DetermineWeight_SimClust: Determines an optimal weight for weighted clustering by...

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

Determines an optimal weight for weighted clustering by similarity weighted clustering.

Description

The function DetermineWeight_SimClust determines an optimal weight for performing weighted similarity clustering. For each given weight, each separate clustering is compared to the clustering on a weighted dissimilarity matrix and a Jaccard coefficient is calculated. The ratio of the Jaccard coefficients closest to one indicates an optimal weight.

Usage

DetermineWeight_SimClust(
  List,
  type = c("data", "dist", "clusters"),
  distmeasure = c("tanimoto", "tanimoto"),
  normalize = c(FALSE, FALSE),
  method = c(NULL, NULL),
  weight = seq(0, 1, by = 0.01),
  nrclusters = NULL,
  clust = "agnes",
  linkage = c("flexible", "flexible"),
  linkageF = "ward",
  alpha = 0.625,
  gap = FALSE,
  maxK = 15,
  names = NULL,
  StopRange = FALSE,
  plottype = "new",
  location = NULL
)

Arguments

List	A list of matrices of the same type. It is assumed the rows are corresponding with the objects.
type	indicates whether the provided matrices in "List" are either data matrices, distance matrices or clustering results obtained from the data. Type should be one of "data", "dist" or "clusters".
distmeasure	A vector of the distance measures to be used on each data matrix. Defaults to c("tanimoto","tanimoto").
normalize	Logical. Indicates whether to normalize the distance matrices. Default is c(FALSE, FALSE).
method	A method of normalization. Default is c(NULL,NULL).
weight	Optional. A list of different weight combinations for the data sets in List. Defaults to seq(0,1,by=0.01).
nrclusters	The number of clusters to cut the dendrogram in. Default is NULL.
clust	Choice of clustering function (character). Defaults to "agnes".
linkage	Choice of inter group dissimilarity for the individual clusterings. Defaults to c("flexible","flexible").
linkageF	Choice of inter group dissimilarity for the final clustering. Defaults to "ward".
alpha	The parameter alpha to be used in the "flexible" linkage of the agnes function. Defaults to 0.625.
gap	Logical. Whether or not to calculate the gap statistic. Only if type="data". Default is FALSE.
maxK	The maximal number of clusters to consider in calculating the gap statistic. Default is 15.
names	The labels to give to the elements in List. Default is NULL.
StopRange	Logical. Indicates whether the distance matrices with values not between zero and one should be standardized to have so. Default is FALSE.
plottype	Should be one of "pdf","new" or "sweave". Default is "new".
location	If plottype is "pdf", a location should be provided here. Default is NULL.

Value

The returned value is a list with three elements:

ClustSep	The result of Cluster for each single element of List
Result	A data frame with the Jaccard coefficients and their ratios for each weight
Weight	The optimal weight

.

References

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

Examples

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

MCF7_F = Cluster(fingerprintMat,type="data",distmeasure="tanimoto",normalize=FALSE,
method=NULL,clust="agnes",linkage="flexible",alpha=0.625,gap=FALSE,maxK=55,StopRange=FALSE)
MCF7_T = Cluster(targetMat,type="data",distmeasure="tanimoto",normalize=FALSE,
method=NULL,clust="agnes",linkage="flexible",alpha=0.625,gap=FALSE,maxK=55,StopRange=FALSE)

L=list(MCF7_F,MCF7_T)

MCF7_Weight=DetermineWeight_SimClust(List=L,type="clusters",weight=seq(0,1,by=0.01),
nrclusters=c(7,7),distmeasure=c("tanimoto","tanimoto"),normalize=c(FALSE,FALSE),
method=c(NULL,NULL),clust="agnes",linkage=c("flexible","flexible"),linkageF="ward",
alpha=0.625,gap=FALSE,maxK=50,names=c("FP","TP"),StopRange=FALSE,plottype="new",location=NULL)

## End(Not run)

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