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
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"]
{
"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']
{
"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