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perseus:user:activities:matrixanalysis:clustering_pca:co-expression_clustering

Co-expression clustering

This analysis is provided through the R-language integration into Perseus and therefore requires R as well as the WGCNA package to be installed. Visit WGCNA page for more information and installation instructions.

Co-expression clustering is covered by the official WGCNA tutorials.

Description

The co-expression network is created using the defined correlation function. The determined power is applied to the network (see Soft-threshold for more info). Topological overlap distance is used to create the hierarchical clustering dendrogram. The co-expression modules are determined using the dynamic tree-cut method. For each module, a module eigengene is reported, with its name corresponding to the color of the cluster.

Output

  • Hierarchical clustering heatmap with a dendrogram and automatic cluster assignments.
  • Matrix of module eigengenes that represent a cluster. See Correlate for identifying clusters that correlate with clinical/phenotype data.
perseus/user/activities/matrixanalysis/clustering_pca/co-expression_clustering.txt · Last modified: 2018/07/17 18:51 by rudolph