Sentence examples for clusters of modules from inspiring English sources

The common biological themes associated with the gene sets in each of the resulting seven clusters are listed on the left in Figure 7. Based on the complete table of enrichment p-values (Supplementary Figure S4), it is evident that there are clusters of modules that also show a similar enrichment pattern across the gene sets.

Therefore, we obtained the union of the four transition-wise subnetworks and dissected them into clusters of modules by clustering the vectors of dC values associated with each protein interaction pair (Euclidean distance measure; complete linkage clustering).

Further, the measured cohesion value is used to perform clustering of modules in order to increase cohesion and decrease coupling among modules simultaneously.

Module preservation during iterative re-clustering of modules.

The network summarizes and facilitates the interpretation of the biological processes significantly enriched in the initial list of genes, revealing important information such as: distance and overlap between clusters, identification of modules and hubs.

Instead, we use the following dissimilarity D i s s i m I J (A E i g e n ) = 1 − cor (E I, E J ) 2. Using this dissimilarity as input to average linkage hierarchical clustering leads to a cluster tree of modules (represented by eigengenes).

We discovered 8 NLR genes in the kinase sub-cluster of Module 1.

In AA subjects, clustering of module-trait correlations indicated the strongest overlap between SI- and AIRg-associated modules.

Furthermore, 29 out of 34 genes in the cluster overlapped with the cluster of module 2. And interestingly, all 9 TFs predicted for the cluster were exactly the same as those in module 2. This analysis demonstrates that our method can produce rather consistent results with a reasonable range of thresholds of selecting differentially expressed genes.

At young age, lipid metabolism module was prominent, whereas the functional clusters of other modules were not immediately apparent by the pathway analysis (results not shown).

We show how using self-organizing maps we can grow clusters in a dynamic fashion and thus explicitly capture relationships between the software measures and quantify these dependencies for larger and less homogeneous clusters of software modules.