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Integrative analysis with a system of semiparametric projection non-linear regression models

Author

Listed:
  • Yuan Ao
  • Wu Tianmin
  • Fang Hong-Bin
  • Tan Ming T.

    (Department of Biostatistics, Bioinformatics and Biomathematics, Georgetown University, 20057Washington DC, USA)

Abstract

In integrative analysis parametric or nonparametric methods are often used. The former is easier for interpretation but not robust, while the latter is robust but not easy to interpret the relationships among the different types of variables. To combine the advantages of both methods and for flexibility, here a system of semiparametric projection non-linear regression models is proposed for the integrative analysis, to model the innate coordinate structure of these different types of data, and a diagnostic tool is constructed to classify new subjects to the case or control group. Simulation studies are conducted to evaluate the performance of the proposed method, and shows promising results. Then the method is applied to analyze a real omics data from The Cancer Genome Atlas study, compared the results with those from the similarity network fusion, another integrative analysis method, and results from our method are more reasonable.

Suggested Citation

  • Yuan Ao & Wu Tianmin & Fang Hong-Bin & Tan Ming T., 2021. "Integrative analysis with a system of semiparametric projection non-linear regression models," The International Journal of Biostatistics, De Gruyter, vol. 17(1), pages 55-74, May.
    • Handle: RePEc:bpj:ijbist:v:17:y:2021:i:1:p:55-74:n:3
    DOI: 10.1515/ijb-2019-0124
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