CharacteristicFeatures: Determining the characteristic features of a cluster

In IntClust: Integration of Multiple Data Sets with Clustering Techniques

Description

The function CharacteristicFeatures requires as input a list of one or multiple clustering results. It is capable of selecting the binary features which determine a cluster with the help of the fisher's exact test.

Usage

CharacteristicFeatures(List, Selection = NULL, binData = NULL,
  contData = NULL, datanames = NULL, nrclusters = NULL, sign = 0.05,
  topChar = NULL, fusionsLog = TRUE, weightclust = TRUE, names = NULL)

Arguments

List	A list of the clustering outputs to be compared. The first element of the list will be used as the reference in ReorderToReference.
Selection	If differential gene expression should be investigated for a specific selection of objects, this selection can be provided here. Selection can be of the type "character" (names of the objects) or "numeric" (the number of specific cluster). Default is NULL.
binData	A list of the binary feature data matrices. These will be evaluated with the fisher's extact test. Default is NULL.
contData	A list of continuous data sets of the objects. These will be evaluated with the t-test. Default is NULL.
datanames	A vector with the names of the data matrices. Default is NULL.
nrclusters	Optional. The number of clusters to cut the dendrogram in. The number of clusters should not be specified if the interest lies only in a specific selection of objects which is known by name. Otherwise, it is required. Default is NULL.
sign	The significance level to be handled. Default is 0.05.
topChar	Overrules sign. The number of features to display for each cluster. If not specified, only the significant genes are shown. Default is NULL.
fusionsLog	Logical. To be handed to ReorderToReference: indicator for the fusion of clusters. Default is TRUE
weightclust	Logical. To be handed to ReorderToReference: to be used for the outputs of CEC, WeightedClust or WeightedSimClust. If TRUE, only the result of the Clust element is considered. Default is TRUE.
names	Optional. Names of the methods. Default is NULL.

Details

The function rearranges the clusters of the methods to a reference method such that a comparison is made easier. Given a list of methods, it calls upon ReorderToReference to rearrange the number of clusters according to the first element of the list which will be used as the reference.

Value

The returned value is a list with an element per method. Each element contains a list per cluster with the following elements:

objects	A list with the elements LeadCpds (the objects of interest) and OrderedCpds (all objects in the order of the clustering result)
Characteristics	A list with an element per defined binary data matrix in BinData and continuous data in ContData. Each element is again a list with the elements TopFeat (a table with information on the top features) and AllFeat (a table with information on all features)

Examples

## Not run: 
data(fingerprintMat)
data(targetMat)
data(geneMat)

MCF7_F = Cluster(fingerprintMat,type="data",distmeasure="tanimoto",normalize=FALSE,
method=NULL,clust="agnes",linkage="flexible",gap=FALSE,maxK=55,StopRange=FALSE)
MCF7_T = Cluster(targetMat,type="data",distmeasure="tanimoto",normalize=FALSE,
method=NULL,clust="agnes",linkage="flexible",gap=FALSE,maxK=55,StopRange=FALSE)

L=list(MCF7_T ,MCF7_F)

MCF7_Char=CharacteristicFeatures(List=L,Selection=NULL,BinData=list(fingerprintMat,
targetMat),datanames=c("FP","TP"),nrclusters=7,topC=NULL,sign=0.05,fusionsLog=TRUE,
weightclust=TRUE,names=c("FP","TP"))

## End(Not run)

IntClust documentation built on May 2, 2019, 5:51 a.m.