sJIVE: Supervised Joint and Individual Variation Explained

by   Elise F. Palzer, et al.

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 .


page 1

page 2

page 3

page 4


Angle-Based Joint and Individual Variation Explained

Integrative analysis of disparate data blocks measured on a common set o...

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

With increasing availability of high dimensional, multi-source data, the...

Joint and individual variation explained (JIVE) for integrated analysis of multiple data types

Research in several fields now requires the analysis of data sets in whi...

Bayesian predictive modeling of multi-source multi-way data

We develop a Bayesian approach to predict a continuous or binary outcome...

Multi-View Independent Component Analysis with Shared and Individual Sources

Independent component analysis (ICA) is a blind source separation method...

Scalable Randomized Kernel Methods for Multiview Data Integration and Prediction

We develop scalable randomized kernel methods for jointly associating da...

Joint and Individual Component Regression

Multi-group data are commonly seen in practice. Such data structure cons...

Please sign up or login with your details

Forgot password? Click here to reset