clusterSamplesByComp {MineICA} | R Documentation |
This function allows to cluster samples according to the results of an ICA decomposition. One clustering is run independently for each component.
clusterSamplesByComp(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 one of
|
filename |
A file name to write the results of the clustering in |
clusterOn |
Specifies the matrix used to apply clustering:
|
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 |
A list consisting of three elements
a list specifying the sample clustering for each component,
the complete output of the clustering function,
the function used to perform the clustering.
. When clusterOn="S"
, if some
components were not used because no contributing elements
is selected using the cutoff, the icaSet with the
corresponding component deleted is also returned.
Anne Biton
Mclust
, kmeans
, pam
, pamk
,
hclust
, agnes
, cutree
data(icaSetCarbayo) params <- buildMineICAParams(resPath="carbayo/", selCutoff=4) ## cluster samples according to their contributions # using Mclust without a number of clusters res <- clusterSamplesByComp(icaSet=icaSetCarbayo, params=params, funClus="Mclust", clusterOn="A", filename="clusA") # using kmeans res <- clusterSamplesByComp(icaSet=icaSetCarbayo, params=params, funClus="kmeans", clusterOn="A", nbClus=2, filename="clusA")