cut_trees: Module detection for the results from a nb_mcupgma call
In netboost: Network Analysis Supported by Boosting

Description Usage Arguments Value Examples

Module detection for the results from a nb_mcupgma call

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cut_trees(trees, datan, forest, min_cluster_size = 2L, ME_diss_thres,
  qc_plot = TRUE, n_pc = 1, robust_PCs = FALSE,
  nb_min_varExpl = 0.5, method = c("pearson", "kendall", "spearman"))

`trees`	List of trees, where one tree is a list of ids and rows
`datan`	Data frame were rows correspond to samples and columns to features.
`forest`	Raw dendrogram-matrix as generated by the nb_mcupgma function.
`min_cluster_size`	Integer. The minimum number of features in one module.
`ME_diss_thres`	Numeric. Module Eigengene Dissimilarity Threshold for merging close modules.
`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.

List

data('tcga_aml_meth_rna_chr18',  package='netboost')
 cores <- as.integer(getOption('mc.cores', 2))
 datan <- as.data.frame(scale(tcga_aml_meth_rna_chr18, center=TRUE,
 scale=TRUE))
 filter <- nb_filter(datan=datan, stepno=20L, until=0L, progress=1000L,
 cores=cores,mode=2L)
 dist <- nb_dist(datan=datan, filter=filter, soft_power=3L, cores=cores)
 max_singleton = dim(tcga_aml_meth_rna_chr18)[2]
 forest <- nb_mcupgma(filter=filter, dist=dist, max_singleton=max_singleton,
 cores=cores)
 trees <- tree_search(forest)
 results <- cut_trees(trees=trees,datan=datan, forest=forest,
 min_cluster_size=10L, ME_diss_thres=0.25, qc_plot=TRUE)