Description Usage Arguments Details Value Author(s) See Also Examples
View source: R/functions_comp2annot.R
This function allows to cluster samples according to the results of an ICA decomposition. Several clustering functions and several levels of data for clustering can be performed by the function.
1 2 3 4 5  clusterSamplesByComp_multiple(icaSet, params,
funClus = c("Mclust", "kmeans", "pam", "pamk", "hclust", "agnes"),
filename, clusterOn = c("A", "S"),
level = c("genes", "features"), nbClus,
metric = "euclidean", method = "ward", ...)

icaSet 
An 
params 
A 
funClus 
The function to be used for clustering,
must be several of

filename 
A file name to write the results of the clustering in 
clusterOn 
Specifies the matrix used to apply clustering, can be several of:

level 
The level of projections to be used when

nbClus 
The number of clusters to be computed,
either a single number or a numeric vector whose length
equals the number of components. If missing (only allowed
if 
metric 
Metric used in 
method 
Method of hierarchical clustering, used in

... 
Additional parameters required by the
clustering function 
One clustering is run independently for each component.
A list consisting of three elements
a data.frame specifying the sample clustering for each component using the different ways of clustering,
the complete output of the clustering function(s),
the adjusted Rand indices, used to compare the clusterings obtained for a same component.
Anne
Mclust
, adjustedRandIndex
, kmeans
,
pam
, pamk
, hclust
, agnes
,
cutree
1 2 3 4 5 6 7 8  data(icaSetCarbayo)
params < buildMineICAParams(resPath="carbayo/", selCutoff=3)
## compare kmeans clustering applied to A and data restricted to the contributing genes
## on components 1 to 3
res < clusterSamplesByComp_multiple(icaSet=icaSetCarbayo[,,1:3], params=params, funClus="kmeans",
nbClus=2, clusterOn=c("A","S"), level="features")
head(res$clus)

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