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Reconstruction of molecular network evolution from cross-sectional omics data.

Mehran Aflakparast1, Mathisca C M de Gunst1, Wessel N van Wieringen1,2

  • 1Department of Mathematics, Vrije Universiteit Amsterdam, De Boelelaan 1081a, 1081 HV, Amsterdam, The Netherlands.

Biometrical Journal. Biometrische Zeitschrift
|January 11, 2018
PubMed
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This study reconstructs gene-gene interaction networks across cancer stages using Gaussian graphical models. The method identifies network changes during cancer progression, aiding in understanding disease evolution.

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Gaussian graphical modelconditional (in)dependencefused ridgemixture modelℓ2-penalization

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Area of Science:

  • Genomics
  • Bioinformatics
  • Computational Biology

Background:

  • Understanding cancer evolution requires analyzing patient traits across disease stages.
  • Reconstructing gene-gene interaction networks from omics data is complex, especially with non-homogeneous patient groups.
  • Gene-gene interaction networks may change dynamically during cancer progression.

Purpose of the Study:

  • To operationalize the estimation of stage-wise mixtures of Gaussian graphical models (GGMs) from high-dimensional omics data.
  • To develop a robust methodology for identifying dynamic changes in gene-gene interaction networks across different cancer stages.
  • To apply the developed method to identify gene-gene interaction network alterations in prostate cancer progression.

Main Methods:

  • Utilized a fused ridge penalized Expectation-Maximization (EM) algorithm for fitting mixtures of GGMs.
  • Employed cross-validation for selecting optimal fused ridge penalty parameters.
  • Proposed estimation procedures were evaluated for consistency and performance through simulations.

Main Results:

  • The proposed estimation procedures demonstrated consistency in estimating stage-wise GGMs.
  • Simulation studies confirmed the performance of the methodology in various aspects.
  • The method successfully identified gene-gene interaction network changes in the transition from normal to prostate cancer tissue.

Conclusions:

  • The developed methodology provides a robust framework for analyzing dynamic gene-gene interaction networks across disease stages.
  • This approach enhances the understanding of cancer evolution by revealing network alterations.
  • The application to prostate cancer data illustrates the practical utility in identifying clinically relevant network changes.