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Dissecting tumor cell programs through group biology estimation in clinical single-cell transcriptomics

Author

Listed:
  • Shreya Johri

    (Dana-Farber Cancer Institute
    Broad Institute of Harvard and MIT)

  • Kevin Bi

    (Dana-Farber Cancer Institute
    Broad Institute of Harvard and MIT)

  • Breanna M. Titchen

    (Dana-Farber Cancer Institute
    Broad Institute of Harvard and MIT
    Harvard Graduate Program in Biological and Biomedical Sciences)

  • Jingxin Fu

    (Dana-Farber Cancer Institute
    Broad Institute of Harvard and MIT)

  • Jake Conway

    (Dana-Farber Cancer Institute
    Broad Institute of Harvard and MIT
    Harvard Graduate Program in Bioinformatics and Integrative Genomics)

  • Jett P. Crowdis

    (Dana-Farber Cancer Institute
    Broad Institute of Harvard and MIT)

  • Natalie I. Vokes

    (MD Anderson Cancer Center
    MD Anderson Cancer Center)

  • Zenghua Fan

    (University of California
    University of California
    University of California)

  • Lawrence Fong

    (University of California
    University of California
    University of California)

  • Jihye Park

    (Dana-Farber Cancer Institute
    Broad Institute of Harvard and MIT)

  • David Liu

    (Dana-Farber Cancer Institute
    Broad Institute of Harvard and MIT)

  • Meng Xiao He

    (Dana-Farber Cancer Institute
    Broad Institute of Harvard and MIT)

  • Eliezer M. Van Allen

    (Dana-Farber Cancer Institute
    Broad Institute of Harvard and MIT)

Abstract

With the growth of clinical cancer single-cell RNA sequencing studies, robust differential expression methods for case/control analyses (e.g., treatment responders vs. non-responders) using gene signatures are pivotal to nominate hypotheses for further investigation. However, many commonly used methods produce a large number of false positives, do not adequately represent the patient-specific hierarchical structure of clinical single-cell RNA sequencing data, or account for sample-driven confounders. Here, we present a nonparametric statistical method, BEANIE, for differential expression of gene signatures between clinically relevant groups that addresses these issues. We demonstrate its use in simulated and real-world clinical datasets in breast cancer, lung cancer and melanoma. BEANIE outperforms existing methods in specificity while maintaining sensitivity, as demonstrated in simulations. Overall, BEANIE provides a methodological strategy to inform biological insights into unique and shared differentially expressed gene signatures across different tumor states, with utility in single-study, meta-analysis, and cross-validation across cell types.

Suggested Citation

  • Shreya Johri & Kevin Bi & Breanna M. Titchen & Jingxin Fu & Jake Conway & Jett P. Crowdis & Natalie I. Vokes & Zenghua Fan & Lawrence Fong & Jihye Park & David Liu & Meng Xiao He & Eliezer M. Van Alle, 2025. "Dissecting tumor cell programs through group biology estimation in clinical single-cell transcriptomics," Nature Communications, Nature, vol. 16(1), pages 1-14, December.
  • Handle: RePEc:nat:natcom:v:16:y:2025:i:1:d:10.1038_s41467-025-57377-6
    DOI: 10.1038/s41467-025-57377-6
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    References listed on IDEAS

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    2. Nayoung Kim & Hong Kwan Kim & Kyungjong Lee & Yourae Hong & Jong Ho Cho & Jung Won Choi & Jung-Il Lee & Yeon-Lim Suh & Bo Mi Ku & Hye Hyeon Eum & Soyean Choi & Yoon-La Choi & Je-Gun Joung & Woong-Yang, 2020. "Single-cell RNA sequencing demonstrates the molecular and cellular reprogramming of metastatic lung adenocarcinoma," Nature Communications, Nature, vol. 11(1), pages 1-15, December.
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