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EpiAgent: foundation model for single-cell epigenomics.

Xiaoyang Chen1, Keyi Li1, Xuejian Cui1

  • 1Ministry of Education Key Laboratory of Bioinformatics, Bioinformatics Division at the Beijing National Research Center for Information Science and Technology, Center for Synthetic and Systems Biology, Department of Automation, Tsinghua University, Beijing, China.

Nature Methods
|September 25, 2025
PubMed
Summary
This summary is machine-generated.

EpiAgent is a new foundation model for analyzing single-cell ATAC sequencing (scATAC-seq) data. It effectively encodes cell chromatin patterns, aiding in cell type annotation and predicting cellular responses to perturbations.

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Area of Science:

  • Genomics
  • Computational Biology
  • Epigenetics

Background:

  • Single-cell assay for transposase-accessible chromatin using sequencing (scATAC-seq) is crucial for understanding cellular transcription regulation.
  • Current computational methods struggle with the complexity and diverse applications of scATAC-seq data.

Purpose of the Study:

  • To develop a sophisticated and versatile computational method for scATAC-seq data analysis.
  • To introduce EpiAgent, a foundation model designed to address the limitations of existing tools.

Main Methods:

  • EpiAgent was pretrained on a large-scale Human-scATAC-Corpus.
  • The model encodes chromatin accessibility as 'cell sentences' using bidirectional attention.
  • It was benchmarked on tasks like feature extraction, cell type annotation, and data imputation.

Main Results:

  • EpiAgent demonstrated superior performance in unsupervised feature extraction, supervised cell type annotation, and data imputation.
  • The model successfully predicted cellular responses to genetic perturbations and integrated reference data.
  • EpiAgent showed potential in modeling cell state changes via in silico cis-regulatory element knockout and enabled zero-shot cell type annotation.

Conclusions:

  • EpiAgent offers a powerful and adaptable foundation model for scATAC-seq data analysis.
  • It enhances the understanding of cellular heterogeneity and regulatory networks.
  • The model has broad applications, including response prediction, data integration, and cell type annotation.