Integrative Analysis 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.Conclusions: 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.

RaJIVE: Robust Angle Based JIVE for Integrating Noisy Multi-Source Data

“…In such multi-source data, it is important to identify the variation patterns that are shared among the different data layers, arising from the underlying joint processes, and to separate them from the individual, source specific components of variation. Proper identification of both types of variation components can contribute to a more accurate interpretation of the underlying processes and yield better model predictions (Måge et al, 2019;Ponzi et al, 2020). Several methods have been proposed for this purpose of separating the common and the componentwise variations from multiple data sources (Lofsted et al, 2012;Schouteden et al, 2013;Feng et al, 2018;Tang and Allen, 2018;Fan et al, 2019); most of them are based on a common framework of matrix decomposition.…”