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Multiple Augmented Reduced Rank Regression for Pan-Cancer Analysis.

Jiuzhou Wang, Eric F Lock

    Arxiv
    |September 11, 2023
    PubMed
    Summary

    Combining multiple datasets with multiple augmented reduced rank regression (maRRR) enhances statistical power and scientific insight. This flexible method concurrently learns covariate-driven and auxiliary variation for high-dimensional data across cohorts.

    Area of Science:

    • Statistics
    • Bioinformatics
    • Genomics

    Background:

    • Integrating multiple datasets yields more powerful and informative analyses than separate studies.
    • High-dimensional data across cohorts requires methods that comprehensively address variation architectures.

    Approach:

    • Propose multiple augmented reduced rank regression (maRRR), a flexible matrix regression and factorization technique.
    • Utilize a structured nuclear norm objective inspired by random matrix theory.
    • Develop a framework that concurrently learns covariate-driven and auxiliary structured variation, allowing shared or cohort-specific terms.

    Key Points:

    • maRRR unifies and extends existing methods like reduced rank regression and multi-matrix factorization.
    • Includes augmented reduced rank regression (aRRR) for single-dataset analysis as a special case.

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  • Simulations show significant power gains by combining datasets and accounting for structured variation.

    • Conclusions:

    • maRRR applied to pan-cancer gene expression data from TCGA demonstrates strong performance in prediction and imputation.
    • The method reveals novel insights into mutation-driven and auxiliary variation, both shared and specific across cancer types.