ClusterTest-class {ClassDiscovery} | R Documentation |

This is a base class for tests that attempt to determine whether the groups found by an unsupervised clustering method are statistically significant.

## S4 method for signature 'ClusterTest': image(x, dendrogram, ...)

`x` |
An object of the `ClusterTest` class. |

`dendrogram` |
An object with S3 class `hclust` , as returned
by the `hclust` function. |

`...` |
Additional graphical parameters to be passed to the
standard `image` function. |

Objects can be created by calls of the form `new("ClusterTest", ...)`

.
Most users, however, will only create objects from one of the derived
classes such as `BootstrapClusterTest`

or
`PerturbationClusterTest`

.

`call`

:- An object of class
`call`

, which shows how the object was constructed. `result`

:- A symmetric
`matrix`

containing the results of the cluster reproducibility test. The size of the matrix corresponds to the number of samples (columns) in the data set on which the test was performed. The`result`

matrix should contain "agreement" values between 0 and 1, representing for each pair of samples the fraction of times that they were collected into the same cluster.

- hist
`signature(x = "ClusterTest")`

: Produces a histogram of the agreement fractions. When a true group structure exists, one expects a multimodal distribution,with one group of agreements near 0 (for pairs belonging to different clusters) and one group of agreements near 1 (for pairs belonging to the same cluster).- image
`signature(x = "ClusterTest")`

: Uses the`heatmap`

function to display the agreement matrix. The optional`dendrogram`

argument should be used to display the extent to which the ageement matrix matches the results of hierarchical clustering using the full data set.

`signature(object = "ClusterTest")`

: Write out a
summary of the object.Kevin R. Coombes <kcoombes@mdanderson.org>

Kerr MK, Churchill GJ. Boostrapping cluster analysis: Assessing the reliability of conclusions from microarray experiments. PNAS 2001; 98:8961-8965.

`BootstrapClusterTest`

,
`PerturbationClusterTest`

, `heatmap`

# 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 the data hc <- hclust(distanceMatrix(dd, 'pearson'), method='average') # make a fake reproducibility matrix fraud <- function(x) { new('ClusterTest', result=abs(cor(x)), call=match.call()) } fake <- fraud(dd) summary(fake) hist(fake) image(fake) # let heatmap compute a new dendrogram from the agreements image(fake, dendrogram=hc) # use the actual dendrogram from the data image(fake, dendrogram=hc, col=blueyellow(64)) # change the colors #cleanup rm(fake, fraud, hc, dd, d1, d2)

[Package *ClassDiscovery* version 2.5.0 Index]