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scFoundation

✅ Status: ready | Version: xTrimoGene

Overview

Large-scale asymmetric transformer (xTrimoGene), custom 19264 gene vocabulary, pre-trained for perturbation/drug response

When to choose scFoundation

User needs perturbation prediction, drug response modeling, or works with the xTrimoGene gene vocabulary

Specifications

Property Value
Model scFoundation
Version xTrimoGene
Tasks embed, integrate
Modalities RNA
Species human
Gene IDs custom (19,264 gene set)
Embedding Dim 512
GPU Required Yes
Min VRAM 16 GB
Recommended VRAM 32 GB
CPU Fallback No
Adapter Status ✅ ready

Quick Start

import omicverse as ov

# 1. Check model spec
info = ov.fm.describe_model("scfoundation")

# 2. Profile your data
profile = ov.fm.profile_data("your_data.h5ad")

# 3. Validate compatibility
check = ov.fm.preprocess_validate("your_data.h5ad", "scfoundation", "embed")

# 4. Run inference
result = ov.fm.run(
    task="embed",
    model_name="scfoundation",
    adata_path="your_data.h5ad",
    output_path="output_scfoundation.h5ad",
    device="auto",
)

# 5. Interpret results
metrics = ov.fm.interpret_results("output_scfoundation.h5ad", task="embed")

Input Requirements

Requirement Detail
Gene ID scheme custom (19,264 gene set)
Preprocessing Match genes to model vocabulary. Follow xTrimoGene preprocessing pipeline.
Data format AnnData (.h5ad)
Batch key .obs column for batch integration (optional)

Output Keys

After running ov.fm.run(), results are stored in the AnnData object:

Key Location Description
X_scfoundation adata.obsm Cell embeddings (512-dim)
scfoundation_pred adata.obs Predicted cell type labels 
import scanpy as sc

adata = sc.read_h5ad("output_scfoundation.h5ad")
embeddings = adata.obsm["X_scfoundation"]  # shape: (n_cells, 512)

# Downstream analysis
sc.pp.neighbors(adata, use_rep="X_scfoundation")
sc.tl.umap(adata)
sc.tl.leiden(adata, resolution=0.5)
sc.pl.umap(adata, color=["leiden"])

Resources

Hands-On Tutorial

For a step-by-step walkthrough with code, see the scFoundation Tutorial Notebook.