---
library_name: scvi-tools
license: cc-by-4.0
tags:
- biology
- genomics
- single-cell
- model_cls_name:RNAStereoscope
- scvi_version:1.2.0
- anndata_version:0.11.1
- modality:rna
- tissue:various
- annotated:True
---


Stereoscope is a variational inference model for single-cell RNA-seq data that can learn a
cell-type specific rate of gene expression. The predictions of the model are meant to be afterward
used for deconvolution of a second spatial transcriptomics dataset in Stereoscope. Stereoscope
predicts the cell-type proportions in the spatial data.

Stereoscope takes as input a scRNA-seq gene expression matrix with cells and genes as well as a
cell-type annotation for all cells.
We provide an extensive for DestVI including a description of CondSCVI
[user guide](https://docs.scvi-tools.org/en/1.2.0/user_guide/models/destvi.html).

- See our original manuscript for further details of the model:
[Stereoscope manuscript](https://www.nature.com/articles/s42003-020-01247-y) as well as the
[scvi-tools manuscript](https://www.nature.com/articles/s41587-021-01206-w) about implementation
details.
- See our manuscript on [scvi-hub](https://www.biorxiv.org/content/10.1101/2024.03.01.582887v2)
how to leverage pre-trained models.


# Model Description

Tabula Sapiens is a benchmark, first-draft human cell atlas of nearly 500,000 cells from 24 organs of 15 normal human subjects.

# Metrics

We provide here key performance metrics for the uploaded model, if provided by the data uploader.

<details>
<summary><strong>Coefficient of variation</strong></summary>

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 uploader

</details>

<details>
<summary><strong>Differential expression metric</strong></summary>

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 uploader

</details>

# Model Properties

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

<details>
<summary><strong>Model Parameters</strong></summary>

These provide the settings to setup the original model:
```json
{}
```

</details>

<details>
<summary><strong>Setup Data Arguments</strong></summary>

Arguments passed to setup_anndata of the original model:
```json
{
    "labels_key": "cell_ontology_class",
    "layer": null
}
```

</details>

<details>
<summary><strong>Data Registry</strong></summary>

Registry elements for AnnData manager:
| Registry Key |    scvi-tools Location    |
|--------------|---------------------------|
|      X       |          adata.X          |
|    labels    | adata.obs['_scvi_labels'] |

- **Data is Minified**: False

</details>

<details>
<summary><strong>Summary Statistics</strong></summary>

| Summary Stat Key | Value |
|------------------|-------|
|     n_cells      | 7114  |
|     n_labels     |  13   |
|      n_vars      | 3000  |

</details>


<details>
<summary><strong>Training</strong></summary>

<!-- If your model is not uploaded with any data (e.g., minified data) on the Model Hub, then make
sure to provide this field if you want users to be able to access your training data. See the
scvi-tools documentation for details. -->
**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**: https://github.com/YosefLab/scvi-hub-models/blob/main/src/scvi_hub_models/TS_train_all_tissues.ipynb

</details>


# References

The Tabula Sapiens Consortium. The Tabula Sapiens: A multiple-organ, single-cell transcriptomic atlas of humans. Science, May 2022. doi:10.1126/science.abl4896