metadata
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 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
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.