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ATACformer

⚠️ Status: partial | Version: v1.0

Overview

ATAC-seq-native transformer, peak-based (not gene-based) input, chromatin accessibility specialist

When to choose ATACformer

User has ATAC-seq data, chromatin accessibility profiles, or peak-based (not gene expression) inputs

Specifications

Property Value
Model ATACformer
Version v1.0
Tasks embed, integrate
Modalities ATAC
Species human
Gene IDs custom (peak-based)
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("atacformer")

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

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

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

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

Input Requirements

Requirement Detail
Gene ID scheme custom (peak-based)
Preprocessing Input must be ATAC-seq peak matrix (not gene expression). Follow standard scATAC-seq preprocessing (LSI/TF-IDF).
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_atacformer adata.obsm Cell embeddings (512-dim) 
import scanpy as sc

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

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