ATACformer

⚠️ Status: partial | Version: v1.0

Overview

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

!!! tip “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.