deregulation.scores {joda} | R Documentation |
Deregulation scores quantify the extent to which the regulatory eflect of each regulator changes between the two compared cell populations.
deregulation.scores(reg.scores1, reg.scores2,verbose)
reg.scores1 |
A matrix of regulation scores of the genes (rows) for the
regulators (columns), compued with the |
reg.scores2 |
The same as reg.scores1 but given for the second cell population. |
verbose |
When TRUE, the execution prints informative messages |
The deregulation scores are computed by subtracting reg.scores1 from reg.scores2.
A matrix with columns for the regulators, rows for the genes, and entries giving the deregulation scores.
Ewa Szczurek
http://joda.molgen.mpg.de
differential.probs
, regulation.scores
data(damage) # Step 1 # Get the probabilities of differential expression # for the knockout of ATM in the healthy cells probs.healthy.ATM= differential.probs(data.healthy[,"ATM",FALSE], NULL) # Get the probabilities of differential expression # for the knockout of ATM in the damaged cells probs.damage.ATM= differential.probs(data.damage[,"ATM",FALSE], NULL) # Step 2 # Regulation scores for a dataset with only one regulator # equal the signed probabilities # Step 3 # Get the deregulation scores deregulation.ATM= deregulation.scores(probs.healthy.ATM, probs.damage.ATM, TRUE) ## Not run: # Step 1 probs.healthy= differential.probs(data.healthy, beliefs.healthy) probs.damage= differential.probs(data.damage, beliefs.damage) # Step 2 regulation.healthy= regulation.scores(probs.healthy, model.healthy) regulation.damage= regulation.scores(probs.damage, model.damage) # Step 3 deregulation= deregulation.scores(regulation.healthy, regulation.damage, TRUE) ## End(Not run)