scCello

⚠️ Status: partial | Version: v1.0

Overview

Cell ontology-aligned embeddings, zero-shot cell type annotation with hierarchical coherence

!!! tip “When to choose scCello”

User wants zero-shot cell type annotation, ontology-consistent predictions, or hierarchical cell-type labeling

Specifications

Property

Value

Model

scCello

Version

v1.0

Tasks

embed, integrate, annotate

Modalities

RNA

Species

human

Gene IDs

symbol

Embedding Dim

512

GPU Required

Yes

Min VRAM

16 GB

Recommended VRAM

32 GB

CPU Fallback

No

Adapter Status

⚠️ partial

Quick Start

import omicverse as ov

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

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

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

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

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

Input Requirements

Requirement

Detail

Gene ID scheme

symbol

Preprocessing

Standard preprocessing. Annotations are aligned to the Cell Ontology hierarchy.

Data format

AnnData (.h5ad)

Batch key

.obs column for batch integration (optional)

Label key

.obs column for cell type labels (optional)

Output Keys

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

Key

Location

Description

X_sccello

adata.obsm

Cell embeddings (512-dim)

sccello_pred

adata.obs

Predicted cell type labels

 
import scanpy as sc

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

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