In order to perform aggregated data clustering, the ADClust
function was written. The data matrices are aggregated into one
and hierarchical clustering is performed.
A list of data matrices of the same type. It is assumed the
rows are corresponding with the objects.
distmeasure
Choice of metric for the dissimilarity matrix (character).
Should be one of "tanimoto", "euclidean", "jaccard", "hamming".
normalize
Logical. Indicates whether to normalize the distance matrices or not.
This is recommended if different distance types are used. More details
on normalization in Normalization.
method
A method of normalization. Should be one of "Quantile","Fisher-Yates",
"standardize","Range" or any of the first letters of these names.
clust
Choice of clustering function (character). Defaults to "agnes".
linkage
Choice of inter group dissimilarity (character). Defaults to "ward".
alpha
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"
Value
The returned value is a list with the following three elements.
AllData
Fused data matrix of the data matrices
DistM
The distance matrix computed from the AllData element
Clust
The resulting clustering
The value has class 'ADC'. The Clust element will be of interest for
further applications.
Details
In order to perform aggregated data clustering, the ADC
function was written. A list of data matrices of the same type
(continuous or binary) is required as input which are combined
into a single (larger) matrix. Hierarchical clustering is performed
with the agnes function and the ward link on the resulting
data matrix and an applicable distance measure is indicated by the
user.
References
FODEH, J. S., BRANDT, C., LUONG, B. T., HADDAD, A., SCHULTZ, M., MURPHY, T.,
KRAUTHAMMER, M. (2013). Complementary Ensemble Clustering of Biomedical Data.
J Biomed Inform. 46(3) pp.436-443.
# NOT RUN {data(fingerprintMat)
data(targetMat)
L=list(fingerprintMat,targetMat)
MCF7_ADC=ADC(L,distmeasure="tanimoto",normalize=FALSE,method=NULL,clust="agnes",
linkage="ward",alpha=0.625)
# }