ADAM2.truePositiveRate: Profile of True Positive Rates
In DepMap-Analytics/ADAM2: Adaptive Daisy Model

Description Usage Arguments Details Value Author(s) Examples

This function calculates a profile of True Positive Rates for fitness genes in at least n cell lines, with positive cases from a reference set of essential genes.

1 2	ADAM2.truePositiveRate(depMat, essentialGeneSet)

`depMat`	Binary dependency matrix, rows are genes and columns are samples. 1 in position [i,j] indicates that inactivation of the i-th gene exerts a significant loss of fitness in the j-th sample, 0 otherwise.
`essentialGeneSet`	Reference set of predefined essential genes. This is used to define positive cases.

This function calculates true positive rates for fitness genes in at least n cell lines (for each n). First, this function calculates the number of cell lines each gene is a fitness gene. Second, for a given number of cell lines, the set of genes that are fitness genes in at least that number of cell lines is determined. Finally, this set of genes is then compared to the reference set of essential genes to calculate a true positive rate.

A list of the following vectors:

`P`	Vector of number of genes that are depleted for a number of cell lines.
`TP`	Vector of number of genes in sets of P are true positives, i.e. in the essentialGeneSet.
`TPR`	TP divided by number of genes in set essentialGeneSet to give the true positive rate.

C. Pacini, E. Karakoc & F. Iorio

data(exampleDepMat)
pprofile<-ADAM2.panessprofile(depMat=exampleDepMat)
nullmodel<-ADAM2.generateNullModel(depMat=exampleDepMat,ntrials = 1000)
data(curated_BAGEL_essential)
EO<-ADAM2.empiricalOdds(observedCumSum = pprofile$CUMsums,simulatedCumSum =nullmodel$nullCumSUM )
TPR<-ADAM2.truePositiveRate(exampleDepMat,curated_BAGEL_essential)