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Comparative analysis of dimension reduction methods for cytometry by time-of-flight data

Author

Listed:
  • Kaiwen Wang

    (Southern Methodist University)

  • Yuqiu Yang

    (Southern Methodist University
    University of Texas Southwestern Medical Center)

  • Fangjiang Wu

    (University of Texas Southwestern Medical Center)

  • Bing Song

    (University of Texas Southwestern Medical Center)

  • Xinlei Wang

    (Southern Methodist University
    University of Texas at Arlington
    University of Texas at Arlington)

  • Tao Wang

    (University of Texas Southwestern Medical Center
    University of Texas Southwestern Medical Center)

Abstract

While experimental and informatic techniques around single cell sequencing (scRNA-seq) are advanced, research around mass cytometry (CyTOF) data analysis has severely lagged behind. CyTOF data are notably different from scRNA-seq data in many aspects. This calls for the evaluation and development of computational methods specific for CyTOF data. Dimension reduction (DR) is one of the critical steps of single cell data analysis. Here, we benchmark the performances of 21 DR methods on 110 real and 425 synthetic CyTOF samples. We find that less well-known methods like SAUCIE, SQuaD-MDS, and scvis are the overall best performers. In particular, SAUCIE and scvis are well balanced, SQuaD-MDS excels at structure preservation, whereas UMAP has great downstream analysis performance. We also find that t-SNE (along with SQuad-MDS/t-SNE Hybrid) possesses the best local structure preservation. Nevertheless, there is a high level of complementarity between these tools, so the choice of method should depend on the underlying data structure and the analytical needs.

Suggested Citation

  • Kaiwen Wang & Yuqiu Yang & Fangjiang Wu & Bing Song & Xinlei Wang & Tao Wang, 2023. "Comparative analysis of dimension reduction methods for cytometry by time-of-flight data," Nature Communications, Nature, vol. 14(1), pages 1-18, December.
    • Handle: RePEc:nat:natcom:v:14:y:2023:i:1:d:10.1038_s41467-023-37478-w
    DOI: 10.1038/s41467-023-37478-w
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    References listed on IDEAS

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    1. Chongzhi Zang & Tao Wang & Ke Deng & Bo Li & Sheng’en Hu & Qian Qin & Tengfei Xiao & Shihua Zhang & Clifford A. Meyer & Housheng Hansen He & Myles Brown & Jun S. Liu & Yang Xie & X. Shirley Liu, 2016. "High-dimensional genomic data bias correction and data integration using MANCIE," Nature Communications, Nature, vol. 7(1), pages 1-8, September.
    2. Dmitry Kobak & Philipp Berens, 2019. "The art of using t-SNE for single-cell transcriptomics," Nature Communications, Nature, vol. 10(1), pages 1-14, December.
    3. Hartland W. Jackson & Jana R. Fischer & Vito R. T. Zanotelli & H. Raza Ali & Robert Mechera & Savas D. Soysal & Holger Moch & Simone Muenst & Zsuzsanna Varga & Walter P. Weber & Bernd Bodenmiller, 2020. "The single-cell pathology landscape of breast cancer," Nature, Nature, vol. 578(7796), pages 615-620, February.
    4. Wei Vivian Li & Jingyi Jessica Li, 2018. "An accurate and robust imputation method scImpute for single-cell RNA-seq data," Nature Communications, Nature, vol. 9(1), pages 1-9, December.
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