Ac3net.MTC {Ac3net}

Return adjusted p-values of the adjancency matrix.

Description

Ac3net.MTC Return adjusted p-values of the adjancency matrix.

Usage

Ac3net.MTC(data, iterations=10, MTC=TRUE, MTCmethod="BH",
estmethod='pearson')

Arguments

data

Data matrix where rows are variables and columns are the samples. Rows must have the variable names

iterations

Number of iterations to get null distribuiton from multiple shuffling of the data.

MTC

If TRUE,as the default status, the the returned p-value matrix has the adjusted p-values

MTCmethod

The Multiple Testing Correction method. BH is default. See R p.adjust function for options.

estmethod

The default method is 'pearson' assuming that the data was normalized (at least with Log-2). If the data is not normalized, its recomended setting is 'spearman'.

Details

Ac3net.MTC takes a data matrix and returns the adjusted p-values matrix that correspond to the adjacency matrix of the input data. This way varios p-values can be tried to get different cutoff values without re-calculating the p-values.

Value

Ac3net.MTC returns a default adjusted p-values matrix that corresponds to the input adjacency matrix. If MTC is set to FALSE then it returns raw p-values.

Author(s)

Gokmen Altay

References

G. Altay,"Directed Conservative Causal Core Gene Networks", bioRxiv, 2018. G. Altay, F. Emmert-Streib, "Inferring the conservative causal core of gene regulatory networks", BMC Systems Biology (2010) 4:132.

See Also

Ac3net.maxmim, Ac3net.commonlinks,

Examples

#data(expdata)
#mim.pvals <- Ac3net.MTC(data = expdata, iterations=10,
#            MTC=TRUE, MTCmethod="BH", estmethod='pearson')