unsupervised_clustering_auto_m_c-omics_array-method: Cluster a omics_array object: determine optimal fuzzification...

In fbertran/Patterns: Deciphering Biological Networks with Patterned Heterogeneous Measurements

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

Description

Based on soft clustering performed by the Mfuzz package.

Usage

## S4 method for signature '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(s)

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)
}

fbertran/Patterns documentation built on May 6, 2024, 9:23 p.m.