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Basics of Multivariate Analysis in Neuroimaging Data
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Testing for association in multiview network data.

Lucy L Gao1, Daniela Witten2, Jacob Bien3

  • 1Department of Statistics and Actuarial Science, University of Waterloo, Waterloo, Ontario, Canada.

Biometrics
|April 1, 2021
PubMed
Summary
This summary is machine-generated.

This study introduces a new statistical test to determine if community structures in multiple networks are related. Applying this to protein interaction data revealed a weak association between different network views.

Keywords:
community detectiondata integrationmultiview datanode covariatesstochastic block model

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Area of Science:

  • Network science
  • Statistical modeling
  • Bioinformatics

Background:

  • Multiview network data analysis often assumes relatedness between network views.
  • Evaluating this assumption is crucial for accurate interpretation of complex biological systems.

Purpose of the Study:

  • To develop statistical tools for assessing the association between latent community structures in multiple networks.
  • To test the independence of community memberships across different network views.

Main Methods:

  • Extension of the stochastic block model (SBM) to a two-view setting.
  • Development of a novel hypothesis test for independence of latent community memberships.
  • Application to protein-protein interaction data from the HINT database.

Main Results:

  • A weak association was found between protein community memberships derived from binary interaction data and cocomplex association data.
  • The proposed hypothesis test provides a quantitative measure of association between network views.

Conclusions:

  • The developed methods offer a robust framework for analyzing multiview network data.
  • Findings suggest that different types of biological network data capture partially overlapping community structures.