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The condition clusters of a given module are first linked together in a hierarchical decision tree.
In some instances, the regulators of a given module will make it into the module itself based on the PCIT networking strategy.
In some cases the master regulators of a given module were components of the relevant module, based purely on the PCIT driven network analysis.
The module eigengene, the first principal component of the matrix of expression values of a given module, was adopted to characterize the gene expression profile of the module [23].
The statistical significance for enrichment of a given module in ontologies and pathways was determined by a chi-square test.
The highly correlated module genes of a given module are represented and summarized by their first principal component (which is referred to as the module eigengene).
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The value of M for a given module is inversely related to the average value of min(P u, P d ) among the G genes included in a module.
The list of regulators for a given module is ordered according to their individual score.
The spot intensity is determined by the proportion of transcripts reaching significance for a given module.
The module membership (also known as module eigengene based connectivity, kME) of each genes was calculated by correlating the gene expression profile with module eigengenes, and represents the extent of a gene close to a given module.
Cluster frequency indicates, for a given module, the number of genes annotated over the number of all included genes.
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