clusterSamplesByComp_multiple: Cluster samples from an IcaSet

Description Usage Arguments Details Value Author(s) See Also Examples

View source: R/functions_comp2annot.R

Description

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.

Usage

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  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", ...)

Arguments

icaSet

An IcaSet object

params

A MineICAParams object

funClus

The function to be used for clustering, must be several of c("Mclust","kmeans","pam","pamk","hclust","agnes")

filename

A file name to write the results of the clustering in

clusterOn

Specifies the matrix used to apply clustering, can be several of:

"A":

the clustering is performed in one dimension, on the vector of sample contributions,

"S":

the clustering is performed on the original data restricted to the contributing individuals.

level

The level of projections to be used when clusterOn="S", either "features" or "genes".

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 funClus is one of c("Mclust","pamk"))

metric

Metric used in pam and hclust, default is "euclidean"

method

Method of hierarchical clustering, used in hclust and agnes

...

Additional parameters required by the clustering function funClus.

Details

One clustering is run independently for each component.

Value

A list consisting of three elements

clus:

a data.frame specifying the sample clustering for each component using the different ways of clustering,

resClus:

the complete output of the clustering function(s),

comparClus:

the adjusted Rand indices, used to compare the clusterings obtained for a same component.

Author(s)

Anne

See Also

Mclust, adjustedRandIndex, kmeans, pam, pamk, hclust, agnes, cutree

Examples

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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)

Bioconductor-mirror/MineICA documentation built on May 29, 2017, 8:30 a.m.