ScVI is a variational inference model for single-cell RNA-seq data that can learn an underlying latent space, integrate technical batches and impute dropouts.
Model Card
By Scvi Tools, published under cc-by-4.0, revision eb928910e3bd.
ScVI is a variational inference model for single-cell RNA-seq data that can learn an underlying latent space, integrate technical batches and impute dropouts. The learned low-dimensional latent representation of the data can be used for visualization and clustering. scVI takes as input a scRNA-seq gene expression matrix with cells and genes. We provide an extensive user guide. - See our original manuscript for further details of the model: - See our manuscript on scvi-hub how to leverage pre-trained models. This model can be used for fine tuning on new data using our Arches framework: scVI model trained on synthetic IID data and uploaded with the minified data. We provide here key…
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ScVI is a variational inference model for single-cell RNA-seq data that can learn an underlying latent space, integrate technical batches and impute dropouts. The learned low-dimensional latent representation of the data can be used for visualization and clustering.
scVI takes as input a scRNA-seq gene expression matrix with cells and genes. We provide an extensive user guide.
- See our original manuscript for further details of the model: scVI manuscript.
- See our manuscript on scvi-hub how to leverage pre-trained models.
This model can be used for fine tuning on new data using our Arches framework: Arches tutorial.
Model Description
scVI model trained on synthetic IID data and uploaded with the minified data.
Metrics
We provide here key performance metrics for the uploaded model, if provided by the data uploader.
Coefficient of variation
The cell-wise coefficient of variation summarizes how well variation between different cells is preserved by the generated model expression. Below a squared Pearson correlation coefficient of 0.4 , we would recommend not to use generated data for downstream analysis, while the generated latent space might still be useful for analysis. **Cell-wise Coefficient of Variation**: Not provided by uploader The gene-wise coefficient of variation summarizes how well variation between different genes is preserved by the generated model expression. This value is usually quite high. **Gene-wise Coefficient of Variation**: Not provided by uploaderDifferential expression metric
The differential expression metric provides a summary of the differential expression analysis between cell types or input clusters. We provide here the F1-score, Pearson Correlation Coefficient of Log-Foldchanges, Spearman Correlation Coefficient, and Area Under the Precision Recall Curve (AUPRC) for the differential expression analysis using Wilcoxon Rank Sum test for each cell-type. **Differential expression**: Not provided by uploaderModel Properties
We provide here key parameters used to setup and train the model.
Model Parameters
These provide the settings to setup the original model:{
"n_hidden": 128,
"n_latent": 10,
"n_layers": 1,
"dropout_rate": 0.1,
"dispersion": "gene",
"gene_likelihood": "zinb",
"use_observed_lib_size": true,
"latent_distribution": "normal"
}
Setup Data Arguments
Arguments passed to setup_anndata of the original model:{
"layer": null,
"batch_key": null,
"labels_key": null,
"size_factor_key": null,
"categorical_covariate_keys": null,
"continuous_covariate_keys": null
}
Data Registry
Registry elements for AnnData manager: | Registry Key | scvi-tools Location | |--------------------------|--------------------------------------| | X | adata.X | | batch | adata.obs['_scvi_batch'] | | labels | adata.obs['_scvi_labels'] | | latent_qzm | adata.obsm['scvi_latent_qzm'] | | latent_qzv | adata.obsm['scvi_latent_qzv'] | | minify_type | adata.uns['_scvi_adata_minify_type'] | | observed_lib_size | adata.obs['observed_lib_size'] | - **Data is Minified**: TrueSummary Statistics
| Summary Stat Key | Value | |--------------------------|-------| | n_batch | 1 | | n_cells | 400 | | n_extra_categorical_covs | 0 | | n_extra_continuous_covs | 0 | | n_labels | 1 | | n_latent_qzm | 10 | | n_latent_qzv | 10 | | n_vars | 100 |Training
**Training data url**: Not provided by uploader If provided by the original uploader, for those interested in understanding or replicating the training process, the code is available at the link below. **Training Code URL**: Not provided by uploaderReferences
To be added...
Identity and Version
- Repository
- scvi-tools/test-scvi-minified
- Publisher
- Scvi Tools
- Task
- Not stated by the source
- Modality
- Other
- Library
- scvi-tools
- Parameters
- Not stated by the source
- Languages
- Not stated by the source
- Revision
- eb928910e3bdd9dee1f628b7dc7f2adc89dfaf99
- First published
- 2024-01-22
- Last updated
- 2026-09-18
Files and Weights
5 files, 1.1 MB in total. The weights are 1 file totalling 307.6 KB in pt.
Every file
| File | Type | Size | SHA-256 |
|---|---|---|---|
| model.pt | Weights | 307.6 KB | 0edc6e72c532 |
| _scvi_required_metadata.json | Configuration | 166 B | — |
| README.md | Documentation | 5.1 KB | — |
| adata.h5ad | Other | 769.1 KB | bd1cb946364e |
| .gitattributes | Repository | 1.6 KB | — |
License and Download
- License
- cc-by-4.0
- Access
- Open weights, no gate
- Download size
- 307.6 KB
Released by Scvi Tools through its official repository on Hugging Face. Read the license.
Memory Requirements
| Precision | Weights in memory |
|---|---|
| As published | 307.6 KB |
Weights only, from the published parameter count; the key-value cache and runtime add to this.
Questions About test-scvi-minified
Can I use test-scvi-minified commercially?
Yes. test-scvi-minified is released under Creative Commons Attribution 4.0. CC BY 4.0 permits sharing and adapting the work, including commercially, provided the creator is credited and changes are indicated.