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Transcriptome Analysis of Single Cells
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Comparative benchmarking of single-cell clustering algorithms for transcriptomic and proteomic data.

Yu-Hang Yin1,2, Fang Wang3, Wei Li4

  • 1College of Life Science, Northeast Forestry University, Harbin, 150040, China.

Genome Biology
|September 3, 2025
PubMed
Summary
This summary is machine-generated.

This study benchmarks 28 algorithms for single-cell transcriptomic and proteomic data clustering, revealing modality-specific performance and guiding method selection for optimal results across diverse omics integration scenarios.

Keywords:
Feature integrationProteomicsSingle-cell clusteringTranscriptomics

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Area of Science:

  • Computational biology
  • Single-cell multi-omics analysis
  • Bioinformatics

Background:

  • Clustering single-cell data is challenging due to variations in data distribution, dimensionality, and quality across different omics modalities.
  • Existing clustering algorithms are primarily developed for specific omics types (e.g., transcriptomics, proteomics), with limited understanding of their cross-modal performance and integration capabilities.

Purpose of the Study:

  • To systematically benchmark the performance of computational clustering algorithms across single-cell transcriptomic and proteomic data.
  • To evaluate the impact of factors like highly variable genes (HVGs) and cell type granularity on clustering outcomes.
  • To assess the benefits of integrating multi-omics data for improved clustering and to guide method selection.

Main Methods:

  • Comparative benchmark analysis of 28 computational algorithms on 10 paired single-cell transcriptomic and proteomic datasets.
  • Evaluation of algorithm performance using metrics for clustering accuracy, peak memory usage, and running time.
  • Assessment of method robustness using 30 simulated datasets and analysis of integrated multi-omics data from 7 integration methods.

Main Results:

  • Identified modality-specific strengths and limitations of various clustering algorithms.
  • Demonstrated the complementary nature of different methods and highlighted the impact of HVGs and cell type granularity.
  • Showcased the performance of clustering on integrated transcriptomic and proteomic data, revealing potential improvements.

Conclusions:

  • Actionable insights provided for selecting appropriate clustering methods based on specific single-cell multi-omics data scenarios and user priorities (e.g., performance, memory, time efficiency).
  • Recommended scAIDE, scDCC, and FlowSOM for top performance across both omics; FlowSOM noted for robustness.
  • Suggested scDCC and scDeepCluster for memory efficiency, TSCAN, SHARP, and MarkovHC for time efficiency, and community detection methods for a balance.