An empirical Bayes approach to inferring large-scale gene association networks.

@article{citeulike:1088995, abstract = {MOTIVATION: Genetic networks are often described statistically using graphical models (e.g. Bayesian networks). However, inferring the network structure offers a serious challenge in microarray analysis where the sample size is small compared to the number of considered genes. This renders many standard algorithms for graphical models inapplicable, and inferring genetic networks an 'ill-posed' inverse problem. METHODS: We introduce a novel framework for small-sample inference of graphical models from gene expression data. Specifically, we focus on the so-called graphical Gaussian models (GGMs) that are now frequently used to describe gene association networks and to detect conditionally dependent genes. Our new approach is based on (1) improved (regularized) small-sample point estimates of partial correlation, (2) an exact test of edge inclusion with adaptive estimation of the degree of freedom and (3) a heuristic network search based on false discovery rate multiple testing. Steps (2) and (3) correspond to an empirical Bayes estimate of the network topology. RESULTS: Using computer simulations, we investigate the sensitivity (power) and specificity (true negative rate) of the proposed framework to estimate GGMs from microarray data. This shows that it is possible to recover the true network topology with high accuracy even for small-sample datasets. Subsequently, we analyze gene expression data from a breast cancer tumor study and illustrate our approach by inferring a corresponding large-scale gene association network for 3883 genes.}, address = {Department of Statistics, University of Munich, Ludwigstrasse 33, D-80539 Munich, Germany.}, author = {Sch\"{a}fer, J. and Strimmer, K.}, citeulike-article-id = {1088995}, issn = {1367-4803}, journal = {Bioinformatics}, keywords = {genetic\_regulatory\_networks}, month = {March}, number = {6}, pages = {754--764}, posted-at = {2007-02-06 00:59:40}, priority = {5}, title = {An empirical Bayes approach to inferring large-scale gene association networks.}, url = {http://view.ncbi.nlm.nih.gov/pubmed/15479708}, volume = {21}, year = {2005} }

TY - JOUR ID - citeulike:1088995 L3 - citeulike-article-id:1088995 N2 - MOTIVATION: Genetic networks are often described statistically using graphical models (e.g. Bayesian networks). However, inferring the network structure offers a serious challenge in microarray analysis where the sample size is small compared to the number of considered genes. This renders many standard algorithms for graphical models inapplicable, and inferring genetic networks an 'ill-posed' inverse problem. METHODS: We introduce a novel framework for small-sample inference of graphical models from gene expression data. Specifically, we focus on the so-called graphical Gaussian models (GGMs) that are now frequently used to describe gene association networks and to detect conditionally dependent genes. Our new approach is based on (1) improved (regularized) small-sample point estimates of partial correlation, (2) an exact test of edge inclusion with adaptive estimation of the degree of freedom and (3) a heuristic network search based on false discovery rate multiple testing. Steps (2) and (3) correspond to an empirical Bayes estimate of the network topology. RESULTS: Using computer simulations, we investigate the sensitivity (power) and specificity (true negative rate) of the proposed framework to estimate GGMs from microarray data. This shows that it is possible to recover the true network topology with high accuracy even for small-sample datasets. Subsequently, we analyze gene expression data from a breast cancer tumor study and illustrate our approach by inferring a corresponding large-scale gene association network for 3883 genes. IS - 6 JF - Bioinformatics SN - 1367-4803 AD - Department of Statistics, University of Munich, Ludwigstrasse 33, D-80539 Munich, Germany. EP - 764 TI - An empirical Bayes approach to inferring large-scale gene association networks. VL - 21 SP - 754 KW - genetic_regulatory_networks AU - Schäfer, J AU - Strimmer, K PY - 2005/03// UR - http://view.ncbi.nlm.nih.gov/pubmed/15479708 ER -