scDIFF: automatic cell type annotation using scATAC-seq data by incorporating bulk-level genomic and epigenomic information in a deep diffusive transformer

Single-cell assay for transposase-accessible chromatin sequencing (scATAC-seq) provides an opportunity to look deeply into the gene regulation mechanism at the single-cell resolution. With the rapid accumulation of scATAC-seq data, there is an urgent need for automatic cell type annotations using scATAC-seq data. Most existing methods rely on creating artificial gene activity matrix, due to the extreme sparsity nature of the scATAC-seq data. However, these methods fail to exploit the intrinsic information inherent in the scATAC-seq peaks. We present scDIFF, a diffusive transformer-based method that integrates bulk-level genomic and epigenomic information with scATAC-seq data to annotate cell types without creating artificial gene activity matrix. Our scDIFF performed constantly better than state-of-the-art methods on all 46 benchmarking pairs of reference and query datasets across different sequencing platforms. The complete implementation of scDIFF is well-documented and freely available on GitHub (https://github.com/haoyu-wangg/scDIFF).

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Publication Details

Journal
Briefings in Bioinformatics
Published
2025-11-01
DOI
https://doi.org/10.1093/bib/bbaf597
Primary Topic
Single-cell and spatial transcriptomics
Type
article
Field-Weighted Citation Impact
0.00

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article

scDIFF: automatic cell type annotation using scATAC-seq data by incorporating bulk-level genomic and epigenomic information in a deep diffusive transformer

Pu-Feng Du, Haoyu Wang, Hao Dong
Briefings in Bioinformatics
Single-cell and spatial transcriptomics
article

scDIFF: automatic cell type annotation using scATAC-seq data by incorporating bulk-level genomic and epigenomic information in a deep diffusive transformer

Pu-Feng Du, Haoyu Wang, Hao Dong
article en

Abstract

Single-cell assay for transposase-accessible chromatin sequencing (scATAC-seq) provides an opportunity to look deeply into the gene regulation mechanism at the single-cell resolution. With the rapid accumulation of scATAC-seq data, there is an urgent need for automatic cell type annotations using scATAC-seq data. Most existing methods rely on creating artificial gene activity matrix, due to the extreme sparsity nature of the scATAC-seq data. However, these methods fail to exploit the intrinsic information inherent in the scATAC-seq peaks. We present scDIFF, a diffusive transformer-based method that integrates bulk-level genomic and epigenomic information with scATAC-seq data to annotate cell types without creating artificial gene activity matrix. Our scDIFF performed constantly better than state-of-the-art methods on all 46 benchmarking pairs of reference and query datasets across different sequencing platforms. The complete implementation of scDIFF is well-documented and freely available on GitHub (https://github.com/haoyu-wangg/scDIFF).

Briefings in BioinformaticsVol. 26(6)
Tianjin University (CN)
National Natural Science Foundation of China
Openalex Percentile: Top 69%
Single-cell and spatial transcriptomics
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