  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 .
