Multi-source Data Decomposition and Prediction for Various Data Types

Abstract

Analyzing multi-source data, which are multiple views of data on the same subjects, has become increasingly common in molecular biomedical research. Recent methods have sought to uncover underlying structure and relationships within and/or between the data sources, and other methods have sought to build a predictive model for an outcome using all sources. However, existing methods that do both are presently limited because they either (1) only consider data structure shared by all datasets while ignoring structures unique to each source, or (2) they extract underlying structures first without consideration to the outcome. In Chapter 2, we propose a method called supervised joint and individual variation explained (sJIVE) [1] that can simultaneously (1) identify shared (joint) and source-specific (individual) underlying structure and (2) build a linear prediction model for an outcome using these structures. Simulations show sJIVE to outperform existing methods when large amounts of noise are present in the multi-source data, and an application to data from the COPDGene study reveals gene expression and proteomic patterns that are predictive of lung function. In Chapter 3, we extend sJIVE to allow for binary and/or count data and to incorporate sparsity using a method called sparse exponential family sJIVE (sesJIVE). Simulations show the non-sparse version of sesJIVE to outperform existing methods when the data is Bernoulli- or Poisson- distributed with large amounts of noise, and sesJIVE outperforms other JIVE-based methods in our application with COPDGene data. Lastly, chapter 4 will discuss our R package, sup.r.jive, that implements sJIVE, sesJIVE, and a previous method called JIVE-Predict [2]. Summary and visualization tools are also available within our R package for all three methods.