Reconstruction of molecular network evolution from cross-sectional omics data

Research output: Contribution to JournalArticleAcademicpeer-review

Abstract

Cross-sectional studies may shed light on the evolution of a disease like cancer
through the comparison of patient traits among disease stages. This problem is especially challenging when a gene–gene interaction network needs to be reconstructed from omics data, and, in addition, the patients of each stage need not form a homogeneous group. Here, the problem is operationalized as the estimation of stage-wise mixtures of Gaussian graphical models (GGMs) from high-dimensional data. These mixtures are fitted by a (fused) ridge penalized EM algorithm. The fused ridge penalty shrinks GGMs of contiguous stages. The (fused) ridge penalty parameters are chosen through cross-validation. The proposed estimation procedures are shown to be consistent and their performance in other respects is studied in simulation. The down-stream exploitation of the fitted GGMs is outlined. In a data illustration the methodology is employed to identify gene–gene interaction network changes in the transition from normal to cancer prostate tissue.
Original languageEnglish
Pages (from-to)1-17
Number of pages17
JournalBiometrical Journal
Volume2017
DOIs
Publication statusPublished - 2017

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Molecular Evolution
Network Evolution
Gaussian Model
Graphical Models
Ridge
Penalty
Homogeneous Groups
Prostate Cancer
High-dimensional Data
EM Algorithm
Interaction
Cross-validation
Exploitation
Methodology
Cross-sectional data
Graphical models
Network evolution
Simulation

Cite this

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title = "Reconstruction of molecular network evolution from cross-sectional omics data",
abstract = "Cross-sectional studies may shed light on the evolution of a disease like cancerthrough the comparison of patient traits among disease stages. This problem is especially challenging when a gene–gene interaction network needs to be reconstructed from omics data, and, in addition, the patients of each stage need not form a homogeneous group. Here, the problem is operationalized as the estimation of stage-wise mixtures of Gaussian graphical models (GGMs) from high-dimensional data. These mixtures are fitted by a (fused) ridge penalized EM algorithm. The fused ridge penalty shrinks GGMs of contiguous stages. The (fused) ridge penalty parameters are chosen through cross-validation. The proposed estimation procedures are shown to be consistent and their performance in other respects is studied in simulation. The down-stream exploitation of the fitted GGMs is outlined. In a data illustration the methodology is employed to identify gene–gene interaction network changes in the transition from normal to cancer prostate tissue.",
author = "M. Aflakparast and {de Gunst}, M.C.M. and {van Wieringen}, W.N.",
year = "2017",
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language = "English",
volume = "2017",
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Reconstruction of molecular network evolution from cross-sectional omics data. / Aflakparast, M.; de Gunst, M.C.M.; van Wieringen, W.N.

In: Biometrical Journal, Vol. 2017, 2017, p. 1-17.

Research output: Contribution to JournalArticleAcademicpeer-review

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AU - Aflakparast, M.

AU - de Gunst, M.C.M.

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AB - Cross-sectional studies may shed light on the evolution of a disease like cancerthrough the comparison of patient traits among disease stages. This problem is especially challenging when a gene–gene interaction network needs to be reconstructed from omics data, and, in addition, the patients of each stage need not form a homogeneous group. Here, the problem is operationalized as the estimation of stage-wise mixtures of Gaussian graphical models (GGMs) from high-dimensional data. These mixtures are fitted by a (fused) ridge penalized EM algorithm. The fused ridge penalty shrinks GGMs of contiguous stages. The (fused) ridge penalty parameters are chosen through cross-validation. The proposed estimation procedures are shown to be consistent and their performance in other respects is studied in simulation. The down-stream exploitation of the fitted GGMs is outlined. In a data illustration the methodology is employed to identify gene–gene interaction network changes in the transition from normal to cancer prostate tissue.

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