GeneCompass

⚠️ Status: partial | Version: 120M-cells

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Overview

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

!!! tip “When to choose GeneCompass”

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

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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

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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")

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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)

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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"])

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Resources

• Repository / Checkpoint: https://github.com/xCompass-AI/GeneCompass

• License: Check upstream LICENSE

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Hands-On Tutorial

For a step-by-step walkthrough with code, see the GeneCompass Tutorial Notebook.