kmeansClust: K-Means clustering across sections

In liuwd15/tomoda: Tomo-seq data analysis

K-Means clustering across sections

Description

Performs K-Means clustering across sections in a SummarizedExperiment object.

Usage

kmeansClust(object, centers, matrix = "normalized", ...)

Arguments

object	A SummarizedExperiment object.
centers	Integer, number of clusters, namely k.
matrix	Character, must be one of "count", "normalized", or "scaled".
...	other parameters passed to kmeans.

Value

A SummarizedExperiment object. The obtained cluster labels are saved in slot meta.

See Also

kmeans for performing K-Means clustering on a matrix.

Examples

data(zh.data)
zh <- createTomo(zh.data)
zh <- kmeansClust(zh, 3)

# Use scaled read counts to calculate distance
zh <- scaleTomo(zh)
zh <- kmeansClust(zh, 3, matrix="scaled")

liuwd15/tomoda documentation built on March 29, 2022, 1:09 a.m.