network.stability {bootcluster}

Estimate of detect module stability

Description

Estimate of detect module stability

Usage

network.stability(
  data.input,
  threshold,
  B = 20,
  cor.method,
  large.size,
  PermuNo,
  scheme_2 = FALSE
)

Arguments

data.input

a data.frame of the data set where the rows are observations and columns are covariates

threshold

a numeric number of threshold for correlation matrix

B

number of bootstrap re-samplings

cor.method

the correlation method applied to the data set,three method are available: "pearson", "kendall", "spearman".

large.size

the smallest set of modules, the large.size=0 is recommended to use right now.

PermuNo

number of random graphs for null

scheme_2

logical TRUE if scheme 2 is used, FASLE if scheme 1 is used. Right now, only FASLE is recommended.

Details

This function estimates the modules' stability through bootstrapping approach for the given threshold. The approach to stability estimation is to compare the module composition of the reference correlation graph to the various bootstrapped correlation graphs, and to assess the stability at the (1) node-level, (2) module-level, and (3) overall.

Value

stabilityresult

a list of result for nodes-wise stability

modularityresult

list of modularity information with the given threshold

jaccardresult

list estimated unconditional observed stability and the estimates of expected stability under the null

originalinformation

list information for original data, igraph object and adjacency matrix constructed with the given threshold

Author(s)

Mingmei Tian

References

A framework for stability-based module detection in correlation graphs. Mingmei Tian,Rachael Hageman Blair,Lina Mu, Matthew Bonner, Richard Browne and Han Yu.

Examples

set.seed(1)
data(wine)
x0 <- wine[1:50,]

mytest<-network.stability(data.input=x0,threshold=0.7, B=20, 
cor.method='pearson',large.size=0,
PermuNo = 10,
scheme_2 = FALSE)