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Droplet Barcoding-Based Single Cell Transcriptomics of Adult Mammalian Tissues

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scDIFF: automatic cell type annotation using scATAC-seq data by incorporating bulk-level genomic and epigenomic

Hao-Yu Wang¹, Hao Dong¹, Pu-Feng Du¹

¹College of Intelligence and Computing, Tianjin University, Tianjin 300350, China.

Briefings in Bioinformatics

|November 14, 2025

Summary

scDIFF is a new computational method for cell type annotation using single-cell ATAC sequencing (scATAC-seq) data. It outperforms existing methods by directly analyzing chromatin accessibility peaks without needing artificial gene activity matrices.

Keywords:

cell type annotation graph transformer scATAC-seq

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Area of Science:

Genomics
Computational Biology
Epigenetics

Background:

Single-cell assay for transposase-accessible chromatin sequencing (scATAC-seq) enables gene regulation analysis at single-cell resolution.
Automatic cell type annotation for scATAC-seq data is crucial due to rapid data accumulation.
Current methods often create artificial gene activity matrices, neglecting intrinsic scATAC-seq peak information.

Purpose of the Study:

To develop an automated cell type annotation method for scATAC-seq data.
To overcome limitations of existing methods that rely on artificial gene activity matrices.
To leverage intrinsic information within scATAC-seq peaks for improved annotation.

Main Methods:

Introduced scDIFF, a diffusive transformer-based computational method.
Integrated bulk-level genomic and epigenomic information with scATAC-seq data.
Avoided the creation of artificial gene activity matrices for annotation.

Main Results:

scDIFF demonstrated superior and consistent performance across 46 benchmarking datasets.
The method outperformed state-of-the-art cell type annotation techniques.
Performance was robust across different sequencing platforms.

Conclusions:

scDIFF provides an effective approach for cell type annotation using scATAC-seq data.
The method's ability to directly utilize peak information enhances annotation accuracy.
scDIFF offers a valuable, well-documented, and freely available tool for the research community.