Simulating Omic Data Structures for Network Analysis

In systems biology, where a main goal is acquiring knowledge of biological systems, one of the challenges is to infer relationships between biological components. Network analysis tools are widely used for this purpose. A network is the representation of the structure of the data and is typically recovered by correlation patterns. Here, we want to investigate how network modelling techniques for omic data perform when we mix different datasets for example metabolites and gene expressions, or copy number variation and gene expression.

For assessing the performance of network estimation methods on different scenarios, we simulate omic data structures under different settings. We will use existing packages for simulations of correlated datasets. By using a linear model based approach containing various sources of randomness for example measurement error, we aim at generating sets of variables. Perturbation on the network edges can be used for simulating different network structures for a considered phenotype. The network simulation performance will be studied by checking the similarity of the nodes’ profiles with respect the true network and subject to the different sources of variation.

The methods will be illustrated by analysing real datasets.