Sparse graphical Gaussian modeling of the isoprenoid gene network in Arabidopsis thaliana

Abstract

We present a novel graphical Gaussian modeling approach for reverse engineering of genetic regulatory networks with many genes and few observations. When applying our approach to infer a gene network for isoprenoid biosynthesis in Arabidopsis thaliana, we detect modules of closely connected genes and candidate genes for possible cross-talk between the isoprenoid pathways. Genes of downstream pathways also fit well into the network. We evaluate our approach in a simulation study and using the yeast galactose network.

An Arabidopsis gene network based on the graphical Gaussian model

“…GGM-based gene network structures at the genome level for Arabidopsis have not been presented before, but networks for selected pathways have been constructed (Wille et al 2004;Nikiforova et al 2005;Li and Gui 2006;Gutierrez et al 2007). The models presented here, when queried for nodes in these pathways, revealed significant overlap (data not shown).…”

“…Classical GGM theory cannot accommodate settings for P >> n (Schäfer and Strimmer 2005a;Wille and Buhlmann 2006). Recently, GGM with a limited-order partial correlation function, which estimates correlations conditional on one or two, but not all other genes, has been developed to infer gene networks from Arabidopsis and yeast transcript profiles (Magwene and Kim 2004;Wille et al 2004). Another way to tackle the small sampling problem is to infer GGM with regularization and moderation (Schäfer and Strimmer 2005b).…”

Comparative study of discretization methods of microarray data for inferring transcriptional regulatory networks

“…A number of algorithms have been proposed to infer the transcription networks, including Boolean Networks [2,3], Gaussian Networks [4], Bayesian Networks [5,6], and Dynamic Bayesian Networks [7]. Most algorithms require discrete data as input.…”

Positive Semidefinite Rank-Based Correlation Matrix Estimation With Application to Semiparametric Graph Estimation

“…This dataset includes 118 gene expression profiles from arabidposis thaliana that originally appeared in Wille et al (2004). Our analysis focuses on gene expression from 39 genes involved in two isoprenoid pathways: 16 from the mevalonate (MVA) pathway are located in the cytoplasm, 18 from the plastidial (MEP) pathway are located in the chloroplast, and 5 are located in the mitochondria.…”

“…Undirected graphical models provide a powerful framework for exploring relationships between a large number of variables (Lauritzen, 1996; Wille et al, 2004; Honorio et al, 2009). More specifically, we can represent a d -dimensional random vector X = ( X 1 , …, X d ) T by an undirected graph 𝒢 = ( V,E ), where V contains nodes corresponding to the d variables in X , and the edge set E describes the conditional independence relationships between X 1 , …, X d .…”