FeatSelection: Determine the characteristic features of a cluster or...

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

Determine the characteristic features of a cluster or selection

Description

FeatSelection determines the characteristic features of a selection of objects or of a specific cluster across the methods. Binary features are evaluated with Fisher's exact test, continuous features with the t-test.

Usage

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

Arguments

List	A list of clustering outputs. Default is NULL.
Selection	Either a character vector of objects, or a numeric cluster number. 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.
topChar	The number of top characteristics to return. Default is NULL.
sign	The significance level. Default is 0.05.
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.

Value

A list with the found (top) characteristics of the feature data.

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)

L=list(MCF7_F)
MCF7_FeatSel=FeatSelection(List=L,Selection=4,binData=list(fingerprintMat),
datanames=c("FP"),nrclusters=7,topChar=20)

## End(Not run)

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