Non-iterative Joint and Individual Variation Explained

TL;DR

This paper introduces Non-iterative JIVE, a fast and robust method for simultaneously exploring joint and individual variation in multiple data blocks, improving integrative analysis especially in heterogeneous data like genomics.

Contribution

The paper presents a novel non-iterative approach that captures joint and individual variation using score subspaces and perturbation theory, enhancing robustness and computational efficiency.

Findings

Effective separation of joint and individual signals in TCGA data

Robustness to data heterogeneity without normalization

Revealed distinct tumor subtype characteristics

Abstract

Integrative analysis of disparate data blocks measured on a common set of experimental subjects is one major challenge in modern data analysis. This data structure naturally motivates the simultaneous exploration of the joint and individual variation within each data block resulting in new insights. For instance, there is a strong desire to integrate the multiple genomic data sets in The Cancer Genome Atlas (TCGA) to characterize the common and also the unique aspects of cancer genetics and cell biology for each source. In this paper we introduce Non-iterative Joint and Individual Variation Explained (Non-iterative JIVE), capturing both joint and individual variation within each data block. This is a major improvement over earlier approaches to this challenge in terms of a new conceptual understanding, much better adaption to data heterogeneity and a fast linear algebra computation. Important mathematical contributions are the use of score subspaces as the principal descriptors of variation structure and the use of perturbation theory as the guide for variation segmentation. This leads to a method which is robust against the heterogeneity among data blocks without a need for normalization. An application to TCGA data reveals different behaviors of each type of signal in characterizing tumor subtypes. An application to a mortality data set reveals interesting historical lessons.