1. Computational and Systems Biology
  2. Neuroscience
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Discovering and deciphering relationships across disparate data modalities

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Cite this article as: eLife 2019;8:e41690 doi: 10.7554/eLife.41690

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

  1. 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.
    ORCID icon "This ORCID iD identifies the author of this article:" 0000-0003-2487-6237
  2. Eric W Bridgeford

    Department of Biostatistics, Johns Hopkins University, Baltimore, United States
    Competing interests
    The authors declare that no competing interests exist.
  3. Qing Wang

    Department of Oncology, Johns Hopkins University, Baltimore, United States
    Competing interests
    The authors declare that no competing interests exist.
  4. 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.
  5. Mauro Maggioni

    Department of Mathematics, Johns Hopkins University, Baltimore, United States
    Competing interests
    The authors declare that no competing interests exist.
  6. 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.

Reviewing Editor

  1. Dane Taylor, University of Buffalo, United States

Publication history

  1. Received: September 3, 2018
  2. Accepted: January 14, 2019
  3. Accepted Manuscript published: January 15, 2019 (version 1)
  4. Version of Record published: February 22, 2019 (version 2)

Copyright

© 2019, Vogelstein et al.

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.

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    Background:

    Predicting neurological recovery after spinal cord injury (SCI) is challenging. Using topological data analysis, we have previously shown that mean arterial pressure (MAP) during SCI surgery predicts long-term functional recovery in rodent models, motivating the present multicenter study in patients.

    Methods:

    Intra-operative monitoring records and neurological outcome data were extracted (n = 118 patients). We built a similarity network of patients from a low-dimensional space embedded using a non-linear algorithm, Isomap, and ensured topological extraction using persistent homology metrics. Confirmatory analysis was conducted through regression methods.

    Results:

    Network analysis suggested that time outside of an optimum MAP range (hypotension or hypertension) during surgery was associated with lower likelihood of neurological recovery at hospital discharge. Logistic and LASSO (least absolute shrinkage and selection operator) regression confirmed these findings, revealing an optimal MAP range of 76–[104-117] mmHg associated with neurological recovery.

    Conclusions:

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    Funding:

    NIH/NINDS: R01NS088475 (ARF); R01NS122888 (ARF); UH3NS106899 (ARF); Department of Veterans Affairs: 1I01RX002245 (ARF), I01RX002787 (ARF); Wings for Life Foundation (ATE, ARF); Craig H. Neilsen Foundation (ARF); and DOD: SC150198 (MSB); SC190233 (MSB); DOE: DE-AC02-05CH11231 (DM).