Integrating gene regulatory priors into Transformer attention with scTransformer for interpretable scRNA-seq analysis

• URL: http://arxiv.org/abs/2606.09558v1
• Date: Mon, 08 Jun 2026 14:32:52 GMT
• Title: Integrating gene regulatory priors into Transformer attention with scTransformer for interpretable scRNA-seq analysis
• Abstract summary: scTransformer is the first Transformer-based approach that builds a priori knowledge of biological mechanisms into the model's attention patterns.<n>Compared to standard Transformers, our approach improves classification accuracy, enhances separation of cell types in embedding space, and produces attention patterns consistent with known regulatory programs.
• Score: 0.5463550120736066
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Motivation: Transformer-based models are increasingly applied to large-scale single-cell transcriptomics, showing strong performance through self-supervised learning on millions of cells. However, most existing approaches treat genes as independent features, and largely ignore prior biological knowledge, which limits interpretability and robustness. In this paper, we explore whether explicitly incorporating gene regulatory information can improve both model performance and biological insight. Results: We present scTransformer, the first Transformer-based approach that builds a priori knowledge of biological mechanisms into the model's attention patterns. By constraining information flow according to known regulatory structures, the model learns representations that are more biologically meaningful. We evaluate scTransformer on a disease-relevant single-nucleus RNA-seq dataset using supervised cell-type classification. Compared to standard Transformers, our approach improves classification accuracy, enhances separation of cell types in embedding space, and produces attention patterns consistent with known regulatory programs. Overall, our results demonstrate that embedding biological structure into Transformer models can enhance interpretability without sacrificing performance, offering a principled step toward biologically grounded foundation models for single-cell omics.

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