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perturblab/
scfoundation-cell

Name: scfoundation-cell
Availability: InStock

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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 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
Paper: Large Scale Foundation Model on Single-cell Transcriptomics (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.

perturblab/scfoundation-cell