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GeneCompass

⚠️ Status: partial | Version: 120M-cells

Overview

Prior-knowledge-enhanced pretraining (gene regulatory networks + pathway info), 120M cell training corpus

When to choose GeneCompass

User mentions prior knowledge, gene regulatory networks, pathway-informed embeddings, or mouse+human cross-species

Specifications

Property Value
Model GeneCompass
Version 120M-cells
Tasks embed, integrate
Modalities RNA
Species human, mouse
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("genecompass")

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

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

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

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

Input Requirements

Requirement Detail
Gene ID scheme symbol
Preprocessing Follow GeneCompass preprocessing. Supports both human and mouse gene symbols.
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_genecompass adata.obsm Cell embeddings (512-dim)
genecompass_pred adata.obs Predicted cell type labels 
import scanpy as sc

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

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