Discovering and deciphering relationships across disparate data modalities

Abstract
Data availability
Article and author information
Metrics

Abstract

Understanding the relationships between different properties of data, such as whether a genome or connectome has information about disease status, is increasingly important. While existing approaches can test whether two properties are related, they may require unfeasibly large sample sizes and often are not interpretable. Our approach, 'Multiscale Graph Correlation' (MGC), is a dependence test that juxtaposes disparate data science techniques, including k-nearest neighbors, kernel methods, and multiscale analysis. Other methods may require double or triple the number of samples to achieve the same statistical power as MGC in a benchmark suite including high-dimensional and nonlinear relationships, with dimensionality ranging from 1 to 1000. Moreover, MGC uniquely characterizes the latent geometry underlying the relationship, while maintaining computational efficiency. In real data, including brain imaging and cancer genetics, MGC detects the presence of a dependency and provides guidance for the next experiments to conduct.

Data availability

To facilitate reproducibility, we make all datasets available from: https://github.com/neurodata/MGC-paper/tree/master/Data/Preprocessed

Article and author information

Author details

Joshua T Vogelstein

Department of Biomedical Engineering, Johns Hopkins University, Baltimore, United States

For correspondence
jovo@jhu.edu

Competing interests
The authors declare that no competing interests exist.

"This ORCID iD identifies the author of this article:" 0000-0003-2487-6237
Eric W Bridgeford

Department of Biostatistics, Johns Hopkins University, Baltimore, United States

Competing interests
The authors declare that no competing interests exist.
Qing Wang

Department of Oncology, Johns Hopkins University, Baltimore, United States

Competing interests
The authors declare that no competing interests exist.
Carey E Priebe

Department of Applied Mathematics and Statistics, Johns Hopkins University, Baltimore, United States

Competing interests
The authors declare that no competing interests exist.
Mauro Maggioni

Department of Mathematics, Johns Hopkins University, Baltimore, United States

Competing interests
The authors declare that no competing interests exist.
Cencheng Shen

Department of Applied Mathematics and Statistics, Johns Hopkins University, Baltimore, United States

Competing interests
The authors declare that no competing interests exist.

Funding

Child Mind Institute Endeavor Scientist Program

Joshua T Vogelstein

National Science Foundation

Joshua T Vogelstein

Defense Advanced Research Projects Agency

Joshua T Vogelstein

Office of Naval Research

Joshua T Vogelstein

Air Force Office of Scientific Research

Joshua T Vogelstein

The funders had no role in study design, data collection and interpretation, or the decision to submit the work for publication.

Copyright

This article is distributed under the terms of the Creative Commons Attribution License permitting unrestricted use and redistribution provided that the original author and source are credited.