clr {MetNet} | R Documentation

## Create an adjacency matrix based on context likelihood or relatedness network

### Description

`clr` infers an adjacency matrix using context likelihood/relatedness network using the `clr` function from the `parmigene` package. The presence/absence is based on if the returned value exceeds a user-defined threshold value. `clr` will return the adjacency matrix containing the presence/absence value.

### Usage

    clr(mi, clr_threshold=0)

### Arguments

`mi` | matrix, where columns and the rows are features (metabolites), cell entries are mutual information values between the features. As input, the mutual information (e.g. raw MI estimates or Jackknife bias corrected MI estimates) from the `cmi` function of the `mpmi` package can be used.
`clr_threshold` | numeric, if the clr value exceeds the threshold (clr$_i,j$ > threshold, where clr$_i, j$ is the clr value of the ith row feature and of the jth column feature), the connection is defined as present, if the clr value is lower than the threshold value (clr$_i,j$ ≤q threshold) there is no statistical connection reported.

### Details

For more details on the `clr` function, refer to ?parmigene::clr.

### Value

matrix, matrix with edges inferred from Context Likelihood or Relatedness Network algorithm `clr`

### Author(s)

Thomas Naake, thomasnaake @googlemail.com

### Examples

    data("x_test", package="MetNet")
    x <- x_test[, 3:dim(x_test)[2]]
    x <- as.matrix(x)
    x_z <- t(apply(x, 1, function(y) (y - mean(y)) / sd(y)))
    mi_x_z <- mpmi::cmi(x_z)$bcmi
    clr(mi_x_z, clr_threshold=0)

[Package MetNet version 1.0.1 Index]