Defines functions makeHCdendrogram

Documented in makeHCdendrogram

#' Plot HC dendrogram
#' Plot HC dendrogram given a data frame.
#' @param df A data frame on which PCA plot is based.
#' @param sample_to_color_map A data frame mapping sample names to color. Make sure the order of row of 'sample_to_color_map' is the same as order of columns in 'df'. Make sure there is column named 'color'.
#' @param method_distance A character of method for calculating distances between samples. Defaults to "euclidean". Can also be one of:"maximum", "manhattan", "canberra", "binary", "minkowski", "pearson", "kendall", "spearman". The last free are derived from fdistColPearson() function. 
#' @param method_agglomeration A character of sample clustering method. Defaults to "complete" (i.e. complete linkage algorithm). Can also be one of: "ward.D", "ward.D2", "single","average","mcquitty","median","centroid". See ?hclust for more info.
#' @return A HC dendrogram
#' @author Alexander Kheirallah ([email protected])
#' @export

makeHCdendrogram <- function(df, sample_to_color_map, method_distance="euclidean", method_agglomeration="complete", main="hierarchical clustering dendrogram"){
  ## checks
  if(!("dendextend" %in% rownames(installed.packages()))){ # testing if required packaged installed
    stop("dendextend package is not installed. Please install it. Exiting!")
    stop("sample_to_color_map does not have two columns. Ensure it has two columns. Exiting")
  if(!("color" %in% colnames(sample_to_color_map))) {
    stop("There is no column named color. Ensure there is.Exiting")
    distObj <- f.distCol.Pearson.2(x = df, method = method_distance)
    distObj <- dist(t(df), method = method_distance)
  hc <- hclust(d=distObj, method = method_agglomeration)
  dendo <- as.dendrogram(hc)
  sample_to_color_map <- sample_to_color_map[labels(dendo),]
  colVec <- as.character(sample_to_color_map$color)
  dendo %>% set("labels_col", as.character(colVec)) %>%
msxakk89/dat documentation built on April 3, 2020, 5:39 p.m.