cut_dendro: Module detection for an individual tree
In netboost: Network Analysis Supported by Boosting

Module detection for an individual tree

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cut_dendro(tree_dendro, min_cluster_size = 2L, datan, ME_diss_thres,
  name_of_tree = "", qc_plot = TRUE, n_pc = 1, robust_PCs = FALSE,
  nb_min_varExpl = 0.5, method = c("pearson", "kendall", "spearman"))

`tree_dendro`	List of tree specific objects including dendrogram, tree data and features originating from the tree_dendro function.
`min_cluster_size`	Integer. The minimum number of features in one module.
`datan`	Data frame were rows correspond to samples and columns to features.
`ME_diss_thres`	Numeric. Module Eigengene Dissimilarity Threshold for merging close modules.
`name_of_tree`	String. Annotating plots and messages.
`qc_plot`	Logical. Should plots be created?
`n_pc`	Number of principal components and variance explained entries to be calculated. The number of returned variance explained entries is currently ‘min(n_pc,10)’. If given ‘n_pc’ is greater than 10, a warning is issued.
`robust_PCs`	Should PCA be calculated on ranked data (Spearman PCA)? Rotations will not correspond to original data if this is applied.
`nb_min_varExpl`	Minimum proportion of variance explained for returned module eigengenes. The number of PCs is capped at n_pc.
`method`	A character string specifying the method to be used for correlation coefficients.