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Benchmarking tools for detecting longitudinal differential expression in proteomics data allows establishing a robust reproducibility optimization regression approach

Author

Listed:
  • Tommi Välikangas

    (University of Turku and Åbo Akademi University)

  • Tomi Suomi

    (University of Turku and Åbo Akademi University)

  • Courtney E. Chandler

    (University of Maryland – Baltimore)

  • Alison J. Scott

    (University of Maryland – Baltimore)

  • Bao Q. Tran

    (US Army 20th Support Command CBRNE Analytical and Remediation Activity)

  • Robert K. Ernst

    (University of Maryland – Baltimore)

  • David R. Goodlett

    (University of Victoria
    International Centre for Cancer Vaccine Science)

  • Laura L. Elo

    (University of Turku and Åbo Akademi University
    University of Turku)

Abstract

Quantitative proteomics has matured into an established tool and longitudinal proteomics experiments have begun to emerge. However, no effective, simple-to-use differential expression method for longitudinal proteomics data has been released. Typically, such data is noisy, contains missing values, and has only few time points and biological replicates. To address this need, we provide a comprehensive evaluation of several existing differential expression methods for high-throughput longitudinal omics data and introduce a Robust longitudinal Differential Expression (RolDE) approach. The methods are evaluated using over 3000 semi-simulated spike-in proteomics datasets and three large experimental datasets. In the comparisons, RolDE performs overall best; it is most tolerant to missing values, displays good reproducibility and is the top method in ranking the results in a biologically meaningful way. Furthermore, RolDE is suitable for different types of data with typically unknown patterns in longitudinal expression and can be applied by non-experienced users.

Suggested Citation

  • Tommi Välikangas & Tomi Suomi & Courtney E. Chandler & Alison J. Scott & Bao Q. Tran & Robert K. Ernst & David R. Goodlett & Laura L. Elo, 2022. "Benchmarking tools for detecting longitudinal differential expression in proteomics data allows establishing a robust reproducibility optimization regression approach," Nature Communications, Nature, vol. 13(1), pages 1-18, December.
    • Handle: RePEc:nat:natcom:v:13:y:2022:i:1:d:10.1038_s41467-022-35564-z
    DOI: 10.1038/s41467-022-35564-z
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    References listed on IDEAS

    as
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    2. Zhiyuan Xu & Xiaotong Shen & Wei Pan & for the Alzheimer's Disease Neuroimaging Initiative, 2014. "Longitudinal Analysis Is More Powerful than Cross-Sectional Analysis in Detecting Genetic Association with Neuroimaging Phenotypes," PLOS ONE, Public Library of Science, vol. 9(8), pages 1-13, August.
    3. Smyth Gordon K, 2004. "Linear Models and Empirical Bayes Methods for Assessing Differential Expression in Microarray Experiments," Statistical Applications in Genetics and Molecular Biology, De Gruyter, vol. 3(1), pages 1-28, February.
    4. Lu Cheng & Siddharth Ramchandran & Tommi Vatanen & Niina Lietzén & Riitta Lahesmaa & Aki Vehtari & Harri Lähdesmäki, 2019. "An additive Gaussian process regression model for interpretable non-parametric analysis of longitudinal data," Nature Communications, Nature, vol. 10(1), pages 1-11, December.
    5. R. A. Rigby & D. M. Stasinopoulos, 2005. "Generalized additive models for location, scale and shape," Journal of the Royal Statistical Society Series C, Royal Statistical Society, vol. 54(3), pages 507-554, June.
    6. Jasmin Straube & Alain-Dominique Gorse & PROOF Centre of Excellence Team & Bevan Emma Huang & Kim-Anh Lê Cao, 2015. "A Linear Mixed Model Spline Framework for Analysing Time Course ‘Omics’ Data," PLOS ONE, Public Library of Science, vol. 10(8), pages 1-20, August.
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