Mutual information network (discrete)
For categorical data (e.g. Allred scores), mutual information is calculated for each pairwise combination of markers using a discrete approach. Statistical significance is determined using 100,000 score permutations and multiple hypothesis correction is performed according to either Benjamini-Yekutieli FDR [Benjamini & Yekutieli 2001 Ann. Statist. 29:1165] (default) or Bonferroni to give a corrected p-value. The result is displayed as a network (a plot of markers connected pairwise by edges/lines) with a default significance threshold of FDR p≤0.05. The threshold value can subsequently be adjusted interactively. Mutual information provides an information-theoretic measure of interdependence between marker scores and can detect both linear and non-linear relationships.
Available for: categorical scoring
[Top]Viewing the results
See accessing and interpreting network results.
[Top]Example output
Figure 1: An example protein network with edges representing significant mutual information relationships. The example is based on continuous data, but the network is representative of the output for categorical scores as well.