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<p>Single-cell multiomic technologies enable the joint analysis of different modalities, but face challenges due to experimental complexity. Current computational methods for single-cell cross-modality translation lack biological interpretability. Here, we present Cisformer, a cross-attention-based generative model tailored for cross-modality generation between gene expression and chromatin accessibility at single-cell resolution. Systematic benchmarking demonstrates the superior accuracy and generalization of Cisformer against existing methods. Cisformer leverages its inherent interpretability to precisely link <i>cis</i>-regulatory elements to target genes, facilitating the identification of functional transcription factors associated with tumorigenesis and aging. Overall, Cisformer is a powerful tool for single-cell multiomic data analysis.</p>

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Cisformer: a scalable cross-modality generation framework for decoding transcriptional regulation at single-cell resolution

  • 期刊论文

  • 出版模式：

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  • 发表日期：

    2025年10月6日

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  • Luzhang Ji,
  • Qihang Zou,
  • Ke Tang,
  • Chenfei Wang

摘要

Single-cell multiomic technologies enable the joint analysis of different modalities, but face challenges due to experimental complexity. Current computational methods for single-cell cross-modality translation lack biological interpretability. Here, we present Cisformer, a cross-attention-based generative model tailored for cross-modality generation between gene expression and chromatin accessibility at single-cell resolution. Systematic benchmarking demonstrates the superior accuracy and generalization of Cisformer against existing methods. Cisformer leverages its inherent interpretability to precisely link cis-regulatory elements to target genes, facilitating the identification of functional transcription factors associated with tumorigenesis and aging. Overall, Cisformer is a powerful tool for single-cell multiomic data analysis.