Biological and Medical science is increasingly acknowledging the use of gene co-expression networks for the analysis of complex systems or diseases. In many studies, the goal is not only to describe a network, but to investigate how this network is changing under different conditions, with respect to certain diseases, or between different species. While methods for comparing two networks exist, this is not the case for comparing multiple networks, although this is a common aim of many studies. Moreover, much of the power of network analyses is lost when restricting a comparison to solely determining presence/absence of nodes and links. Here we present a method for the  comparison of an unlimited number of networks: Co-expression Differential Network Analysis (CoDiNA) . Our method distinguishes between links that are common to all networks, links that are specific to only one of the compared networks, and links that are different in that their sign changes between networks. Importantly, we developed a statistical framework to normalize between these different categories of common or changed networklinks. Our approach also allows us to categorize nodes as dominated by common, specific, or differentiated links. We demonstrate the usage of our new method by presenting the analysis of a dataset consisting of different types of cancers. We revealed common , specific, and differentiated links between cancer types and show that each category of links is enriched for genes with distinct Gene Ontology groups. This suggests that our method can detect functional differences. CoDiNA has been implemented in an R package that is available from CRAN .
Biomedical sciences are increasingly recognising the relevance of gene co-expression-networks for analysing complex-systems, phenotypes or diseases. When the goal is investigating complex-phenotypes under varying conditions, it comes naturally to employ comparative network methods. While approaches for comparing two networks exist, this is not the case for multiple networks. Here we present a method for the systematic comparison of an unlimited number of networks: Co-expression Differential Network Analysis (CoDiNA) for detecting links and nodes that are common , specific or different to the networks. Applying CoDiNA to a neurogenesis study identified genes for neuron differentiation. Experimentally overexpressing one candidate resulted in significant disturbance in the underlying neurogenesis' gene regulatory network. We compared data from adults and children with active tuberculosis to test for signatures of HIV. We also identified common and distinct network features for particular cancer types with CoDiNA. These studies show that CoDiNA successfully detects genes associated with the diseases .
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Biological and Medical science is increasingly acknowledging the use of gene co-expression networks for the analysis of complex systems
Biomedical sciences are increasingly recognising the relevance of gene co-expression-networks for analysing complex-systems, phenotypes
R
In many studies,
When
R
not only to describe a network, but to investigate how this network is changing under different conditions, with respect to certain diseases, or between different species. While methods
investigating complex-phenotypes under varying conditions, it comes naturally to employ comparative network methods. While approaches
R
comparing multiple networks, although this is a common aim of many studies. Moreover, much of the power of network analyses is lost when restricting a comparison to solely determining presence/absence of nodes and links.
multiple networks.
A
systematic
R
. Our method distinguishes between links
for detecting links and nodes
R
to all networks, links that are specific to only one of the compared networks, and links that are different in that their sign changes between networks. Importantly, we developed a statistical framework to normalize between these different categories of common or changed networklinks. Our approach also allows us to categorize nodes as dominated by common, specific, or differentiated links. We demonstrate the usage of our new method by presenting the analysis of a dataset consisting of different types of cancers. We revealed common , specific, and differentiated links between cancer types and show that each category of links is enriched for genes with distinct Gene Ontology groups. This suggests that our method can detect functional differences. CoDiNA has been implemented in an R package that is available from CRAN
, specific or different to the networks. Applying CoDiNA to a neurogenesis study identified genes for neuron differentiation. Experimentally overexpressing one candidate resulted in significant disturbance in the underlying neurogenesis' gene regulatory network. We compared data from adults and children with active tuberculosis to test for signatures of HIV. We also identified common and distinct network features for particular cancer types with CoDiNA. These studies show that CoDiNA successfully detects genes associated with the diseases