sJIVE: Supervised Joint and Individual Variation Explained

TL;DR

sJIVE is a novel method that simultaneously uncovers shared and source-specific structures in multi-source data and builds predictive models for outcomes, outperforming existing methods especially with noisy data.

Contribution

It introduces a supervised approach to jointly analyze multi-source data, capturing both shared and individual structures while directly modeling the outcome.

Findings

sJIVE outperforms existing methods in noisy data scenarios.

Application to COPDGene data identified predictive gene and protein patterns.

Functions for sJIVE are available in the R.JIVE package.

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. We propose a method called supervised joint and individual variation explained (sJIVE) 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. These two components are weighted to compromise between explaining variation in the multi-source data and in the outcome. Simulations show sJIVE to outperform existing methods when large amounts of noise are present in the multi-source data. An application to data from the COPDGene study reveals gene expression and proteomic patterns that are predictive of lung function. Functions to perform sJIVE are included in the R.JIVE package, available online at http://github.com/lockEF/r.jive .