CharacteristicFeatures: Determine the characteristic features of clusters

In MosaiClusteR: An Umbrella Framework for Multi-Source and Multi-Omics Clustering

Determine the characteristic features of clusters

Description

CharacteristicFeatures identifies the features that are characteristic for the objects of each cluster of one or more clustering results. Binary features are evaluated with Fisher's exact test, continuous features with the t-test. Multiplicity correction is included.

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 clustering outputs to be compared. The first element of the list is used as the reference in ReorderToReference.
Selection	If a selection of objects is provided, the function only investigates the features of this selection. Default is NULL.
binData	A list of the binary feature data matrices. Default is NULL.
contData	A list of continuous feature data matrices. Default is NULL.
datanames	A vector with the names of the data matrices. Default is NULL.
nrclusters	The number of clusters to cut the dendrogram in. Default is NULL.
sign	The significance level. Default is 0.05.
topChar	The number of top characteristics to return. If NULL, only the significant characteristics are saved. Default is NULL.
fusionsLog	Logical. Indicator for the fusion of clusters. Default is TRUE.
weightclust	Logical. 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.

Value

A list with for each method the found (top) characteristics of the feature data per cluster.

References

Perualila-Tan N. et al. (2016). Weighted similarity-based clustering of chemical structures and bioactivity data in early drug discovery.

Examples

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

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_F,MCF7_T)
MCF7_Char=CharacteristicFeatures(List=L,Selection=NULL,binData=list(fingerprintMat),
contData=NULL,datanames=c("FP"),nrclusters=7,topChar=20)

## End(Not run)

MosaiClusteR documentation built on Oct. 25, 2026, 1:08 a.m.