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Imputing single-cell protein abundance in multiplex tissue imaging

Author

Listed:
  • Raphael Kirchgaessner

    (Oregon Health & Science University
    Oregon Health & Science University)

  • Cameron Watson

    (Oregon Health & Science University
    Oregon Health & Science University)

  • Allison Creason

    (Oregon Health & Science University
    Oregon Health & Science University)

  • Kaya Keutler

    (Oregon Health & Science University)

  • Jeremy Goecks

    (Oregon Health & Science University
    Moffitt Cancer Center)

Abstract

Multiplex tissue imaging enables single-cell spatial proteomics and transcriptomics but remains limited by incomplete molecular profiling, tissue loss, and probe failure. Here, we apply machine learning to impute single-cell protein abundance using multiplex tissue imaging data from a breast cancer cohort. We evaluate regularized linear regression, gradient-boosted trees, and deep learning autoencoders, incorporating spatial context to enhance imputation accuracy. Our models achieve mean absolute errors between 0.05–0.3 on a [0,1] scale, closely approximating ground truth values. Using imputed data, we classify single cells as pre- or post-treatment, demonstrating their biological relevance. These findings establish the feasibility of imputing missing protein abundance, highlight the advantages of spatial information, and support machine learning as a powerful tool for improving single-cell tissue imaging.

Suggested Citation

  • Raphael Kirchgaessner & Cameron Watson & Allison Creason & Kaya Keutler & Jeremy Goecks, 2025. "Imputing single-cell protein abundance in multiplex tissue imaging," Nature Communications, Nature, vol. 16(1), pages 1-14, December.
    • Handle: RePEc:nat:natcom:v:16:y:2025:i:1:d:10.1038_s41467-025-59788-x
    DOI: 10.1038/s41467-025-59788-x
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    References listed on IDEAS

    as
    1. Tuan Tran & Uyen Le & Yihui Shi, 2022. "An effective up-sampling approach for breast cancer prediction with imbalanced data: A machine learning model-based comparative analysis," PLOS ONE, Public Library of Science, vol. 17(5), pages 1-30, May.
    2. Robrecht Cannoodt & Wouter Saelens & Louise Deconinck & Yvan Saeys, 2021. "Spearheading future omics analyses using dyngen, a multi-modal simulator of single cells," Nature Communications, Nature, vol. 12(1), pages 1-9, December.
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