Trends in Genetics
ReviewFrom ‘differential expression’ to ‘differential networking’ – identification of dysfunctional regulatory networks in diseases
Section snippets
Dysfunctional networks in disease
To understand the roles of genes in complex human diseases, genes need to be studied in the context of the regulatory systems they are involved in [1]. Regulatory systems inside cells can be effectively abstracted into networks (Box 1). Such regulatory networks hold the potential to provide the cellular context of all genes of interest and give a means to identify specific subnetworks that are dysfunctional in a given disease state. It is therefore not surprising that several recent
From differential expression to differential coexpression
Recent investigations have gone beyond testing for differential expression and aim to elucidate dysfunctional regulatory networks in disease. Instead of focusing on differences in mean gene expression levels, the goal is to identify differences in their coexpression patterns (commonly quantified by pairwise correlations) in healthy and disease-affected samples (Box 4). Pairwise relationships between gene expression levels result from regulatory relationships among the genes, and identifying
From differential coexpression to differential networking
Identifying differential coexpression is the first step towards identifying differential gene networks. As Bill Shipley insightfully states in his book Cause and Correlation in Biology[47]: ‘As with shadows, these correlational patterns are incomplete – and potentially ambiguous – projections of the original causal processes. As with shadows, we can infer much about the underlying causal process if we can learn to study their details, to sharpen their contours, and especially if we can study
Acknowledgements
I kindly thank Paolo Uva, Diogo Camacho, three anonymous reviewers and the editor for critical reading of the manuscript and their insightful suggestions. This work was supported in part by the Regional Authorities of Sardinia (see: http://www.sardegnaricerche.it/).
Glossary
- Differential coexpression
- the observation that the correlation (or other measure of association) between the expression levels of two (or more) genes is significantly different (higher or lower) in case (e.g. disease) and control (e.g. healthy) samples.
- Differential expression
- the observation that the mean expression level of a given gene (or set of genes) is significantly different (higher or lower) between case and control samples.
- False discovery rate (FDR)
- expected ratio of false positive
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