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#' @title Extract a list of \emph{k} subdendrograms from a given dendrogram
#' object
#' @export
#' @description
#' Extracts a list (\link{dendlist}) of subdendrogram structures based on the cutree \code{\link{cutree.dendrogram}} function
#' from a given dendrogram object. It can be useful in case we're interested in a visual investigation of
#' specific clustering results.
#' @param dend a dendrogram object
#' @param k the number of subdendrograms that should be extracted
#' @param order_clusters_as_data passed to \link[dendextend]{cutree}, default is FALSE
#' (while the cutree default is TRUE). The reason is since it's easier to look at the dendrogram plot
#' and then get subtrees that are in the same order is in the plot/dendrogram object.
#' This is in contrast to more traditional use of cutree, where it is used with the original order or rows from the data.
#' @param ... parameters that should be passed to the cutree
#' \code{\link{cutree.dendrogram}}
#' @return
#' A list of \emph{k} subdendrograms, based on the cutree
#' \code{\link{cutree.dendrogram}} clustering
#' clusters.
#' @examples
#'
#' # needed packages:
#' # install.packages(gplots)
#' # install.packages(viridis)
#' # install.packages(devtools)
#' # devtools::install_github('talgalili/dendextend') #' dendextend from github
#'
#' # define dendrogram object to play with:
#' dend <- iris[1:20, -5] %>%
#' dist() %>%
#' hclust() %>%
#' as.dendrogram() %>%
#' # set("labels_to_character") %>%
#' color_branches(k = 5)
#' labels(dend) <- letters[1:20]
#' plot(dend)
#' dend_list <- get_subdendrograms(dend, 5)
#' lapply(dend_list, labels)
#' # [[1]]
#' # [1] "a" "b"
#' #
#' # [[2]]
#' # [1] "c" "d" "e" "f" "g"
#' #
#' # [[3]]
#' # [1] "h" "i"
#' #
#' # [[4]]
#' # [1] "j" "k" "l" "m"
#' #
#' # [[5]]
#' # [1] "n" "o" "p" "q" "r" "s" "t"
#'
#' # define dendrogram object to play with:
#' dend <- iris[, -5] %>%
#' dist() %>%
#' hclust() %>%
#' as.dendrogram() %>%
#' set("labels_to_character") %>%
#' color_branches(k = 5)
#' dend_list <- get_subdendrograms(dend, 5)
#'
#' # Plotting the result
#' par(mfrow = c(2, 3))
#' plot(dend, main = "Original dendrogram")
#' sapply(dend_list, plot)
#'
#' # plot a heatmap of only one of the sub dendrograms
#' par(mfrow = c(1, 1))
#' library(gplots)
#' sub_dend <- dend_list[[1]] #' get the sub dendrogram
#' # make sure of the size of the dend
#' nleaves(sub_dend)
#' length(order.dendrogram(sub_dend))
#' # get the subset of the data
#' subset_iris <- as.matrix(iris[order.dendrogram(sub_dend), -5])
#' # update the dendrogram's internal order so to not cause an error in heatmap.2
#' order.dendrogram(sub_dend) <- as.integer(rank(order.dendrogram(sub_dend)))
#' heatmap.2(subset_iris, Rowv = sub_dend, trace = "none", col = viridis::viridis(100))
get_subdendrograms <- function(dend, k, order_clusters_as_data = FALSE, ...) {
clusters <- cutree(dend, k, order_clusters_as_data = order_clusters_as_data, ...)
dend_list <- lapply(unique(clusters), function(cluster.id) {
find_dendrogram(dend, names(which(clusters == cluster.id)))
})
class(dend_list) <- "dendlist"
dend_list
}
#' @title Search for the sub-dendrogram structure composed of selected labels
#' @export
#' @description
#' Given a dendrogram object, the function performs a recursive DFS algorithm to determine
#' the sub-dendrogram which is composed of (exactly) all 'selected_labels'.
#' @param dend a dendrogram object
#' @param selected_labels A character vector with the labels we expect to have
#' in the sub-dendrogram. This doesn't have to be in the same order as in the dendrogram.
#' @return
#' Either a sub-dendrogram composed of only members of selected_labels.
#' If such a sub-dendrogram doesn't exist, the function returns NULL.
#' @examples
#'
#' \dontrun{
#' # define dendrogram object to play with:
#' dend <- iris[, -5] %>%
#' dist() %>%
#' hclust() %>%
#' as.dendrogram() %>%
#' set("labels_to_character") %>%
#' color_branches(k = 5)
#' first.subdend.only <- names(cutree(dend, 4)[cutree(dend, 4) == 1])
#' sub.dend <- find_dendrogram(dend, first.subdend.only)
#' # Plotting the result
#' par(mfrow = c(1, 2))
#' plot(dend, main = "Original dendrogram")
#' plot(sub.dend, main = "First subdendrogram")
#'
#' dend <- 1:10 %>%
#' dist() %>%
#' hclust() %>%
#' as.dendrogram() %>%
#' set("labels_to_character") %>%
#' color_branches(k = 5)
#'
#' selected_labels <- as.character(1:4)
#' sub_dend <- find_dendrogram(dend, selected_labels)
#' plot(dend, main = "Original dendrogram")
#' plot(sub_dend, main = "First subdendrogram")
#'
#'
#' }
#'
find_dendrogram <- function(dend, selected_labels) {
# if the dendrogram is exactly the labels in selected_labels - then we found our dend
if (all(labels(dend) %in% selected_labels) &&
(length(labels(dend)) == length(selected_labels))) {
return(dend)
}
# if not, either we can find such a sub dendrogram, or it doesn't exist (return NULL)
for(i in 1:length(dend)) {
if(all(selected_labels %in% labels(dend[[i]]))) {
return(Recall(dend[[i]], selected_labels))
}
}
# if we couldn't find any sub-dend that includes all the labels we're looking for
# then we return NULL
return(NULL)
}
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