title: Graph reconstruction using covariance-based methods.
abstract: Methods based on correlation and partial correlation are today employed in the reconstruction of a statistical interaction graph from high-throughput omics data. These dedicated methods work well even for the case when the number of variables exceeds the number of samples. In this study, we investigate how the graphs extracted from covariance and concentration matrix estimates are related by using Neumann series and transitive closure and through discussing concrete small examples. Considering the ideal case where the true graph is available, we also compare correlation and partial correlation methods for large realistic graphs. In particular, we perform the comparisons with optimally selected parameters based on the true underlying graph and with data-driven approaches where the parameters are directly estimated from the data.
authors.affiliation: Department of Electrical Engineering and Information Technology, Technische Universität Darmstadt, Rundeturmstr. 12, Darmstadt, 64283 Germany ; Department of Biology, Technische Universität Darmstadt, Schnittspahnstr. 10, Darmstadt, 64287 Germany.
authors.forename: Nurgazy
authors.initials: N
authors.lastname: Sulaimanov
authors.affiliation: Department of Electrical Engineering and Information Technology, Technische Universität Darmstadt, Rundeturmstr. 12, Darmstadt, 64283 Germany ; Department of Biology, Technische Universität Darmstadt, Schnittspahnstr. 10, Darmstadt, 64287 Germany.
authors.forename: Heinz
authors.initials: H
authors.lastname: Koeppl
keywords: Concentration and covariance graphs; High-dimensional graph reconstruction methods