deregulation.p.values: Calculating deregulation p-values using resampling method.
In joda: JODA algorithm for quantifying gene deregulation using knowledge

Description Usage Arguments Details Value Author(s) References See Also Examples

View source: R/deregulation.p.values.R

Deregulation p-values based on deregulation scores. They are calculated as fraction of permutations that give more extreme deregulation scores than for original data.

1	deregulation.p.values(data.1, beliefs.1, model.1, data.2, beliefs.2, model.2, N=100, verbose=FALSE)

`data.1, data.2`	Matrices of log expression ratios perturbation vs control, for the genes (rows), in the perturbations of the regulators (columns). See `differential.probs` for more details.
`beliefs.1, beliefs.2`	Lists of beliefs. See `differential.probs` for more details.
`model.1, model.2`	Pathway topologies. See `differential.probs` for more details.
`N`	A number of replications used to calculate p-values
`verbose`	When TRUE, the execution prints informative messages

The deregulation p-values are calculated as fraction of permutations that give more extreme deregulation scores than for original data.

A list with two matrices. This p-values in the slot deregulation.p.values and with the original deregulation scores in the slot deregulationOrg.

Ewa Szczurek

http://joda.molgen.mpg.de

differential.probs, regulation.scores, regulation.scores

## Not run: 
# Step 1
library(joda)
data(damage)

deregulationObj = deregulation.p.values(data.healthy, beliefs.healthy, model.healthy, data.damage, beliefs.damage, model.damage, N=100, verbose=TRUE) 
boxplot(deregulationObj$deregulation.p.values)

## End(Not run)