cd02-1-cluster3: Cluster a Dataset Three Ways

In ClassDiscovery: Classes and Methods for "Class Discovery" with Microarrays or Proteomics

Cluster a Dataset Three Ways

Description

Produces and plots dendrograms using three similarity measures: Euclidean distance, Pearson correlation, and Manhattan distance on dichotomized data.

Usage

cluster3(data, eps=logb(1, 2), name="", labels=dimnames(data)[[2]])

Arguments

data	A matrix, numeric data.frame, or ExpressionSet object.
eps	A numerical value; the threshold at which to dichotomize the data
name	A character string to label the plots
labels	A vector of character strings used to label the items in the dendrograms.

Value

Invisibly returns the data object on which it was invoked.

Author(s)

Kevin R. Coombes [e-mail]

See Also

hclust

Examples

## simulate data from two different classes
d1 <- matrix(rnorm(100*30, rnorm(100, 0.5)), nrow=100, ncol=30, byrow=FALSE)
d2 <- matrix(rnorm(100*20, rnorm(100, 0.5)), nrow=100, ncol=20, byrow=FALSE)
dd <- cbind(d1, d2)
## cluster it 3 ways
par(mfrow=c(2,2))
cluster3(dd)
par(mfrow=c(1,1))

ClassDiscovery documentation built on Sept. 2, 2024, 3 a.m.