---
library_name: perturblab
tags:
- biology
- genomics
- scfoundation
- foundation-model
license: apache-2.0
base_model: biomap-research/scFoundation
---

# scfoundation-cell

## Model Description
This is the **cell embedding** model from scFoundation. It generates cell-level embeddings from single-cell RNA-seq data.

Model weights were originally from the [biomap-research/scFoundation](https://github.com/biomap-research/scFoundation) repository and have been re-uploaded here for ease of use with the `perturblab` library.

## Model Details
- **Model Type**: Cell embedding model
- **Architecture**: xTrimoGene with MAE (Masked Autoencoder), Performer/Transformer modules
- **Parameters**: 100M parameters
- **Training Data**: 50M+ human single-cell transcriptomics data
- **Input**: Single-cell or bulk RNA-seq expression data (19,264 fixed genes)
- **Output**: Cell-level embeddings

## Source
- **Original Repository**: [biomap-research/scFoundation](https://github.com/biomap-research/scFoundation)
- **Paper**: [Large Scale Foundation Model on Single-cell Transcriptomics](https://www.nature.com/articles/s41592-024-02305-7) (_Nature Methods_, 2024)

## Usage

```python
from perturblab.model.scfoundation import scFoundationModel

# Load model
model = scFoundationModel.from_pretrained('scfoundation-cell', device='cuda')

# Generate cell embeddings
cell_embeddings = model.predict_embedding(
    adata, 
    output_type='cell',
    pool_type='all'
)
```

## Note
Intended for internal use with the PerturbLab framework.