brickmanlab/mouse-scanvi

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
• notebook analysis

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