clValid-class {clValid}

Class "clValid"

Description

The class "clValid" contains the clustering results and validation measures from the accompanying call to the function clValid.

Objects from the Class

Objects can be created using the function clValid.

Slots

clusterObjs:

Object of class "list". A list containing the results from the clustering methods.

measures:

Object of class "array". A 3-dimensional array which contains the validation measures for the clustering results. The first dimension indicates the validation measures, the second the number of clusters, and the third the clustering methods.

measNames:

Object of class "character". The names of the validation measures.

clMethods:

Object of class "character". A character vector giving the clustering methods.

labels:

Object of class "character". A character vector giving the item (gene) labels.

nClust:

Object of class "numeric". A numeric vector giving the numbers of clusters which were evaluated.

validation:

Object of class "character". A character vector giving the type of validation measures used, consisting of some combination of "internal", "stability", or "biological".

metric:

Object of class "character". The metric used to determine the distance matrix.

method:

Object of class "character". For hierarchical clustering, the agglomeration method used.

neighbSize:

Object of class "numeric". For internal validation, the neighborhood size used for the connectivity measure.

annotation:

Object of class "character or array or list". Either a character string naming the Bioconductor annotation package for mapping genes to GO categories, or a list with the names of the functional classes and the observations belonging to each class.

GOcategory:

Object of class "character". For biological validation, gives which GO categories to use for biological validation. Can be one of "BP", "MF", "CC", or "all"

goTermFreq:

Object of class "numeric". For the BSI, what threshold frequency of GO terms to use for functional annotation.

call:

Object of class "call". Gives the call to clValid used to create the clValid object.

Methods

clusterMethods

signature(object = "clValid"): Returns the names of the clustering methods.

clusters

signature(object = "clValid"): Returns the results from the clustering methods.

Additional arguments:

method = clMethods(object)

The clustering method(s) to extract.

measNames

signature(object = "clValid"): Returns the names of the validation measures.

measures

signature(object = "clValid"): Returns the validation measures.

Additional arguments:

measures = measNames(object)

The validation measure(s) to extract.

nClusters

signature(object = "clValid"): Returns the numbers of clusters evaluated.

optimalScores

signature(object = "clValid"): Returns the optimal value for each validation measure, along with the corresponding clustering method and number of clusters.

Additional arguments:

measures = measNames(object)

The validation measure(s) to extract.

plot

signature(x = "clValid", y = "missing"): Plots the validation measures.

Additional arguments:

measures=measNames(x)

The validation measures to plot.

legend=TRUE

If TRUE provides a legend.

legendLoc="topright"

The location of the legend.

main=NULL

Title of graph.

pch=NULL

Plotting characters to use.

type="b"

Type of plot.

ask=prod(par("mfcol")) < length(measures) && dev.interactive()

Logical. If TRUE the user is prompted before each plot.

print

signature(x = "clValid"): Print method for class clValid.

show

signature(object = "clValid"): Same as print.

summary

signature(object = "clValid"): Summary method for class clValid.

Additional arguments:

digits = max(3,getOption("digits")-3)

The number of significant digits to use.

Note

See the vignette for an illustration of the class.

Author(s)

Guy Brock, Vasyl Pihur, Susmita Datta, Somnath Datta

References

Brock, G., Pihur, V., Datta, S. and Datta, S. (2008) clValid: An R Package for Cluster Validation Journal of Statistical Software 25(4) https://www.jstatsoft.org/v25/i04/

Datta, S. and Datta, S. (2003) Comparisons and validation of statistical clustering techniques for microarray gene expression data. Bioinformatics 19(4): 459-466.

Datta, S. and Datta, S. (2006) Methods for evaluating clustering algorithms for gene expression data using a reference set of functional classes. BMC Bioinformatics 7:397. https://bmcbioinformatics.biomedcentral.com/articles/10.1186/1471-2105-7-397/

Handl, J., Knowles, K., and Kell, D. (2005) Computational cluster validation in post-genomic data analysis. Bioinformatics 21(15): 3201-3212.

See Also

For a description of the function 'clValid' see clValid.

For help on the clustering methods see hclust and kmeans in package stats, kmeans in package stats, agnes, clara, diana, fanny, and pam in package cluster, supersom in package kohonen, Mclust in package mclust, and sota.

For additional help on the validation measures see connectivity, dunn, stability, BHI, and BSI.

Examples

## to delete
library(clValid)


data(mouse)

## internal validation
express <- mouse[1:25,c("M1","M2","M3","NC1","NC2","NC3")]
rownames(express) <- mouse$ID[1:25]
intern <- clValid(express, 2:6, clMethods=c("hierarchical","kmeans","pam"),
                  validation="internal")
slotNames(intern)

## view results
intern
summary(intern)
optimalScores(intern)
plot(intern)

## Extract objects from slots
measures(intern)
hierClust <- clusters(intern,"hierarchical")
plot(hierClust)
measNames(intern)
nClusters(intern)