Cluster a omics_array object: determine optimal fuzzification parameter and number of clusters.

Based on soft clustering performed by the Mfuzz package.

Usage

# S4 method for class 'omics_array'
unsupervised_clustering_auto_m_c(
  M1,
  clust = NULL,
  mestim = NULL,
  M2 = NULL,
  data_log = TRUE,
  screen = NULL,
  crange = NULL,
  repeats = NULL,
  cselect = TRUE,
  dminimum = FALSE
)

Arguments

M1

Object of omics_array class.

clust

[NULL] Number of clusters.

mestim

[NULL] Fuzzification parameter.

M2

[NULL] Object of omics_array class,

data_log

[TRUE] Should data be logged?

screen

[NULL] Specify `screen` parameter.

crange

[NULL] Specify `crange` parameter.

repeats

[NULL] Specify `repeats` parameter.

cselect

[TRUE] Estimate `cselect` parameter.

dminimum

[FALSE] Estimate `dminimum` parameter.

Value

m

Estimate of the optimal fuzzification parameter.

c

Estimate of the optimal number of clusters.

csearch

More result from the cselection function of the Mfuzz package

Author

Bertrand Frederic, Myriam Maumy-Bertrand.

Examples

if(require(CascadeData)){
data(micro_S, package="CascadeData")
M<-as.omics_array(micro_S[1:100,],1:4,6)
mc<-unsupervised_clustering_auto_m_c(M)
}