c3 {c3net} R Documentation

## C3NET second step: Selecting maximum valued elements

### Description

`c3` takes the mutual information matrix as input and implements the second step of C3NET - see details.

### Usage

`c3( mim, sym = TRUE )`

### Arguments

 `mim` A symmetric square mutual information matrix, where the elements (i,j) correspond to the mutual information I(i,j) or I(j,i) between variables i and j. `sym` Decides the output matrix to be symmetric or not.

### Details

The C3NET algorithm consists of two main steps. The first step is the same as for relevance networks (RELNET), where all the non-significant mutual information values in the matrix are eliminated if statistically not significant. The second step of C3NET keeps all maximum valued mutual information values for each row in the matrix and sets the rest of the elements in the matrix zero (the diagonal of the matrix is ignored). The output is normally symmetric matrix but if the argument sym is set to FALSE then the output becomes non-symmetric.

### Value

`c3` implements the second step of the C3NET algorithm returning a symmetric mutual information matrix. Specifically, the non-zero elements in the returned matrix represent undirected links between variables which are statistically significant (tested in the first step of C3NET).

### References

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

`makemim`, `copula`, `sigtestp`, `sigtestMTC`, `netplot`, `c3net`

### Examples

```data(expdata)

expdata <- copula(expdata)

mim <- makemim(expdata)

Ic <- mean(mim[upper.tri(mim)])  #Example cut-off for the first step of C3NET

# Ic <- 2 can be set for the example.

mim[mim < Ic] <-0  #nonsignificant values eliminated wrt C3NET step 1.

net <- c3(mim)  # regulatory network inferred (non zero elements stand for links of
#the predicted network)
```

[Package c3net version 1.1.1 Index]