scBERT

⚠️ Status: partial | Version: v1.0

Overview

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

!!! tip “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.