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GenePT

⚠️ Status: partial | Version: v1.0

Overview

API-based GPT-3.5 gene embeddings (1536-dim), no local GPU required, gene-level (not cell-level)

When to choose GenePT

User wants gene-level embeddings (not cell-level), has no local GPU, or wants API-based OpenAI embeddings

Specifications

Property Value
Model GenePT
Version v1.0
Tasks embed
Modalities RNA
Species human
Gene IDs symbol
Embedding Dim 1536
GPU Required No
Min VRAM 0 GB
Recommended VRAM 0 GB
CPU Fallback Yes
Adapter Status ⚠️ partial

Quick Start

import omicverse as ov

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

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

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

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

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

Input Requirements

Requirement Detail
Gene ID scheme symbol
Preprocessing No local preprocessing needed. Requires OpenAI API key for embedding generation.
Data format AnnData (.h5ad)

Output Keys

After running ov.fm.run(), results are stored in the AnnData object:

Key Location Description
X_genept adata.obsm Cell embeddings (1536-dim) 
import scanpy as sc

adata = sc.read_h5ad("output_genept.h5ad")
embeddings = adata.obsm["X_genept"]  # shape: (n_cells, 1536)

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