hclust_semisupervised: Semi-supervised hierarchical clustering

In RTNsurvival: Survival analysis using transcriptional networks inferred by the RTN package

Description

This function will run a semi-supervised hierarchical clustering, using prior knowledge to initialize clusters, and then unsupervised clustering on the unlabeled data.

Usage

hclust_semisupervised(
  data,
  groups,
  dist_method = "euclidean",
  hclust_method = "complete"
)

Arguments

data	a data.frame to be clustered by rows
groups	a list of vectors. If we unlist(groups), all elements must be present in the rownames of data. Each vector in the list will be treated as a separate group for the hierarchical clustering, and rejoined in order at the end.
dist_method	a distance computation method. Must be one of "euclidean", "maximum", "manhattan", "canberra", "binary", "minkowski", "pearson", "spearman"
hclust_method	an agglomeration method. Should be a method supported by hclust, one of: "ward.D", "ward.D2", "single", "complete", "average" (= UPGMA), "mcquitty" (= WPGMA), "median" (= WPGMC) or "centroid" (= UPGMC).

Value

hclust_semisupervised returns a list. The first element of the list is the data, reordered so that the merged hclust object will work. The second element is the result of the semi-supervised hierarchical clustering.

Examples

#--- add  unique labels to 'iris' data
rownames(iris) <- paste(iris$Species, formatC(1:nrow(iris), width=3, flag="0"), sep="_")

#--- unsupervised clustering
hc <- hclust(dist(iris[, -5]))
plot(hc, hang=-1, cex=0.5)

#--- semi-supervised clustering
hc_ssuper <- hclust_semisupervised(data = iris[, -5], 
                                   groups = split(rownames(iris), iris$Species))
plot(hc_ssuper, hang=-1, cex=0.5)

RTNsurvival documentation built on Nov. 12, 2020, 2 a.m.