Reconstruction of molecular network evolution from cross-sectional omics data

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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)547-563
Number of pages17
JournalBiometrical Journal
Issue number3
Early online date10 Jan 2018
Publication statusPublished - May 2018


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