Methods and an evaluation framework for the inference of differential co-expression/association networks.
There are three categories of functions available
Differential co-expression methods (DC) - These functions are used to perform a differential co-expression analysis on experimental data with binary conditions.
Functions to evaluate DC methods - These functions are used to evaluate methods implemented in the package and novel methods on simulated data. Expression data is simulated for 2 conditions, wild-type and knock-down of given genes.
By-products of implementations
Accessors of simulated data:
Functions for evaluating inference methods
These are functions used in the package but have further uses in general:
cor.pairs - a faster implementation of pairwise
correlation computation
mi.ap - pairwise computation of mutual information
MI with data discretisation performed using adaptive partitioning
perfMethods - available performance metrics
performanceMeasure - performance measures of prediction
algorithms. Predictions have to be binary