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Nonlinear ridge regression improves robustness of cell-type-specific differential expression studies

View ORCID ProfileFumihiko Takeuchi, Norihiro Kato
doi: https://doi.org/10.1101/2020.06.18.158758
Fumihiko Takeuchi
Department of Gene Diagnostics and Therapeutics, Research Institute, National Center for Global Health and Medicine (NCGM), Tokyo, Japan
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Norihiro Kato
Department of Gene Diagnostics and Therapeutics, Research Institute, National Center for Global Health and Medicine (NCGM), Tokyo, Japan
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Abstract

Background Epigenome-wide association studies (EWAS) and differential gene expression analyses are generally performed on tissue samples, which consist of multiple cell types. Cell-type-specific effects of a trait, such as disease, on the omics expression are of interest but difficult or costly to measure experimentally. By measuring omics data for the bulk tissue, cell type composition of a sample can be inferred statistically. Subsequently, cell-type-specific effects are estimated by linear regression that includes terms representing the interaction between the cell type proportions and the trait. This approach involves two issues, scaling and multicollinearity.

Results First, although cell composition is analyzed in linear scale, differential methylation/expression is analyzed suitably in the logit/log scale. To simultaneously analyze two scales, we developed nonlinear regression. Second, we show that the interaction terms are highly collinear, which is obstructive to ordinary regression. To cope with the multicollinearity, we applied ridge regularization. In simulated and real data, the improvement was modest by nonlinear regression and substantial by ridge regularization.

Conclusion Nonlinear ridge regression performed cell-type-specific association test on bulk omics data more robustly than previous methods. The omicwas package for R implements nonlinear ridge regression for cell-type-specific EWAS, differential gene expression and QTL analyses. The software is freely available from https://github.com/fumi-github/omicwas

Competing Interest Statement

The authors have declared no competing interest.

  • Abbreviations

    AUC
    area under the curve
    eQTL
    expression QTL
    EWAS
    epigenome-wide association study
    GEO
    Gene Expression Omnibus
    mQTL
    methylation QTL
    MSE
    mean squared error
    OLS
    ordinary least squares
    QTL
    quantitative trait locus
    ROC
    receiver operating characteristic
    SD
    standard deviation
    SNP
    single nucleotide polymorphism
  • Copyright 
    The copyright holder for this preprint is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made available under a CC-BY 4.0 International license.
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    Posted June 19, 2020.
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    Nonlinear ridge regression improves robustness of cell-type-specific differential expression studies
    Fumihiko Takeuchi, Norihiro Kato
    bioRxiv 2020.06.18.158758; doi: https://doi.org/10.1101/2020.06.18.158758
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    Nonlinear ridge regression improves robustness of cell-type-specific differential expression studies
    Fumihiko Takeuchi, Norihiro Kato
    bioRxiv 2020.06.18.158758; doi: https://doi.org/10.1101/2020.06.18.158758

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