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scBERT

⚠️ Status: partial | Version: v1.0

Overview

Compact 200-dim embeddings, BERT-style masked gene pretraining, lightweight model

When to choose scBERT

User needs compact 200-dim embeddings, BERT-style pretraining, or a lightweight model for constrained hardware

Specifications

Property Value
Model scBERT
Version v1.0
Tasks embed, integrate
Modalities RNA
Species human
Gene IDs symbol
Embedding Dim 200
GPU Required Yes
Min VRAM 8 GB
Recommended VRAM 16 GB
CPU Fallback Yes
Adapter Status ⚠️ partial

Quick Start

import omicverse as ov

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

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

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

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

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

Input Requirements

Requirement Detail
Gene ID scheme symbol
Preprocessing Standard log-normalization and gene selection.
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_scBERT adata.obsm Cell embeddings (200-dim)
scbert_pred adata.obs Predicted cell type labels 
import scanpy as sc

adata = sc.read_h5ad("output_scbert.h5ad")
embeddings = adata.obsm["X_scBERT"]  # shape: (n_cells, 200)

# Downstream analysis
sc.pp.neighbors(adata, use_rep="X_scBERT")
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 scBERT Tutorial Notebook.