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Alleviating batch effects in cell type deconvolution with SCCAF-D

Author

Listed:
  • Shuo Feng

    (Guangzhou Medical University
    University of Science and Technology of China)

  • Liangfeng Huang

    (Guangzhou Medical University
    Shanghai Fourth People’s Hospital Affiliated to Tongji University School of Medicine)

  • Anna Vathrakokoili Pournara

    (Wellcome Genome Campus)

  • Ziliang Huang

    (Guangzhou Medical University)

  • Xinlu Yang

    (Harbin Red Cross Central Hospital)

  • Yongjian Zhang

    (Harbin Medical University the Sixth Affiliated Hospital)

  • Alvis Brazma

    (Wellcome Genome Campus)

  • Ming Shi

    (Harbin Institute of Technology)

  • Irene Papatheodorou

    (Norwich Research Park
    Norwich Research Park)

  • Zhichao Miao

    (Guangzhou Medical University
    Shanghai Fourth People’s Hospital Affiliated to Tongji University School of Medicine
    Wellcome Genome Campus)

Abstract

Cell type deconvolution methods can impute cell proportions from bulk transcriptomics data, revealing changes in disease progression or organ development. But benchmarking studies often use simulated bulk data from the same source as the reference, which limits its application scenarios. This study examines batch effects in deconvolution and introduces SCCAF-D, a computational workflow that ensures a Pearson Correlation Coefficient above 0.75 across simulated and real bulk data for various tissue types. Applied to non-alcoholic fatty liver disease, SCCAF-D unveils meaningful insights into changes in cell proportions during disease progression.

Suggested Citation

  • Shuo Feng & Liangfeng Huang & Anna Vathrakokoili Pournara & Ziliang Huang & Xinlu Yang & Yongjian Zhang & Alvis Brazma & Ming Shi & Irene Papatheodorou & Zhichao Miao, 2024. "Alleviating batch effects in cell type deconvolution with SCCAF-D," Nature Communications, Nature, vol. 15(1), pages 1-14, December.
    • Handle: RePEc:nat:natcom:v:15:y:2024:i:1:d:10.1038_s41467-024-55213-x
    DOI: 10.1038/s41467-024-55213-x
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