Cluster a omics_array object: determine optimal fuzzification parameter and number of clusters. — unsupervised_clustering_auto_m_c,omics_array-method • Patterns

Based on soft clustering performed by the Mfuzz package.

# S4 method for 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)
}