Email updates

Keep up to date with the latest news and content from BMC Systems Biology and BioMed Central.

Open Access Highly Accessed Methodology article

From correlation to causation networks: a simple approximate learning algorithm and its application to high-dimensional plant gene expression data

Rainer Opgen-Rhein1* and Korbinian Strimmer2

Author Affiliations

1 Department of Statistics, Ludwig-Maximilians-Universität München, Ludwigstraße 33, D-80539 München, Germany

2 Institute for Medical Informatics, Statistics and Epidemiology (IMISE), University of Leipzig, Härtelstr. 16-18, 04107 Leipzig, Germany

For all author emails, please log on.

BMC Systems Biology 2007, 1:37  doi:10.1186/1752-0509-1-37

Published: 6 August 2007

Abstract

Background

The use of correlation networks is widespread in the analysis of gene expression and proteomics data, even though it is known that correlations not only confound direct and indirect associations but also provide no means to distinguish between cause and effect. For "causal" analysis typically the inference of a directed graphical model is required. However, this is rather difficult due to the curse of dimensionality.

Results

We propose a simple heuristic for the statistical learning of a high-dimensional "causal" network. The method first converts a correlation network into a partial correlation graph. Subsequently, a partial ordering of the nodes is established by multiple testing of the log-ratio of standardized partial variances. This allows identifying a directed acyclic causal network as a subgraph of the partial correlation network. We illustrate the approach by analyzing a large Arabidopsis thaliana expression data set.

Conclusion

The proposed approach is a heuristic algorithm that is based on a number of approximations, such as substituting lower order partial correlations by full order partial correlations. Nevertheless, for small samples and for sparse networks the algorithm not only yield sensible first order approximations of the causal structure in high-dimensional genomic data but is also computationally highly efficient.

Availability and Requirements

The method is implemented in the "GeneNet" R package (version 1.2.0), available from CRAN and from http://strimmerlab.org/software/genets/ webcite. The software includes an R script for reproducing the network analysis of the Arabidopsis thaliana data.