",
"batch_key": "batch",
"size_factor_key": null,
"categorical_covariate_keys": null,
"continuous_covariate_keys": null
}
```
# References
Proks, M., Salehin, N. & Brickman, J.M. Deep learning-based models for preimplantation mouse and human embryos based on single-cell RNA sequencing. Nat Methods 22, 207–216 (2025). [https://doi.org/10.1038/s41592-024-02511-3](https://doi.org/10.1038/s41592-024-02511-3)
[Xu et al., 2021]: https://www.embopress.org/doi/full/10.15252/msb.20209620
[scvi-tools]: http://scvi-tools.org
|