deregulation.p.values {joda} | R Documentation |
Deregulation p-values based on deregulation scores. They are calculated as fraction of permutations that give more extreme deregulation scores than for original data.
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 |
beliefs.1, beliefs.2 |
Lists of beliefs. See |
model.1, model.2 |
Pathway topologies. See |
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)