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
Issue number3
DOIs
Publication statusPublished - 2017

Fingerprint Dive into the research topics of 'Reconstruction of molecular network evolution from cross-sectional omics data'. Together they form a unique fingerprint.

Cite this