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High-Dimensional Gaussian Graphical Regression Models with Covariates.

Jingfei Zhang1, Yi Li2

  • 1Department of Management Science, University of Miami, Coral Gables, FL 33146.

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|December 25, 2023
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Summary
This summary is machine-generated.

We introduce a Gaussian graphical regression model to link network structures with external factors. This method reveals how covariates influence gene networks in cancer studies, improving network analysis.

Keywords:
Gaussian graphical model with covariatesco-expression QTLnon-asymptotic convergence ratesparse group lassosubject-specific Gaussian graphical model

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Area of Science:

  • Statistics
  • Genomics
  • Bioinformatics

Background:

  • Gaussian graphical models are prevalent but lack covariate integration.
  • Linking network structures to external factors like genetic variants is challenging.

Purpose of the Study:

  • To propose a Gaussian graphical regression model connecting network structures to covariates.
  • To analyze how genetic variants and clinical conditions modulate gene networks.

Main Methods:

  • Regressing both mean and precision matrices on covariates.
  • Implementing simultaneous sparsity for covariate effects on the precision matrix.
  • Establishing variable selection consistency and convergence rates.

Main Results:

  • The model recovers population-level and subject-level gene networks.
  • Demonstrated utility in co-expression quantitative trait locus (QTL) studies.
  • Variable selection consistency and convergence rates were established.

Conclusions:

  • The proposed method effectively links network structures to external covariates.
  • Applicable to co-expression QTL studies, particularly in cancer research.
  • Enhances understanding of genetic and clinical influences on biological networks.