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Improved reconstruction of molecular networks with Gaussian graphical models

PhD ceremony:Mr V.A. (Victor) Bernal Arzola
When:April 25, 2022
Supervisors:P.L. (Peter) Horvatovich, Prof, M.A. (Marco) Grzegorczyk, Prof
Co-supervisor:prof. dr. V. (Victor) Guryev
Where:Academy building RUG
Faculty:Science and Engineering
Improved reconstruction of molecular networks with Gaussian
graphical models

This doctoral thesis contributes to the advancement of statistical analyses of gene expression profiles using Gaussian graphical models (GGMs). The main focus has been on studying the properties of GGMs obtained with the Ledoit Wolf (LW) shrinkage. The premise is that, despite its advantages, the shrinkage approach introduces some biases that have not been studied in sufficient detail. These biases have the potential of obscuring the interpretation of the network structure and impeding the validation of earlier analyses. In this sense, this thesis may be considered a continuation of the works of Schäfer and Strimmer, where the LW-shrinkage was originally used to model Gene regulatory networks (GRN) with GGMs.

The thesis is organized as follows; Chapter 1 presents a test of statistical significance for GGMs based on the LW-shrinkage. Here the probability density of the ‘shrunk’ partial correlation is derived by means of geometric arguments. In Chapter 2 a network analysis of (matched) nasal and bronchial expression profiles is presented. The method developed in the previous chapter is employed to explore whether expression profiles from nasal epithelial cells can be used as a proxy for bronchial epithelial cells. Chapter 3 shows the existence of a non-linear bias on the partial correlations obtained with the LW-shrinkage. This bias is removed via ‘un-shrinking’; a new concept which de-regularizes the partial correlation. In Chapter 4 the LW-shrinkage is revisited from a data-level perspective. The goal is to explore the correspondence between the shrinkage-based covariance matrix and the dataset.