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

Rainer Opgen-Rhein, Korbinian Strimmer

    Research output: Contribution to journalArticlepeer-review

    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.
    Original languageEnglish
    JournalBMC Systems Biology
    Volume1
    Issue number37
    DOIs
    Publication statusPublished - 6 Aug 2007

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