c3net (version 1.1.1)

c3net: All in one function to infer network with C3NET

Description

c3net takes a data set as input and computes the inferred network using C3NET - see details.

Usage

c3net(dataset, cop=TRUE, alpha=0.01, methodstep1="cutoff", cutoffMI= 0,
	      MTCmethod="BH", itnum=5, network=FALSE)

Arguments

dataset

Data set where rows are variables (e.g. genes) and columns are samples.

cop

The data set is copula transformed by default. If not wanted, then "cop" is set to FALSE.

alpha

Statistical significance threshold

itnum

Number of iterations to resample data to get sampling distribution.

methodstep1

If it is "cutoff" then the input cutoffMI is used as threshold for elimination in step 1. If it is "MTC" then MTCmethod input is used as the specific multiple testing correction method to employ. If it is "justp" then only significance threshold alpha is used to compute significance threshold MI.

MTCmethod

Available option are "holm", "hochberg", "hommel", "bonferroni", "BH", "BY".

cutoffMI

If methodstep1 is set to "cutoff" then this value is used for MI threshold. If this is set to 0 (or by default) it uses mean MI for threshold.

network

If TRUE, the network is plotted in the end.

Value

c3net returns a symmetric mutual information matrix, which is obtained after implementing C3NET. Specifically, non-zero elements in the returned matrix represents undirected link between variables. The inferred network may also be plotted if the argument network is set TRUE.

Details

For Step 1 of C3NET, there are three options that can be used. If methodstep1="cutoff" then cutoffMI input is taken as the significance threshold for step 1. In case cutoffMI is set to 0 then by default mean MI is taken as cutoffMI. If methodstep1 is set to "MTC" then the method from input MTCmethod, itnum for iteration number to get null distribution and alpha for statistical significance is taken for computations. If methodstep1 is set to "justp" or anything else then just alpha and itnum is used for computations.

References

G. Altay, F. Emmert-Streib, "Inferring the conservative causal core of gene regulatory networks", BMC Systems Biology (2010) 4:132.

See Also

makemim, copula, c3, sigtestp, sigtestMTC

Examples

data(expdata)
data(truenet)

net <- c3net(expdata, network=TRUE)
scores <- checknet(net,truenet)