151. High-dimensional integrative copula discriminant analysis for multiomics data
- Author
-
Hao Chen, Jiadong Ji, Yufeng Shi, Hao Sun, Xinsheng Zhang, Yong He, and Lei Liu
- Subjects
Statistics and Probability ,Integrative omics ,Epidemiology ,Computer science ,Gaussian ,Normal Distribution ,High dimensional ,Machine learning ,computer.software_genre ,01 natural sciences ,Article ,Copula (probability theory) ,Omics data ,010104 statistics & probability ,03 medical and health sciences ,symbols.namesake ,Human health ,0302 clinical medicine ,Humans ,030212 general & internal medicine ,0101 mathematics ,business.industry ,Discriminant Analysis ,DNA Methylation ,Linear discriminant analysis ,symbols ,Artificial intelligence ,business ,computer - Abstract
Multiomics or integrative omics data have been increasingly common in biomedical studies, holding a promise in better understanding human health and disease. In this article, we propose an integrative copula discrimination analysis classifier in the context of two-class classification, which relaxes the common Gaussian assumption and gains power by borrowing information from multiple omics data types in discriminant analysis. Numerical studies are conducted to assess the finite sample performance of the new classifier. We apply our model to the Religious Orders Study and Memory and Aging Project (ROSMAP) Study, integrating gene expression and DNA methylation data for better prediction.
- Published
- 2020