| --- |
| 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 |
|
|
