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

Chen-Yang Li1, Yong-Jia Hong1, Bo Li1,2

  • 1School of Mathematics and Statistics, and Hubei Key Lab-Math. Sci., Central China Normal University, Wuhan, 430079, China.

Genome Biology
|March 5, 2025
PubMed
Summary

This study benchmarks surface protein imputation methods for single-cell omics. Seurat v4 and Seurat v3 (PCA) show top performance, aiding future single-cell research.

Keywords:
BenchmarkCross-omics imputationSingle-cell RNA-seqSingle-cell multimodal omicsSurface protein expression

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Area of Science:

  • Single-cell omics
  • Molecular biology
  • Bioinformatics

Background:

  • Single-cell multimodal omics sequencing enables simultaneous transcriptome and surface proteome profiling.
  • High costs and complexity of methods like CITE-seq limit large-scale data generation.
  • Surface protein imputation methods predict protein abundances from scRNA-seq data to address these limitations.

Purpose of the Study:

  • To comprehensively benchmark existing surface protein imputation methods.
  • To evaluate method performance across diverse datasets and scenarios.
  • To provide insights into the applicability of imputation methods in single-cell omics.

Main Methods:

  • Benchmarking twelve state-of-the-art imputation methods.
  • Utilizing eleven diverse single-cell omics datasets.
  • Evaluating methods across six distinct experimental scenarios.

Main Results:

  • Comprehensive performance evaluation including accuracy, sensitivity, and robustness.
  • Analysis of usability factors such as running time, memory usage, and popularity.
  • Identification of top-performing methods based on extensive benchmarking.

Conclusions:

  • Seurat v4 (PCA) and Seurat v3 (PCA) demonstrated exceptional performance.
  • These methods offer promising solutions for surface protein data imputation.
  • The findings guide future research in single-cell omics data analysis.