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CellPLM

✅ Status: ready | Version: v1.0

Overview

Cell-centric (not gene-centric) architecture, highest batch throughput (batch_size=128), fast inference

When to choose CellPLM

User needs fast inference, high throughput, million-cell scale processing, or cell-level (not gene-level) modeling

Specifications

Property Value
Model CellPLM
Version v1.0
Tasks embed, integrate
Modalities RNA
Species human
Gene IDs symbol
Embedding Dim 512
GPU Required Yes
Min VRAM 8 GB
Recommended VRAM 16 GB
CPU Fallback Yes
Adapter Status ✅ ready

Quick Start

import omicverse as ov

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

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

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

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

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

Input Requirements

Requirement Detail
Gene ID scheme symbol
Preprocessing Standard preprocessing. 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_cellplm adata.obsm Cell embeddings (512-dim) 
import scanpy as sc

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

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