UCE

✅ Status: ready | Version: 4-layer

Overview

Broadest species support (7 species), 1280-dim embeddings, universal cell embedding via protein structure

!!! tip “When to choose UCE”

User has non-human/non-mouse species (zebrafish, frog, pig, macaque, lemur), or needs cross-species comparison

Specifications

Property

Value

Model

UCE

Version

4-layer

Tasks

embed, integrate

Modalities

RNA

Species

human, mouse, zebrafish, mouse_lemur, macaque, frog, pig

Gene IDs

symbol

Embedding Dim

1280

GPU Required

Yes

Min VRAM

16 GB

Recommended VRAM

16 GB

CPU Fallback

No

Adapter Status

✅ ready

Quick Start

import omicverse as ov

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

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

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

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

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

Input Requirements

Requirement

Detail

Gene ID scheme

symbol

Preprocessing

Standard log-normalization. Model handles tokenization internally.

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_uce

adata.obsm

Cell embeddings (1280-dim)

 
import scanpy as sc

adata = sc.read_h5ad("output_uce.h5ad")
embeddings = adata.obsm["X_uce"]  # shape: (n_cells, 1280)

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