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CellFM

⚠️ Status: partial | Version: v1.0

Overview

MLP architecture (not transformer), trained on ~126M cells (largest training corpus)

When to choose CellFM

User explicitly wants MLP-based (not transformer) model, or wants the largest pretraining scale (~126M cells)

Specifications

Property Value
Model CellFM
Version v1.0
Tasks embed, integrate
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("cellfm")

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

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

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

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

Input Requirements

Requirement Detail
Gene ID scheme symbol
Preprocessing Standard preprocessing. Model uses MLP layers instead of attention.
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_cellfm adata.obsm Cell embeddings (512-dim) 
import scanpy as sc

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

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