Genes and proteins are organized into functional modular networks in which the network context of a gene or protein has implications for cellular function. Highly connected hub proteins, largely responsible for maintaining network connectivity, have been found to be much more likely to be essential for survival.
Here we investigate the relationship between connectivity and essentiality as well as between connectivity and gene sequence conservation in multiple independent data sets. We explore the modular structure of weighted co-expression networks in yeast and show that fundamental modules are preserved across multiple data sets. We also demonstrate how the reliability of a predicted modules construction can be tested by observing whether the local network properties retain the predictive power for determining the relative importance of a gene.
Application of these techniques allows a finer scale prediction of relative gene importance for a particular process within a group of similarly expressed genes.
The weighted gene co-expression network analysis method is described in Theory Paper 1: Zhang and Horvath (2005)
For a more mathematical description of weighted gene co-expression networks consider Theory Papers: Dong and Horvath (2007, 2008)
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