hierarchClust: Hierarchical clustering across sections

In tomoda: Tomo-seq data analysis

Description

Performs hierarchical clustering across sections in a SummarizedExperiment object.

Usage

hierarchClust(
  object,
  matrix = "normalized",
  measure = "euclidean",
  p = 2,
  agglomeration = "complete"
)

Arguments

object	A SummarizedExperiment object.
matrix	Character, must be one of "count", "normalized", or "scaled".
measure	Character, must be one of "euclidean", "maximum", "manhattan", "canberra", "binary" or "minkowski".
p	Numeric, the power of the Minkowski distance.
agglomeration	Character, must be one of "ward.D", "ward.D2", "single", "complete", "average", "mcquitty", "median" or "centroid".

Value

A hclust object.

See Also

dist for measuring distance and hclust for performing hierarchical clustering on a matrix.

Examples

data(zh.data)
zh <- createTomo(zh.data)
hclust_zh <- hierarchClust(zh)
plot(hclust_zh)

# Use other agglomeration method
hclust_zh <- hierarchClust(zh, agglomeration="average")

# (Not recommended) Use scaled read counts to calculate distance
zh <- scaleTomo(zh)
hclust_zh <- hierarchClust(zh, matrix="scaled")

tomoda documentation built on Nov. 8, 2020, 8:10 p.m.