Integrative clustering of high-dimensional data with joint and individual clusters, with an application to the Metabric study

TL;DR

This paper introduces JIC, a novel integrative clustering method that simultaneously estimates joint and individual clusters, demonstrated on cancer data to reveal meaningful patient subgroups linked to survival.

Contribution

The paper presents JIC, an extension of JIVE, capable of identifying both shared and data-specific clusters, outperforming existing methods like iCluster in simulations.

Findings

JIC effectively detects both joint and individual clusters in high-dimensional data.

Application to Metabric data revealed clinically relevant clusters related to survival.

JIC outperforms iCluster in simulation studies when both cluster types are present.

Abstract

When measuring a range of different genomic, epigenomic, transcriptomic and other variables, an integrative approach to analysis can strengthen inference and give new insights. This is also the case when clustering patient samples, and several integrative cluster procedures have been proposed. Common for these methodologies is the restriction of a joint cluster structure, which is equal for all data layers. We instead present Joint and Individual Clustering (JIC), which estimates both joint and data type-specific clusters simultaneously, as an extension of the JIVE algorithm (Lock et. al, 2013). The method is compared to iCluster, another integrative clustering method, and simulations show that JIC is clearly advantageous when both individual and joint clusters are present. The method is used to cluster patients in the Metabric study, integrating gene expression data and copy number aberrations (CNA). The analysis suggests a division into three joint clusters common for both data types and seven independent clusters specific for CNA. Both the joint and CNA-specific clusters are significantly different with respect to survival, also when adjusting for age and treatment.