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Background <p>Recent advances in single-cell multimodal omics sequencing have facilitated the simultaneous profiling of transcriptomes and surface proteomes within individual cells, offering insights into cellular functions and heterogeneity. However, the high costs and technical complexity of protocols like CITE-seq and REAP-seq constrain large-scale dataset generation. To overcome this limitation, surface protein data imputation methods have emerged to predict protein abundances from scRNA-seq data.</p> Results <p>We present a comprehensive benchmark of twelve state-of-the-art imputation methods across eleven datasets and six scenarios. Our analysis evaluates the methods’ accuracy, sensitivity to training data size, robustness across experiments, and usability in terms of running time, memory usage, popularity, and user-friendliness. With benchmark experiments in diverse scenarios and a comprehensive evaluation framework of the results, our study offers valuable insights into the performance and applicability of surface protein data imputation methods in single-cell omics research.</p> Conclusions <p>Based on our results, Seurat v4 (PCA) and Seurat v3 (PCA) demonstrate exceptional performance, offering promising avenues for further research in single-cell omics.</p>

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Benchmarking single-cell cross-omics imputation methods for surface protein expression

  • 期刊论文

  • 出版模式：

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  • 发表日期：

    2025年3月4日

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  • Chen-Yang Li,
  • Yong-Jia Hong,
  • Bo Li,
  • Xiao-Fei Zhang

摘要

Background

Recent advances in single-cell multimodal omics sequencing have facilitated the simultaneous profiling of transcriptomes and surface proteomes within individual cells, offering insights into cellular functions and heterogeneity. However, the high costs and technical complexity of protocols like CITE-seq and REAP-seq constrain large-scale dataset generation. To overcome this limitation, surface protein data imputation methods have emerged to predict protein abundances from scRNA-seq data.

Results

We present a comprehensive benchmark of twelve state-of-the-art imputation methods across eleven datasets and six scenarios. Our analysis evaluates the methods’ accuracy, sensitivity to training data size, robustness across experiments, and usability in terms of running time, memory usage, popularity, and user-friendliness. With benchmark experiments in diverse scenarios and a comprehensive evaluation framework of the results, our study offers valuable insights into the performance and applicability of surface protein data imputation methods in single-cell omics research.

Conclusions

Based on our results, Seurat v4 (PCA) and Seurat v3 (PCA) demonstrate exceptional performance, offering promising avenues for further research in single-cell omics.