Jackstraw Inference for AJIVE Data Integration

TL;DR

This paper introduces a hypothesis testing method based on jackstraw for AJIVE data integration, enabling identification of significant features associated with joint and individual data behaviors in high-dimensional multi-genomic datasets.

Contribution

It proposes a novel jackstraw-based hypothesis test for feature significance in AJIVE, addressing a key challenge in understanding data relationships.

Findings

Effective identification of significant features in multi-genomic data

Demonstrated method's utility on real cancer datasets

Enhanced interpretability of AJIVE results

Abstract

In the age of big data, data integration is a critical step especially in the understanding of how diverse data types work together and work separately. Among data integration methods, the Angle-Based Joint and Individual Variation Explained (AJIVE) approach is particularly attractive because it not only studies joint behavior but also individual behavior. Typically AJIVE scores indicate important relationships between data objects, such as clusters. An important challenge is understanding which features, i.e. variables, are associated with those relationships. This challenge is addressed by the proposal of a hypothesis test for assessing statistical significance of features. The new test is inspired by the related jackstraw method developed for Principal Component Analysis. We use a high-dimensional muti-genomic cancer data set as our strong motivation and deep illustration of the methodology.