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<p>Drug response prediction at the single-cell level provides guidance for drug development and treatment. Existing methods typically rely on integrating data from bulk RNA sequencing and single-cell RNA sequencing, transferring overall cell line drug response labels to individual cells. However, the inherent differences between these data types and the assumption that the cell line response represents each cell’s response can affect prediction accuracy. This study proposes a heterogeneous network transfer learning model, scXDR, for drug response prediction across single-cell datasets. The model uses heterogeneous networks to integrate features and associations among drugs, genes, and cells, performing message passing, feature and structure alignment, structure reconstruction, and drug-cell score prediction. Experimental results show that scXDR outperforms methods transferring from bulk data to single-cell data and methods transferring from single-cell data to single-cell data in multiple scenarios, achieving excellent performance at individual cell and cell group levels. The importance of the model components is also confirmed. Case studies include outcome prediction for cells under drug holiday treatment, melanoma drug screening, multi-tumor drug response analysis, and predictions for approved drugs and potential drug combinations for patient cells. The research strategy and results of scXDR provide references for precision treatment at the cell level.</p>

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scXDR: drug response prediction across single-cell datasets via heterogeneous network transfer learning

  • 期刊论文

  • 出版模式：

    开放获取

  • 发表日期：

    2026年1月8日

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  • Guanpeng Qi,
  • Liugen Wang,
  • Mengdi Nan,
  • Yuhan Fu,
  • Qing Ren,
  • Yuan Zhang,
  • Ziyan Sun,
  • Zhixin Shi,
  • Jiayi Lu,
  • Jie Gao

摘要

Drug response prediction at the single-cell level provides guidance for drug development and treatment. Existing methods typically rely on integrating data from bulk RNA sequencing and single-cell RNA sequencing, transferring overall cell line drug response labels to individual cells. However, the inherent differences between these data types and the assumption that the cell line response represents each cell’s response can affect prediction accuracy. This study proposes a heterogeneous network transfer learning model, scXDR, for drug response prediction across single-cell datasets. The model uses heterogeneous networks to integrate features and associations among drugs, genes, and cells, performing message passing, feature and structure alignment, structure reconstruction, and drug-cell score prediction. Experimental results show that scXDR outperforms methods transferring from bulk data to single-cell data and methods transferring from single-cell data to single-cell data in multiple scenarios, achieving excellent performance at individual cell and cell group levels. The importance of the model components is also confirmed. Case studies include outcome prediction for cells under drug holiday treatment, melanoma drug screening, multi-tumor drug response analysis, and predictions for approved drugs and potential drug combinations for patient cells. The research strategy and results of scXDR provide references for precision treatment at the cell level.