region:us
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# Model Card for databio/r2v-buenrostro2018-hg19

## Model Details

This is a single-cell Region2Vec (r2v) model designed to be used with with scEmbed and Region2Vec. It was trained on the Buenrostro2018 dataset. This model should be used to generate embeddings of single cells from scATAC-seq experiments. It produces 100 dimensional embeddings for each single-cell.

### Model Sources [optional]

<!-- Provide the basic links for the model. -->

- **Repository:** https://github.com/databio/geniml
- **Paper:** https://www.biorxiv.org/content/10.1101/2023.08.01.551452v1

## Uses

This model should be used for producing low-dimensional embeddings of single-cells. These embeddings can be used for downstream clustering or classification tasks.

## Bias, Risks, and Limitations

The Buenrostro2018 dataset comprises 2034 human hematopoietic stem cells from data aligned to hg19. Therefore, it should only be used with other data aligned to hg19.


## How to Get Started with the Model

You can use the `geniml` python library to download this model and start encoding your single-cell data:

```python
import scanpy as sc
from geniml.scembed import ScEmbed

adata = sc.read_h5ad("path/to/adata.h5ad")

model = ScEmbed("databio/r2v-buenrostro2018-hg19")
embeddings = model.encode(adata)
```

## Training Details

### Training Data

The data for this model comes from Buenrostro2018: https://www.sciencedirect.com/science/article/pii/S009286741830446X

