hclust_order: Hierarchical clustering order

In romic: R for High-Dimensional Omic Data

Hierarchical clustering order

Description

Format and hierarchically cluster a data.frame. If hclust could not normally be produced (usually because no samples are in common for a feature) pad the matrix with zeros and still calculate the distance

Usage

hclust_order(
  df,
  feature_pk,
  sample_pk,
  value_var,
  cluster_dim,
  distance_measure = "dist",
  hclust_method = "ward.D2"
)

Arguments

df	data.frame to cluster
feature_pk	variable uniquely defining a row
sample_pk	variable uniquely defining a sample
value_var	An abundance value to use with hclust
cluster_dim	rows, columns, or both
distance_measure	variable to use for computing dis-similarity corr pearson correlation dist euclidean distance
hclust_method	method from stats::hclust to use for clustering

Value

a list containing a hierarchically clustered set of rows and/or columns

Examples

library(dplyr)

df <- tidyr::crossing(letters = LETTERS, numbers = 1:10) %>%
  mutate(noise = rnorm(n()))
hclust_order(df, "letters", "numbers", "noise", "rows")

romic documentation built on Sept. 21, 2023, 9:06 a.m.