Description Usage Arguments Details Value Author(s) References Examples

'clr' infers an adjacency matrix using context likelihood/relatedness network using the 'clr' function from the 'parmigene' package. 'clr' will return the adjacency matrix containing the Context Likelihood of Relatedness Network-adjusted scores of Mutual Information values.

1 | ```
clr(mi)
``` |

`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. |

For more details on the 'clr' function,
refer to '?parmigene::clr'. CLR computes the score
*sqrt(z_i ^2 + z_j ^2)* for each pair of variables i, j, where
*z_i = max(0, ( I(X_i, X_j) - mean(X_i) ) / sd(X_i) )*.
*mean(X_i)* and *sd(X_i)* are the mean and standard deviation of the
mutual information values *I(X_i, X_k)* for all *k = 1, ..., n*.
For more information on the CLR algorithm
see Faith et al. (2007).

matrix, matrix with edges inferred from Context Likelihood of Relatedness Network algorithm 'clr'

Thomas Naake, thomasnaake@googlemail.com

Faith et al. (2007): Large-Scale Mapping and Validation of Escherichia coli Transcriptional Regulation from a Compendium of Expression Profiles. PLoS Biology, e8, doi: [10.1371/journal.pbio.0050008]( https://doi.org/10.1371/journal.pbio.0050008)

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