metadata

library_name: scvi-tools
license: cc-by-4.0
tags:
  - biology
  - genomics
  - single-cell
  - model_cls_name:SCVI
  - scvi_version:1.2.0
  - anndata_version:0.11.1
  - modality:rna
  - annotated:False

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.

Model Properties

We provide here key parameters used to setup and train the model.

Training data url: N/A

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",
    "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['latent_qzm']
latent_qzv	adata.obsm['latent_qzv']
minify_type	adata.uns['_scvi_adata_minify_type']
observed_lib_size	adata.obs['observed_lib_size']

Data is Minified: True

Summary 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

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: N/A

References

To be added...