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In addition, to quantitatively describe the relationships between modules and environmental variables, an eigengene network analysis was performed.
Environmental eigengene networks uncovered relationships between modules (based on the MLB (a) and XKB (b) network) and environmental variables for bacterioplankton.
An eigengene analysis60 was performed for modules that were positively or negatively associated with environmental variables to quantitatively describe the relationships between modules and environmental variables.
Eigengene networks can be effective and biologically meaningful tools for studying the relationships between modules of a gene co-expression network.
Ample literature exists on how to detect biologically meaningful modules in networks but there is a need for methods that allow one to study the relationships between modules.
We use a weighted gene co-expression network to describe the relationships between modules since this maintains the continuous nature of the co-expression information.
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In summary, we find highly significant but relatively weak relationships between module membership and epigenetic variables.
Relationships between module activity scores and sample annotations were analyzed using t-tests or ANOVA.
The above results demonstrate highly significant relationships between module membership and epigenetic variables.
To verify the coexpression relationships between module member genes, we focused on one module enriched with genes associated with the flavonoid biosynthetic pathway.
Figure 3 reports significant relationships between module membership, chromatin structure and epigenetic modifications (histone modifications and DNA methylation), which are known to play a role in controlling gene expression during ES cell self-renewal and differentiation.
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