Integrative analyses of multi omics data improves model predictions: an application to lung cancer
Abstract Background Cancer genomic studies often include data collected from several omics platforms. Each omics data source contributes to the understanding of the underlying biological process via source specific "individual" patterns of variability. At the same time, statistical associations and potential interactions among the different data sources can reveal signals from common biological processes that might not be identified by single source analyses. These common patterns of variability are referred to as "shared" or "joint" To capture both contributions of variance, integrative dimension reduction techniques are needed. Integrated PCA is a model based generalization of principal components analysis that separates shared and source specific variance by iteratively estimating covariance structures from a matrix normal distribution. Angle based JIVE is a matrix factorization method that decomposes joint and individual variation by permutation of row subspaces. We apply these techniques to identify joint and individual contributions of DNA methylation, miRNA and mRNA expression collected from blood samples in a lung cancer case control study nested within the Norwegian Woman and Cancer (NOWAC) cohort study. Results In this work, we show how an integrative analysis that preserves both components of variation is more appropriate than analyses considering uniquely individual or joint components. Our results show how both joint and individual components contribute to a better quality of model predictions, and facilitate the interpretation of the underlying biological processes. Conclusion When compared to a non integrative analysis of the three omics sources, integrative models that simultaneously include joint and individual components result in better prediction of cancer status and metastatic cancer at diagnosis.