scMulan

⚠️ Status: partial | Version: v1.0

Overview

Native multi-omics joint modeling (RNA+ATAC+Protein simultaneously), designed for CITE-seq/10x Multiome

!!! tip “When to choose scMulan”

User has multi-omics data (CITE-seq, 10x Multiome, RNA+ATAC+Protein), or wants joint multi-modal embedding

Specifications

Property

Value

Model

scMulan

Version

v1.0

Tasks

embed, integrate

Modalities

RNA, ATAC, Protein, Multi-omics

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("scmulan")

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

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

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

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

Input Requirements

Requirement

Detail

Gene ID scheme

symbol

Preprocessing

For multi-omics, organize data as MuData with separate modalities. For RNA-only, standard preprocessing.

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_scmulan

adata.obsm

Cell embeddings (512-dim)

 
import scanpy as sc

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

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