hierarchClust {tomoda} | R Documentation

## Hierarchical clustering across sections

### 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")

[Package tomoda version 1.0.0 Index]