# Cell-Type Annotation for scATAC-Seq Data by Integrating Chromatin Accessibility and Genome Sequence

## TL;DR

This paper introduces scAttG, a new deep learning method that improves cell-type annotation in scATAC-seq data by combining chromatin accessibility and genomic sequence information.

## Contribution

The novel integration of graph attention networks and convolutional neural networks to enhance cell-type annotation using scATAC-seq data and genomic sequences.

## Key findings

- scAttG improves robustness and accuracy in cell-type annotation compared to existing methods.
- The model effectively captures chromatin accessibility signals and genomic sequence features.
- Experimental results show competitive performance across multiple scATAC-seq datasets.

## Abstract

Single-cell Assay for Transposase-Accessible Chromatin using sequencing (scATAC-seq) technology enables single-cell resolution analysis of chromatin accessibility, offering critical insights into gene regulation, epigenetic heterogeneity, and cellular differentiation across various biological contexts. However, existing cell annotation methods face notable limitations. Cross-omics approaches, which rely on single-cell RNA sequencing (scRNA-seq) as a reference, often struggle with data alignment due to fundamental differences between transcriptional and chromatin accessibility modalities. Meanwhile, intra-omics methods, which rely solely on scATAC-seq data, are frequently affected by batch effects and fail to fully utilize genomic sequence information for accurate annotation. To address these challenges, we propose scAttG, a novel deep learning framework that integrates graph attention networks (GATs) and convolutional neural networks (CNNs) to capture both chromatin accessibility signals and genomic sequence features. By utilizing the nucleotide sequences corresponding to scATAC-seq peaks, scAttG enhances both the robustness and accuracy of cell-type annotation. Experimental results across multiple scATAC-seq datasets suggest that scAttG generally performs favorably compared to existing methods, showing competitive performance in single-cell chromatin accessibility-based cell-type annotation.