deregulation.scores: Calculating deregulation scores.
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.scores.R

Deregulation scores quantify the extent to which the regulatory eflect of each regulator changes between the two compared cell populations.

1	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 `regulation.scores` function. Given for the first cell population.
`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)