Ac3net.cutoff {Ac3net}

Calculates a quick cutoff value from an adjacency matrix

Description

Ac3net.cutoff A quick way to get a very rough estimate of the cutoff value for the significants of the input adjacency matrix

Usage

Ac3net.cutoff(mim, ratio_ = 0.002, PCmincutoff=0.6, PCmaxcutoff=0.96)

Arguments

mim

An adjacency matrix, where the element at row i and column j corresponds to the correlation or mutual information between variables row i and column j. Row and columns of the matrix must have the variable names.

ratio_

The ratio of all the elements in the input adjacency matrix (even if the matrix is symmetric), which the user considers as significant portion.

PCmincutoff

The minimum absolute Pearson correlation value ( by default but can be different) where the below of is not considered as significant.

PCmaxcutoff

The maximum absolute Pearson correlation value ( by default but can be different) where all the above of is considered as significant.

Details

Ac3net.cutoff takes an adjacency matrix, ratio and minimum absolute Pearson correlation information. Then return a cutoff value that either correspond to the input ratio or the minimum.

Value

Ac3net.cutoff returns a scaler as a very rough estimate of the cutoff value for the significants of the input adjacency matrix.

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 <- cor(t(expdata), method = 'pearson')
 cutoff <- Ac3net.cutoff(mim=mim, ratio_ = 0.002,
              PCmincutoff=0.6, PCmaxcutoff=0.96)