The WeightedSimClust function performs weighted
similarity clustering. The input can be data matrices of which
the distance matrices are computed or clustering results where from
the distance matrices are extracted. An optimal weight is chosen with the
DetermineWeight_SimClust function or can be specified by the user. With the found
weight the distance matrices are linearly combined and hierarchical clustering
is performed.
WeightedSimClust(List, type = c("data", "dist","clusters"), weight = seq(0, 1, 0.01),
clust = "agnes",linkage=c("ward","flexible"),alpha=0.625, distmeasure = c("euclidean",
"tanimoto"),normalize=FALSE,method=NULL, gap = FALSE, maxK = 50, nrclusters = NULL,
names = c("B", "FP"), AllClusters = FALSE,StopRange=FALSE,plottype="new",
location=NULL)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. If type="dist" the calculation of the distance matrices is skipped and if type="clusters" the single source clustering is skipped. Type should be one of "data", "dist" or"clusters".
One specific weight to perform clustering on or a list with different weight
combinations. If different weight combinations are provided, the function
Chooseweight is called and an optimal combination is chosen.
Choice of clustering function (character). Defaults to "agnes".
A vector with the choice of inter group dissimilarity (character) for each data set.
The parameter alpha to be used in the "flexible" linkage of the agnes function. Defaults to 0.625 and is only used if the linkage is set to "flexible"
A character vector with the distance measure for each data matrix. Should be of "tanimoto", "euclidean", "jaccard", "hamming".
Logical. Indicates whether to normalize the distance matrices or not.
This is recommended if different distance types are used. More details
on standardization in Normalization.
A method of normalization. Should be one of "Quantile","Fisher-Yates", "standardize","Range" or any of the first letters of these names.
Logical. Whether or not to calculate the gap statistic in the clustering on each data matrix separately. Only if type="data".
The maximal number of clusters to consider in calculating the gap statistic. Only if type="data".
The number of clusters to cut the dendrogram in.
The labels to give to the elements in List.
Logical. Whether clustering should be performed for every weight.
Logical. Indicates whether the distance matrices with values not between zero and one should be standardized to have so.
If FALSE the range normalization is performed. See Normalization. If TRUE, the distance matrices are not changed.
This is recommended if different types of data are used such that these are comparable.
Should be one of "pdf","new" or "sweave". If "pdf", a location should be provided in "location" and the figure is saved there. If "new" a new graphic device is opened and if "sweave", the figure is made compatible to appear in a sweave or knitr document.
If plottype is "pdf", a location should be provided in "location" and the figure is saved there.
The returned value is a list with four elements:
The distance matrix of the first data object
The distance matrix of the second data object
The optimal weight
The weighted distance matrices for the optimal weight
The resulting clustering
The weight combinations should be provided as elements in a list. For three data matrices an example could be: weights=list(c(0.5,0.2,0.3),c(0.1,0.5,0.4)). To provide a fixed weight for some data sets and let it vary randomly for others, the element "x" indicates a free parameter. An example is weights=list(c(0.7,"x","x")). The weight 0.7 is now fixed for the first data matrix while the remaining 0.3 weight will be divided over the other two data sets. This implies that every combination of the sequence from 0 to 0.3 with steps of 0.1 will be reported and clustering will be performed for each.
RAVINDRANATH, A. C.,PERUALILA-TAN, N., KASIM, A.,DRAKAKIS, G., LIGGI, S., BREWERTON, S. C.,MASON, D., BODKIN, M. J., EVANS, D. A., BHAGWAT, A. TALLOEN, W., GOHLMANN, H. W. H., QSTAR Consortium, SHKEDY, Z., BENDER, A. (2015). Connecting gene expression data from connectivity map and in silico target predictions for small molecule mechanism-of-action analysis. Mol. BioSyst. Available at: <http://pubs.rsc.org/En/content/ articlelanding/2015/mb/c4mb00328d#!divAbstract>
# NOT RUN {
data(fingerprintMat)
data(targetMat)
L=list(fingerprintMat,targetMat)
MCF7_WeightSim=WeightedSimClust(L,type="data", weight=seq(0,1,0.01),clust="agnes",
linkage=c("flexible","flexible"),alpha=0.625,distmeasure=c("tanimoto","tanimoto"),
normalize=FALSE,method=NULL,gap=FALSE,maxK=50,nrclusters=7,names=c("FP","B"),
AllClusters=FALSE,StopRange=FALSE,plottype="new",location=FALSE)
# }
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