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Benchmarking integration of single-cell differential expression

Author

Listed:
  • Hai C. T. Nguyen

    (Ulsan National Institute of Science and Technology)

  • Bukyung Baik

    (Ulsan National Institute of Science and Technology)

  • Sora Yoon

    (Ulsan National Institute of Science and Technology
    University of Pennsylvania Perelman School of Medicine)

  • Taesung Park

    (Seoul National University
    Seoul National University)

  • Dougu Nam

    (Ulsan National Institute of Science and Technology
    Ulsan National Institute of Science and Technology)

Abstract

Integration of single-cell RNA sequencing data between different samples has been a major challenge for analyzing cell populations. However, strategies to integrate differential expression analysis of single-cell data remain underinvestigated. Here, we benchmark 46 workflows for differential expression analysis of single-cell data with multiple batches. We show that batch effects, sequencing depth and data sparsity substantially impact their performances. Notably, we find that the use of batch-corrected data rarely improves the analysis for sparse data, whereas batch covariate modeling improves the analysis for substantial batch effects. We show that for low depth data, single-cell techniques based on zero-inflation model deteriorate the performance, whereas the analysis of uncorrected data using limmatrend, Wilcoxon test and fixed effects model performs well. We suggest several high-performance methods under different conditions based on various simulation and real data analyses. Additionally, we demonstrate that differential expression analysis for a specific cell type outperforms that of large-scale bulk sample data in prioritizing disease-related genes.

Suggested Citation

  • Hai C. T. Nguyen & Bukyung Baik & Sora Yoon & Taesung Park & Dougu Nam, 2023. "Benchmarking integration of single-cell differential expression," Nature Communications, Nature, vol. 14(1), pages 1-16, December.
  • Handle: RePEc:nat:natcom:v:14:y:2023:i:1:d:10.1038_s41467-023-37126-3
    DOI: 10.1038/s41467-023-37126-3
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    References listed on IDEAS

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    1. Jordan W. Squair & Matthieu Gautier & Claudia Kathe & Mark A. Anderson & Nicholas D. James & Thomas H. Hutson & Rémi Hudelle & Taha Qaiser & Kaya J. E. Matson & Quentin Barraud & Ariel J. Levine & Gio, 2021. "Confronting false discoveries in single-cell differential expression," Nature Communications, Nature, vol. 12(1), pages 1-15, December.
    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.
    3. Martin Jinye Zhang & Vasilis Ntranos & David Tse, 2020. "Determining sequencing depth in a single-cell RNA-seq experiment," Nature Communications, Nature, vol. 11(1), pages 1-11, December.
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